From acdc469e39a193882f4559240b202eccb66cf0c8 Mon Sep 17 00:00:00 2001 From: meng-ustc Date: Mon, 1 Feb 2021 21:05:34 +0900 Subject: [PATCH 001/134] Add A New Baseline: DoubleEnsemble --- .../DoubleEnsemble/requirements.txt | 3 + ...rkflow_config_doubleensemble_Alpha158.yaml | 91 +++++++ ...rkflow_config_doubleensemble_Alpha360.yaml | 98 +++++++ qlib/contrib/model/double_ensemble.py | 241 ++++++++++++++++++ 4 files changed, 433 insertions(+) create mode 100644 examples/benchmarks/DoubleEnsemble/requirements.txt create mode 100644 examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml create mode 100644 examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml create mode 100644 qlib/contrib/model/double_ensemble.py diff --git a/examples/benchmarks/DoubleEnsemble/requirements.txt b/examples/benchmarks/DoubleEnsemble/requirements.txt new file mode 100644 index 000000000..f9117361d --- /dev/null +++ b/examples/benchmarks/DoubleEnsemble/requirements.txt @@ -0,0 +1,3 @@ +pandas==1.1.2 +numpy==1.17.4 +lightgbm==3.1.0 \ No newline at end of file diff --git a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml new file mode 100644 index 000000000..b5c669c28 --- /dev/null +++ b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml @@ -0,0 +1,91 @@ +qlib_init: + provider_uri: "~/.qlib/qlib_data/cn_data" + region: cn +market: &market csi300 +benchmark: &benchmark SH000300 +data_handler_config: &data_handler_config + start_time: 2008-01-01 + end_time: 2020-08-01 + fit_start_time: 2008-01-01 + fit_end_time: 2014-12-31 + instruments: *market +port_analysis_config: &port_analysis_config + strategy: + class: TopkDropoutStrategy + module_path: qlib.contrib.strategy.strategy + kwargs: + topk: 50 + n_drop: 5 + backtest: + verbose: False + limit_threshold: 0.095 + account: 100000000 + benchmark: *benchmark + deal_price: close + open_cost: 0.0005 + close_cost: 0.0015 + min_cost: 5 +task: + model: + class: DEnsembleModel + module_path: qlib.contrib.model.double_ensemble + kwargs: + base: "gbm" + loss: mse + k: 6 + enable_sr: True + enable_fs: True + alpha1: 1 + alpha2: 1 + bins_sr: 10 + bins_fs: 5 + decay: 0.5 + sample_ratios: + - 0.8 + - 0.7 + - 0.6 + - 0.5 + - 0.4 + sub_weights: + - 1 + - 0.2 + - 0.2 + - 0.2 + - 0.2 + - 0.2 + colsample_bytree: 0.8879 + learning_rate: 0.2 + subsample: 0.8789 + lambda_l1: 205.6999 + lambda_l2: 580.9768 + max_depth: 8 + num_leaves: 210 + num_threads: 20 + verbosity: -1 + num_iterations: 28 + early_stopping_round: None + dataset: + class: DatasetH + module_path: qlib.data.dataset + kwargs: + handler: + class: Alpha158 + module_path: qlib.contrib.data.handler + kwargs: *data_handler_config + segments: + train: [2008-01-01, 2014-12-31] + valid: [2015-01-01, 2016-12-31] + test: [2017-01-01, 2020-08-01] + record: + - class: SignalRecord + module_path: qlib.workflow.record_temp + kwargs: {} + - class: SigAnaRecord + module_path: qlib.workflow.record_temp + kwargs: + ana_long_short: False + ann_scaler: 252 + - class: PortAnaRecord + module_path: qlib.workflow.record_temp + kwargs: + config: *port_analysis_config \ No newline at end of file diff --git a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml new file mode 100644 index 000000000..b31eac4ce --- /dev/null +++ b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml @@ -0,0 +1,98 @@ +qlib_init: + provider_uri: "~/.qlib/qlib_data/cn_data" + region: cn +market: &market csi300 +benchmark: &benchmark SH000300 +data_handler_config: &data_handler_config + start_time: 2008-01-01 + end_time: 2020-08-01 + fit_start_time: 2008-01-01 + fit_end_time: 2014-12-31 + instruments: *market + infer_processors: [] + learn_processors: + - class: DropnaLabel + - class: CSRankNorm + kwargs: + fields_group: label + label: ["Ref($close, -2) / Ref($close, -1) - 1"] +port_analysis_config: &port_analysis_config + strategy: + class: TopkDropoutStrategy + module_path: qlib.contrib.strategy.strategy + kwargs: + topk: 50 + n_drop: 5 + backtest: + verbose: False + limit_threshold: 0.095 + account: 100000000 + benchmark: *benchmark + deal_price: close + open_cost: 0.0005 + close_cost: 0.0015 + min_cost: 5 +task: + model: + class: DEnsembleModel + module_path: qlib.contrib.model.double_ensemble + kwargs: + base: "gbm" + loss: mse + k: 6 + enable_sr: True + enable_fs: True + alpha1: 1 + alpha2: 1 + bins_sr: 10 + bins_fs: 5 + decay: 0.5 + sample_ratios: + - 0.8 + - 0.7 + - 0.6 + - 0.5 + - 0.4 + sub_weights: + - 1 + - 0.2 + - 0.2 + - 0.2 + - 0.2 + - 0.2 + colsample_bytree: 0.8879 + learning_rate: 0.0421 + subsample: 0.8789 + lambda_l1: 205.6999 + lambda_l2: 580.9768 + max_depth: 8 + num_leaves: 210 + num_threads: 20 + verbosity: -1 + num_iterations: 28 + early_stopping_round: None + dataset: + class: DatasetH + module_path: qlib.data.dataset + kwargs: + handler: + class: Alpha360 + module_path: qlib.contrib.data.handler + kwargs: *data_handler_config + segments: + train: [2008-01-01, 2014-12-31] + valid: [2015-01-01, 2016-12-31] + test: [2017-01-01, 2020-08-01] + record: + - class: SignalRecord + module_path: qlib.workflow.record_temp + kwargs: {} + - class: SigAnaRecord + module_path: qlib.workflow.record_temp + kwargs: + ana_long_short: False + ann_scaler: 252 + - class: PortAnaRecord + module_path: qlib.workflow.record_temp + kwargs: + config: *port_analysis_config \ No newline at end of file diff --git a/qlib/contrib/model/double_ensemble.py b/qlib/contrib/model/double_ensemble.py new file mode 100644 index 000000000..68680b814 --- /dev/null +++ b/qlib/contrib/model/double_ensemble.py @@ -0,0 +1,241 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +import lightgbm as lgb +import numpy as np +import pandas as pd + +from ...model.base import Model +from ...data.dataset import DatasetH +from ...data.dataset.handler import DataHandlerLP +from ...log import get_module_logger + + +class DEnsembleModel(Model): + """Double Ensemble Model""" + + def __init__( + self, + base="gbm", + loss="mse", + k=6, + enable_sr=True, + enable_fs=True, + alpha1=1., + alpha2=1., + bins_sr=10, + bins_fs=5, + decay=None, + sample_ratios=None, + sub_weights=None, + **kwargs): + self.base = base # "gbm" or "mlp", specifically, we use lgbm for "gbm" + self.k = k + self.enable_sr = enable_sr + self.enable_fs = enable_fs + self.alpha1 = alpha1 + self.alpha2 = alpha2 + self.bins_sr = bins_sr + self.bins_fs = bins_fs + self.decay = decay + if not len(sample_ratios) == bins_fs: + raise ValueError("The length of sample_ratios should be equal to bins_fs.") + self.sample_ratios = sample_ratios + if not len(sub_weights) == k: + raise ValueError("The length of sub_weights should be equal to k.") + self.sub_weights = sub_weights + self.logger = get_module_logger("DEnsembleModel") + self.logger.info("Double Ensemble Model...") + self.ensemble = [] # the current ensemble model, a list contains all the sub-models + self.sub_features = [] # the features for each sub model in the form of pandas.Index + self.params = {"objective": loss} + self.params.update(kwargs) + self.loss = loss + + def fit( + self, + dataset: DatasetH + ): + df_train, df_valid = dataset.prepare( + ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L + ) + x_train, y_train = df_train["feature"], df_train["label"] + # initialize the sample weights + N, F = x_train.shape + weights = pd.Series(np.ones(N, dtype=float)) + # initialize the features + features = x_train.columns + pred_sub = pd.DataFrame(np.zeros((N, self.k), dtype=float), index=x_train.index) + # train k sub-models + for i_k in range(self.k): + self.sub_features.append(features) + self.logger.info("Training sub-model: ({}/{})".format(i_k+1, self.k)) + model_k = self.train_submodel(df_train, df_valid, weights, features) + self.ensemble.append(model_k) + # no further sample re-weight and feature selection needed for the last sub-model + if i_k + 1 == self.k: + break + + self.logger.info("Retrieving loss curve and loss values...") + loss_curve = self.retrieve_loss_curve(model_k, df_train, features) + pred_k = self.predict_sub(model_k, df_train, features) + pred_sub.iloc[:, i_k] = pred_k + pred_ensemble = pred_sub.iloc[:, :i_k+1].mean(axis=1) + loss_values = pd.Series(self.get_loss(y_train.values.squeeze(), pred_ensemble.values)) + + if self.enable_sr: + self.logger.info("Sample re-weighting...") + weights = self.sample_reweight(loss_curve, loss_values, i_k+1) + + if self.enable_fs: + self.logger.info("Feature selection...") + features = self.feature_selection(df_train, loss_values) + + def train_submodel(self, df_train, df_valid, weights, features): + dtrain, dvalid = self._prepare_data_gbm(df_train, df_valid, weights, features) + evals_result = dict() + model = lgb.train( + self.params, + dtrain, + valid_sets=[dtrain, dvalid], + valid_names=["train", "valid"], + verbose_eval=20, + evals_result=evals_result, + ) + evals_result["train"] = list(evals_result["train"].values())[0] + evals_result["valid"] = list(evals_result["valid"].values())[0] + return model + + def _prepare_data_gbm(self, df_train, df_valid, weights, features): + x_train, y_train = df_train["feature"].loc[:, features], df_train["label"] + x_valid, y_valid = df_valid["feature"].loc[:, features], df_valid["label"] + + # Lightgbm need 1D array as its label + if y_train.values.ndim == 2 and y_train.values.shape[1] == 1: + y_train, y_valid = np.squeeze(y_train.values), np.squeeze(y_valid.values) + else: + raise ValueError("LightGBM doesn't support multi-label training") + + dtrain = lgb.Dataset(x_train.values, label=y_train, weight=weights) + dvalid = lgb.Dataset(x_valid.values, label=y_valid) + return dtrain, dvalid + + def sample_reweight(self, loss_curve, loss_values, k_th): + """ + the SR module of Double Ensemble + :param loss_curve: the shape is NxT + the loss curve for the previous sub-model, where the element (i, t) if the error on the i-th sample + after the t-th iteration in the training of the previous sub-model. + :param loss_values: the shape is N + the loss of the current ensemble on the i-th sample. + :param k_th: the index of the current sub-model, starting from 1 + :return: weights + the weights for all the samples. + """ + # normalize loss_curve and loss_values with ranking + loss_curve_norm = loss_curve.rank(axis=0, pct=True) + loss_values_norm = (-loss_values).rank(pct=True) + + # calculate l_start and l_end from loss_curve + N, T = loss_curve.shape + part = np.maximum(int(T * 0.1), 1) + l_start = loss_curve_norm.iloc[:, :part].mean(axis=1) + l_end = loss_curve_norm.iloc[:, -part:].mean(axis=1) + + # calculate h-value for each sample + h1 = loss_values_norm + h2 = (l_end / l_start).rank(pct=True) + h = pd.DataFrame({'h_value': self.alpha1 * h1 + self.alpha2 * h2}) + + # calculate weights + h['bins'] = pd.cut(h['h_value'], self.bins_sr) + h_avg = h.groupby('bins')['h_value'].mean() + weights = pd.Series(np.zeros(N, dtype=float)) + for i_b, b in enumerate(h_avg.index): + weights[h['bins'] == b] = 1. / (self.decay ** k_th * h_avg[i_b] + 0.1) + return weights + + def feature_selection(self, df_train, loss_values): + """ + the FS module of Double Ensemble + :param df_train: the shape is NxF + :param loss_values: the shape is N + the loss of the current ensemble on the i-th sample. + :return: res_feat: in the form of pandas.Index + + """ + x_train, y_train = df_train["feature"], df_train["label"] + features = x_train.columns + N, F = x_train.shape + g = pd.DataFrame({'g_value': np.zeros(F, dtype=float)}) + M = len(self.ensemble) + + # shuffle specific columns and calculate g-value for each feature + x_train_tmp = x_train.copy() + for i_f, feat in enumerate(features): + x_train_tmp.loc[:, feat] = np.random.permutation(x_train_tmp.loc[:, feat].values) + pred = pd.Series(np.zeros(N), index=x_train_tmp.index) + for i_s, submodel in enumerate(self.ensemble): + pred += pd.Series(submodel.predict(x_train_tmp.loc[:, self.sub_features[i_s]].values), + index=x_train_tmp.index) / M + loss_feat = self.get_loss(y_train.values.squeeze(), pred.values) + g.loc[i_f, 'g_value'] = np.mean(loss_feat - loss_values) / np.std(loss_feat - loss_values) + x_train_tmp.loc[:, feat] = x_train.loc[:, feat].copy() + + # one column in train features is all-nan # if g['g_value'].isna().any() + g['g_value'].replace(np.nan, 0, inplace=True) + + # divide features into bins_fs bins + g['bins'] = pd.cut(g['g_value'], self.bins_fs) + + # randomly sample features from bins to construct the new features + res_feat = [] + sorted_bins = sorted(g['bins'].unique(), reverse=True) + for i_b, b in enumerate(sorted_bins): + b_feat = features[g['bins'] == b] + num_feat = int(np.ceil(self.sample_ratios[i_b] * len(b_feat))) + res_feat = res_feat + np.random.choice(b_feat, size=num_feat).tolist() + return pd.Index(res_feat) + + def get_loss(self, label, pred): + if self.loss == "mse": + return (label - pred) ** 2 + else: + raise ValueError("not implemented yet") + + def retrieve_loss_curve(self, model, df_train, features): + if self.base == "gbm": + num_trees = model.num_trees() + x_train, y_train = df_train["feature"].loc[:, features], df_train["label"] + # Lightgbm need 1D array as its label + if y_train.values.ndim == 2 and y_train.values.shape[1] == 1: + y_train = np.squeeze(y_train.values) + else: + raise ValueError("LightGBM doesn't support multi-label training") + + N = x_train.shape[0] + loss_curve = pd.DataFrame(np.zeros((N, num_trees))) + pred_tree = np.zeros(N, dtype=float) + for i_tree in range(num_trees): + pred_tree += model.predict(x_train.values, start_iteration=i_tree, num_iteration=1) + loss_curve.iloc[:, i_tree] = self.get_loss(y_train, pred_tree) + else: + raise ValueError("not implemented yet") + return loss_curve + + def predict(self, dataset): + if self.ensemble is None: + raise ValueError("model is not fitted yet!") + x_test = dataset.prepare("test", col_set="feature", data_key=DataHandlerLP.DK_I) + pred = pd.Series(np.zeros(x_test.shape[0]), index=x_test.index) + for i_sub, submodel in enumerate(self.ensemble): + feat_sub = self.sub_features[i_sub] + pred += pd.Series(submodel.predict(x_test.loc[:, feat_sub].values), index=x_test.index) * self.sub_weights[i_sub] + return pred + + def predict_sub(self, submodel, df_data, features): + x_data, y_data = df_data["feature"].loc[:, features], df_data["label"] + pred_sub = pd.Series(submodel.predict(x_data.values), index=x_data.index) + return pred_sub + + From 8c3ec164ff5bb73fea2cc0d63f91776148ed2760 Mon Sep 17 00:00:00 2001 From: meng-ustc Date: Tue, 2 Feb 2021 11:46:37 +0900 Subject: [PATCH 002/134] Add A New Baseline: DoubleEnsemble --- ...rkflow_config_doubleensemble_Alpha158.yaml | 5 +- ...rkflow_config_doubleensemble_Alpha360.yaml | 47 +++++++++---------- examples/run_all_model.py | 2 +- qlib/contrib/model/double_ensemble.py | 3 ++ 4 files changed, 29 insertions(+), 28 deletions(-) diff --git a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml index b5c669c28..c468eb320 100644 --- a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml +++ b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml @@ -33,7 +33,7 @@ task: base: "gbm" loss: mse k: 6 - enable_sr: True + enable_sr: False enable_fs: True alpha1: 1 alpha2: 1 @@ -53,6 +53,7 @@ task: - 0.2 - 0.2 - 0.2 + epochs: 28 colsample_bytree: 0.8879 learning_rate: 0.2 subsample: 0.8789 @@ -62,8 +63,6 @@ task: num_leaves: 210 num_threads: 20 verbosity: -1 - num_iterations: 28 - early_stopping_round: None dataset: class: DatasetH module_path: qlib.data.dataset diff --git a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml index b31eac4ce..3351cefc5 100644 --- a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml +++ b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml @@ -38,28 +38,29 @@ task: module_path: qlib.contrib.model.double_ensemble kwargs: base: "gbm" - loss: mse - k: 6 - enable_sr: True - enable_fs: True - alpha1: 1 - alpha2: 1 - bins_sr: 10 - bins_fs: 5 - decay: 0.5 - sample_ratios: - - 0.8 - - 0.7 - - 0.6 - - 0.5 - - 0.4 - sub_weights: - - 1 - - 0.2 - - 0.2 - - 0.2 - - 0.2 - - 0.2 + loss: mse + k: 6 + enable_sr: True + enable_fs: True + alpha1: 1 + alpha2: 1 + bins_sr: 10 + bins_fs: 5 + decay: 0.5 + sample_ratios: + - 0.8 + - 0.7 + - 0.6 + - 0.5 + - 0.4 + sub_weights: + - 1 + - 0.2 + - 0.2 + - 0.2 + - 0.2 + - 0.2 + epochs: 136 colsample_bytree: 0.8879 learning_rate: 0.0421 subsample: 0.8789 @@ -69,8 +70,6 @@ task: num_leaves: 210 num_threads: 20 verbosity: -1 - num_iterations: 28 - early_stopping_round: None dataset: class: DatasetH module_path: qlib.data.dataset diff --git a/examples/run_all_model.py b/examples/run_all_model.py index d587eff15..1809bac2e 100644 --- a/examples/run_all_model.py +++ b/examples/run_all_model.py @@ -265,7 +265,7 @@ def run(times=1, models=None, dataset="Alpha360", exclude=False): ) # TODO: FIX ME! else: execute( - f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall -e git+https://github.com/microsoft/qlib#egg=pyqlib" + f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall -e git+https://github.com/meng-ustc/qlib#egg=pyqlib" ) # TODO: FIX ME! sys.stderr.write("\n") # run workflow_by_config for multiple times diff --git a/qlib/contrib/model/double_ensemble.py b/qlib/contrib/model/double_ensemble.py index 68680b814..786b3987c 100644 --- a/qlib/contrib/model/double_ensemble.py +++ b/qlib/contrib/model/double_ensemble.py @@ -28,6 +28,7 @@ class DEnsembleModel(Model): decay=None, sample_ratios=None, sub_weights=None, + epochs=100, **kwargs): self.base = base # "gbm" or "mlp", specifically, we use lgbm for "gbm" self.k = k @@ -44,6 +45,7 @@ class DEnsembleModel(Model): if not len(sub_weights) == k: raise ValueError("The length of sub_weights should be equal to k.") self.sub_weights = sub_weights + self.epochs = epochs self.logger = get_module_logger("DEnsembleModel") self.logger.info("Double Ensemble Model...") self.ensemble = [] # the current ensemble model, a list contains all the sub-models @@ -97,6 +99,7 @@ class DEnsembleModel(Model): model = lgb.train( self.params, dtrain, + num_boost_round=self.epochs, valid_sets=[dtrain, dvalid], valid_names=["train", "valid"], verbose_eval=20, From fd5c68a7d1b9e9074464431ab384dfbdda7aa206 Mon Sep 17 00:00:00 2001 From: Meng Dong Date: Tue, 2 Feb 2021 12:39:07 +0800 Subject: [PATCH 003/134] Update workflow_config_doubleensemble_Alpha158.yaml --- .../workflow_config_doubleensemble_Alpha158.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml index c468eb320..74923c0e5 100644 --- a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml +++ b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml @@ -33,7 +33,7 @@ task: base: "gbm" loss: mse k: 6 - enable_sr: False + enable_sr: True enable_fs: True alpha1: 1 alpha2: 1 @@ -87,4 +87,4 @@ task: - class: PortAnaRecord module_path: qlib.workflow.record_temp kwargs: - config: *port_analysis_config \ No newline at end of file + config: *port_analysis_config From 988b42e159111f52dbba3301e7b1ae1187d2dc12 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Tue, 9 Feb 2021 20:28:42 +0800 Subject: [PATCH 004/134] Add Structured Covariance Estimator to riskmodel.py --- qlib/model/riskmodel.py | 141 +++++++++++++++++++++++++++++++++++++++- 1 file changed, 138 insertions(+), 3 deletions(-) diff --git a/qlib/model/riskmodel.py b/qlib/model/riskmodel.py index 07a1e0c9f..32984ed6a 100644 --- a/qlib/model/riskmodel.py +++ b/qlib/model/riskmodel.py @@ -39,7 +39,7 @@ class RiskModel(BaseModel): self.scale_return = scale_return def predict( - self, X: Union[pd.Series, pd.DataFrame, np.ndarray], return_corr: bool = False, is_price: bool = True + self, X: Union[pd.Series, pd.DataFrame, np.ndarray], return_corr: bool = False, is_price: bool = True ) -> Union[pd.DataFrame, np.ndarray]: """ Args: @@ -373,7 +373,8 @@ class ShrinkCovEstimator(RiskModel): roff1 = np.sum(v1 * cov_mkt[:, None].T) / var_mkt - np.sum(np.diag(v1) * cov_mkt) / var_mkt v3 = z.T.dot(z) / t - var_mkt * S roff3 = ( - np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt ** 2 - np.sum(np.diag(v3) * cov_mkt ** 2) / var_mkt ** 2 + np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt ** 2 - np.sum( + np.diag(v3) * cov_mkt ** 2) / var_mkt ** 2 ) roff = 2 * roff1 - roff3 rho = rdiag + roff @@ -433,7 +434,7 @@ class POETCovEstimator(RiskModel): if self.num_factors > 0: Dd, V = np.linalg.eig(Y.T.dot(Y)) V = V[:, np.argsort(Dd)] - F = V[:, -self.num_factors :][:, ::-1] * np.sqrt(n) + F = V[:, -self.num_factors:][:, ::-1] * np.sqrt(n) LamPCA = Y.dot(F) / n uhat = np.asarray(Y - LamPCA.dot(F.T)) Lowrank = np.asarray(LamPCA.dot(LamPCA.T)) @@ -465,3 +466,137 @@ class POETCovEstimator(RiskModel): SigmaY = SigmaU + Lowrank return SigmaY + + +class StructuredCovEstimator(RiskModel): + """Structured Covariance Estimator + + This estimator assumes observations can be predicted by multiple factors + X = FB + U + where `F` can be specified by explicit risk factors or latent factors. + + Therefore the structured covariance can be estimated by + cov(X) = F cov(B) F.T + cov(U) + + We use latent factor models to estimate the structured covariance. + Specifically, the following latent factor models are supported: + - `pca`: Principal Component Analysis + - `fa`: Factor Analysis + + Reference: [1] Fan, J., Liao, Y., & Liu, H. (2016). An overview of the estimation of large covariance and + precision matrices. Econometrics Journal, 19(1), C1–C32. https://doi.org/10.1111/ectj.12061 + """ + + FACTOR_MODEL_PCA = "pca" + FACTOR_MODEL_FA = "fa" + + def __init__(self, factor_model: str = 'pca', num_factors: int = 10, nan_option: str = "ignore", + assume_centered: bool = False, scale_return: bool = True): + """ + Args: + factor_model (str): the latent factor models used to estimate the structured covariance (`pca`/`fa`). + num_factors (int): number of components to keep. + nan_option (str): nan handling option (`ignore`/`fill`). + assume_centered (bool): whether the data is assumed to be centered. + scale_return (bool): whether scale returns as percentage. + """ + super().__init__(nan_option, assume_centered, scale_return) + + assert factor_model in [ + self.FACTOR_MODEL_PCA, + self.FACTOR_MODEL_FA, + ], 'factor_model={} is not supported'.format(factor_model) + self.solver = PCA if factor_model == self.FACTOR_MODEL_PCA else FactorAnalysis + + self.num_factors = num_factors + + def predict( + self, X: Union[pd.Series, pd.DataFrame, np.ndarray], return_corr: bool = False, is_price: bool = True, + return_decomposed_components=False + ) -> Union[pd.DataFrame, np.ndarray, tuple]: + """ + Args: + X (pd.Series, pd.DataFrame or np.ndarray): data from which to estimate the covariance, + with variables as columns and observations as rows. + return_corr (bool): whether return the correlation matrix. + is_price (bool): whether `X` contains price (if not assume stock returns). + return_decomposed_components (bool): whether return decomposed components of the covariance matrix. + + Returns: + tuple or pd.DataFrame or np.ndarray: decomposed covariance matrix or estimated covariance or correlation. + """ + assert not return_corr or not return_decomposed_components, \ + 'Can only return either correlation matrix or decomposed components.' + + # transform input into 2D array + if not isinstance(X, (pd.Series, pd.DataFrame)): + columns = None + else: + if isinstance(X.index, pd.MultiIndex): + if isinstance(X, pd.DataFrame): + X = X.iloc[:, 0].unstack(level="instrument") # always use the first column + else: + X = X.unstack(level="instrument") + else: + # X is 2D DataFrame + pass + columns = X.columns # will be used to restore dataframe + X = X.values + + # calculate pct_change + if is_price: + X = X[1:] / X[:-1] - 1 # NOTE: resulting `n - 1` rows + + # scale return + if self.scale_return: + X *= 100 + + # handle nan and centered + X = self._preprocess(X) + + if return_decomposed_components: + F, cov_b, var_u = self._predict(X, return_structured=True) + return F, cov_b, var_u + else: + # estimate covariance + S = self._predict(X) + + # return correlation if needed + if return_corr: + vola = np.sqrt(np.diag(S)) + corr = S / np.outer(vola, vola) + if columns is None: + return corr + return pd.DataFrame(corr, index=columns, columns=columns) + + # return covariance + if columns is None: + return S + return pd.DataFrame(S, index=columns, columns=columns) + + def _predict(self, X: np.ndarray, return_structured=False) -> Union[np.ndarray, tuple]: + """ + covariance estimation implementation + + Args: + X (np.ndarray): data matrix containing multiple variables (columns) and observations (rows). + return_structured (bool): whether return decomposed components of the covariance matrix. + + Returns: + tuple or np.ndarray: decomposed covariance matrix or covariance matrix. + """ + + model = self.solver(self.num_factors, random_state=0).fit(X) + + F = model.components_.T # num_features x num_factors + B = model.transform(X) # num_samples x num_factors + U = X - B @ F.T + cov_b = np.cov(B.T) # num_factors x num_factors + var_u = np.var(U, axis=0) # diagonal + + if return_structured: + return F, cov_b, var_u + + cov_x = F @ cov_b @ F.T + np.diag(var_u) + + return cov_x From 7b01c5cae7830d2b75c5566443f5a4559b5b2f40 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Tue, 9 Feb 2021 20:30:26 +0800 Subject: [PATCH 005/134] Add an implementation of Enhanced Indexing to optimizer.py --- qlib/.DS_Store | Bin 0 -> 6148 bytes qlib/portfolio/optimizer.py | 129 ++++++++++++++++++++++++++++++++---- 2 files changed, 117 insertions(+), 12 deletions(-) create mode 100644 qlib/.DS_Store diff --git a/qlib/.DS_Store b/qlib/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..3b196d96a164ebf17b658d6f6d8c5ef19fb26c8c GIT binary patch literal 6148 zcmeHK%Wl&^6upx=txW}FQHgGlykS>WX{it_kU}Vn?vR3D0VuU=(^|NmD0Z;g5R^S% z0N;SbckloAHxWIzRhp0nDE)t`24cQXodQJ=0 zG9@>FLbZ`lK|@^IG+JQW0;_;k;9paK=kB_ivk7HXoxf)XsXPi(8G^yf!y}~apUU77 zd2~b)F>erctY&;O;%g&f%|sM5Mod_5U)*l69f~NxdJ;5v9~--ZHYJy`PvXJrW|?Rkd0?VRkk)wz)B!- zgQ;dimFZHcy5udogvWbXZ&~oHsPb`pshPpyu3yTu1gn5m;D1wq*9RYov1f3uQ7s+F z)DZyapja7v{#oD{-(b(+Tq9~=LWcr%s4zzip~F$`8(z=gT%!&rVGbX{JXx3%icn8S z`@W)+=xMaMRlq7xS71foc6k4Pa`E}U9%S#V0#<=5rGRku2m5_|lG$4qK92WVAL$T@ qjd^p8Dg>F?j+Mh(@g|Zo)cNcHdj{tkQ3A6+0!jv(Sq1*80>1zhm={X` literal 0 HcmV?d00001 diff --git a/qlib/portfolio/optimizer.py b/qlib/portfolio/optimizer.py index 0e7d27254..e04923ed6 100644 --- a/qlib/portfolio/optimizer.py +++ b/qlib/portfolio/optimizer.py @@ -28,13 +28,13 @@ class PortfolioOptimizer: OPT_INV = "inv" def __init__( - self, - method: str = "inv", - lamb: float = 0, - delta: float = 0, - alpha: float = 0.0, - scale_alpha: bool = True, - tol: float = 1e-8, + self, + method: str = "inv", + lamb: float = 0, + delta: float = 0, + alpha: float = 0.0, + scale_alpha: bool = True, + tol: float = 1e-8, ): """ Args: @@ -59,10 +59,10 @@ class PortfolioOptimizer: self.tol = tol def __call__( - self, - S: Union[np.ndarray, pd.DataFrame], - u: Optional[Union[np.ndarray, pd.Series]] = None, - w0: Optional[Union[np.ndarray, pd.Series]] = None, + self, + S: Union[np.ndarray, pd.DataFrame], + u: Optional[Union[np.ndarray, pd.Series]] = None, + w0: Optional[Union[np.ndarray, pd.Series]] = None, ) -> Union[np.ndarray, pd.Series]: """ Args: @@ -151,7 +151,7 @@ class PortfolioOptimizer: return self._solve(len(S), self._get_objective_gmv(S), *self._get_constrains(w0)) def _optimize_mvo( - self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None + self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None ) -> np.ndarray: """optimize mean-variance portfolio @@ -256,3 +256,108 @@ class PortfolioOptimizer: warnings.warn(f"optimization not success ({sol.status})") return sol.x + + +class EnhancedIndexingOptimizer: + """ + Portfolio Optimizer with Enhanced Indexing + + Note: + This optimizer always assumes full investment and no-shorting. + """ + + START_FROM_W0 = 'w0' + START_FROM_BENCH = 'benchmark' + DO_NOT_START_FROM = '' + + def __init__(self, lamb: float = 10, delta: float = 0.4, bench_dev: float = 0.01, inds_dev: float = 0.01, + scale_alpha=True, verbose: bool = False, warm_start: str = '', max_iters: int = 10000): + """ + Args: + lamb (float): risk aversion parameter (larger `lamb` means less focus on return) + delta (float): turnover rate limit + bench_dev (float): benchmark deviation limit + inds_dev (float): industry deviation limit + verbose (bool): if print detailed information about the solver + warm_start (str): whether try to warm start (`w0`/`benchmark`/``) + (https://www.cvxpy.org/tutorial/advanced/index.html#warm-start) + """ + + assert lamb >= 0, "risk aversion parameter `lamb` should be positive" + self.lamb = lamb + + assert delta >= 0, "turnover limit `delta` should be positive" + self.delta = delta + + assert bench_dev >= 0, "benchmark deviation limit `bench_dev` should be positive" + self.bench_dev = bench_dev + + assert inds_dev >= 0, "industry deviation limit `inds_dev` should be positive" + self.inds_dev = inds_dev + + assert warm_start in [self.DO_NOT_START_FROM, self.START_FROM_W0, + self.START_FROM_BENCH], "illegal warm start option" + self.start_from_w0 = (warm_start == self.START_FROM_W0) + self.start_from_bench = (warm_start == self.START_FROM_BENCH) + + self.scale_alpha = scale_alpha + self.verbose = verbose + self.max_iters = max_iters + + def __call__(self, u: np.ndarray, F: np.ndarray, covB: np.ndarray, varU: np.ndarray, w0: np.ndarray, + w_bench: np.ndarray, inds_onehot: np.ndarray + ) -> Union[np.ndarray, pd.Series]: + """ + Args: + u (np.ndarray): expected returns (a.k.a., alpha) + F, covB, varU (np.ndarray): see StructuredCovEstimator + w0 (np.ndarray): initial weights (for turnover control) + w_bench (np.ndarray): benchmark weights + inds_onehot (np.ndarray): industry (onehot) + + Returns: + np.ndarray or pd.Series: optimized portfolio allocation + """ + # scale alpha to match volatility + if self.scale_alpha: + u = u / u.std() + x_variance = np.mean(np.diag(F @ covB @ F.T) + varU) + u *= x_variance ** 0.5 + + w = cp.Variable(len(u)) # num_assets + v = w @ F # num_factors + ret = w @ u + risk = cp.quad_form(v, covB) + cp.sum(cp.multiply(varU, w ** 2)) + obj = cp.Maximize(ret - self.lamb * risk) + d_bench = w - w_bench + d_inds = d_bench @ inds_onehot + cons = [ + w >= 0, + cp.sum(w) == 1, + d_bench >= -self.bench_dev, + d_bench <= self.bench_dev, + d_inds >= -self.inds_dev, + d_inds <= self.inds_dev + ] + if w0 is not None: + turnover = cp.sum(cp.abs(w - w0)) + cons.append(turnover <= self.delta) + + warm_start = False + if self.start_from_w0: + if w0 is None: + print('Warning: try warm start with w0, but w0 is `None`.') + else: + w.value = w0 + warm_start = True + elif self.start_from_bench: + w.value = w_bench + warm_start = True + + prob = cp.Problem(obj, cons) + prob.solve(solver=cp.SCS, verbose=self.verbose, warm_start=warm_start, max_iters=self.max_iters) + + if prob.status != 'optimal': + print('Warning: solve failed.', prob.status) + + return np.asarray(w.value) From 9c2653f125e31e754f00ef6df0c455f8e828d78a Mon Sep 17 00:00:00 2001 From: Charles Young Date: Tue, 9 Feb 2021 20:31:00 +0800 Subject: [PATCH 006/134] Add an implementation of Enhanced Indexing to optimizer.py --- qlib/.DS_Store | Bin 6148 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 qlib/.DS_Store diff --git a/qlib/.DS_Store b/qlib/.DS_Store deleted file mode 100644 index 3b196d96a164ebf17b658d6f6d8c5ef19fb26c8c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHK%Wl&^6upx=txW}FQHgGlykS>WX{it_kU}Vn?vR3D0VuU=(^|NmD0Z;g5R^S% z0N;SbckloAHxWIzRhp0nDE)t`24cQXodQJ=0 zG9@>FLbZ`lK|@^IG+JQW0;_;k;9paK=kB_ivk7HXoxf)XsXPi(8G^yf!y}~apUU77 zd2~b)F>erctY&;O;%g&f%|sM5Mod_5U)*l69f~NxdJ;5v9~--ZHYJy`PvXJrW|?Rkd0?VRkk)wz)B!- zgQ;dimFZHcy5udogvWbXZ&~oHsPb`pshPpyu3yTu1gn5m;D1wq*9RYov1f3uQ7s+F z)DZyapja7v{#oD{-(b(+Tq9~=LWcr%s4zzip~F$`8(z=gT%!&rVGbX{JXx3%icn8S z`@W)+=xMaMRlq7xS71foc6k4Pa`E}U9%S#V0#<=5rGRku2m5_|lG$4qK92WVAL$T@ qjd^p8Dg>F?j+Mh(@g|Zo)cNcHdj{tkQ3A6+0!jv(Sq1*80>1zhm={X` From 63d05e4a1a5cde0d47071171de736df55673039e Mon Sep 17 00:00:00 2001 From: Kenneth Tang Date: Tue, 16 Feb 2021 14:08:58 +0800 Subject: [PATCH 007/134] Fix typo --- scripts/data_collector/us_index/README.md | 4 ++-- scripts/data_collector/yahoo/README.md | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/scripts/data_collector/us_index/README.md b/scripts/data_collector/us_index/README.md index 99a0a09c3..f8244491c 100644 --- a/scripts/data_collector/us_index/README.md +++ b/scripts/data_collector/us_index/README.md @@ -10,10 +10,10 @@ pip install -r requirements.txt ```bash # parse instruments, using in qlib/instruments. -python collector.py --index_name SP500 --qlib_dir ~/.qlib/qlib_data/cn_data --method parse_instruments +python collector.py --index_name SP500 --qlib_dir ~/.qlib/qlib_data/us_data --method parse_instruments # parse new companies -python collector.py --index_name SP500 --qlib_dir ~/.qlib/qlib_data/cn_data --method save_new_companies +python collector.py --index_name SP500 --qlib_dir ~/.qlib/qlib_data/us_data --method save_new_companies # index_name support: SP500, NASDAQ100, DJIA, SP400 # help diff --git a/scripts/data_collector/yahoo/README.md b/scripts/data_collector/yahoo/README.md index d4c2d2c9c..520369ef5 100644 --- a/scripts/data_collector/yahoo/README.md +++ b/scripts/data_collector/yahoo/README.md @@ -39,7 +39,7 @@ python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/cn_1d_nor --qli import qlib from qlib.data import D -qlib.init(provider_uri="~/.qlib/stock_data/source/qlib_cn_1d", region="CN") +qlib.init(provider_uri="~/.qlib/stock_data/source/qlib_cn_1d", region="cn") df = D.features(D.instruments("all"), ["$close"], freq="day") ``` @@ -81,13 +81,13 @@ python collector.py normalize_data --source_dir ~/.qlib/stock_data/source/us_1d # dump data cd qlib/scripts -python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/cn_1d_nor --qlib_dir ~/.qlib/stock_data/source/qlib_us_1d --freq day --exclude_fields date,adjclose,dividends,splits,symbol +python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/us_1d_nor --qlib_dir ~/.qlib/stock_data/source/qlib_us_1d --freq day --exclude_fields date,adjclose,dividends,splits,symbol # using import qlib from qlib.data import D -qlib.init(provider_uri="~/.qlib/stock_data/source/qlib_us_1d", region="US") +qlib.init(provider_uri="~/.qlib/stock_data/source/qlib_us_1d", region="us") df = D.features(D.instruments("all"), ["$close"], freq="day") ``` From d27dc8bab8955bc8900c1a0a0d9d564cd4c9400b Mon Sep 17 00:00:00 2001 From: meng-ustc Date: Thu, 18 Feb 2021 19:02:33 +0900 Subject: [PATCH 008/134] Add A New Baseline: DoubleEnsemble --- examples/run_all_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/run_all_model.py b/examples/run_all_model.py index 1809bac2e..d587eff15 100644 --- a/examples/run_all_model.py +++ b/examples/run_all_model.py @@ -265,7 +265,7 @@ def run(times=1, models=None, dataset="Alpha360", exclude=False): ) # TODO: FIX ME! else: execute( - f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall -e git+https://github.com/meng-ustc/qlib#egg=pyqlib" + f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall -e git+https://github.com/microsoft/qlib#egg=pyqlib" ) # TODO: FIX ME! sys.stderr.write("\n") # run workflow_by_config for multiple times From 42590972e49457d659749a65e8c8ad02172c8fe5 Mon Sep 17 00:00:00 2001 From: meng-ustc Date: Thu, 18 Feb 2021 19:15:02 +0900 Subject: [PATCH 009/134] Modify run_all_model.py --- examples/run_all_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/run_all_model.py b/examples/run_all_model.py index d587eff15..f9d4a62a0 100644 --- a/examples/run_all_model.py +++ b/examples/run_all_model.py @@ -265,7 +265,7 @@ def run(times=1, models=None, dataset="Alpha360", exclude=False): ) # TODO: FIX ME! else: execute( - f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall -e git+https://github.com/microsoft/qlib#egg=pyqlib" + f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall -e git+https://github.com/microsoft----/qlib#egg=pyqlib" ) # TODO: FIX ME! sys.stderr.write("\n") # run workflow_by_config for multiple times From cd5b721bc6e91de0d0c83978ec14475a52f94a2e Mon Sep 17 00:00:00 2001 From: meng-ustc Date: Fri, 19 Feb 2021 11:56:50 +0900 Subject: [PATCH 010/134] Update --- examples/run_all_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/run_all_model.py b/examples/run_all_model.py index f9d4a62a0..d587eff15 100644 --- a/examples/run_all_model.py +++ b/examples/run_all_model.py @@ -265,7 +265,7 @@ def run(times=1, models=None, dataset="Alpha360", exclude=False): ) # TODO: FIX ME! else: execute( - f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall -e git+https://github.com/microsoft----/qlib#egg=pyqlib" + f"cd {env_path} && {python_path} -m pip install --upgrade --force-reinstall -e git+https://github.com/microsoft/qlib#egg=pyqlib" ) # TODO: FIX ME! sys.stderr.write("\n") # run workflow_by_config for multiple times From 4000518698f0d5f929a2a493bdb9bd207a313d17 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 22 Feb 2021 08:41:35 +0800 Subject: [PATCH 011/134] Separate specific implementation of Portfolio Optimizer to folder. --- .../portfolio_optimizer/enhanced_indexing.py | 112 ++++++++++++++++++ 1 file changed, 112 insertions(+) create mode 100644 qlib/contrib/portfolio_optimizer/enhanced_indexing.py diff --git a/qlib/contrib/portfolio_optimizer/enhanced_indexing.py b/qlib/contrib/portfolio_optimizer/enhanced_indexing.py new file mode 100644 index 000000000..0c40a617e --- /dev/null +++ b/qlib/contrib/portfolio_optimizer/enhanced_indexing.py @@ -0,0 +1,112 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +import numpy as np +import pandas as pd +import cvxpy as cp +from typing import Union + + +class EnhancedIndexingOptimizer: + """ + Portfolio Optimizer with Enhanced Indexing + + Note: + This optimizer always assumes full investment and no-shorting. + """ + + START_FROM_W0 = 'w0' + START_FROM_BENCH = 'benchmark' + DO_NOT_START_FROM = 'no_warm_start' + + def __init__(self, lamb: float = 10, delta: float = 0.4, bench_dev: float = 0.01, inds_dev: float = 0.01, + scale_alpha=True, verbose: bool = False, warm_start: str = DO_NOT_START_FROM, max_iters: int = 10000): + """ + Args: + lamb (float): risk aversion parameter (larger `lamb` means less focus on return) + delta (float): turnover rate limit + bench_dev (float): benchmark deviation limit + inds_dev (float): industry deviation limit + verbose (bool): if print detailed information about the solver + warm_start (str): whether try to warm start (`w0`/`benchmark`/``) + (https://www.cvxpy.org/tutorial/advanced/index.html#warm-start) + """ + + assert lamb >= 0, "risk aversion parameter `lamb` should be positive" + self.lamb = lamb + + assert delta >= 0, "turnover limit `delta` should be positive" + self.delta = delta + + assert bench_dev >= 0, "benchmark deviation limit `bench_dev` should be positive" + self.bench_dev = bench_dev + + assert inds_dev >= 0, "industry deviation limit `inds_dev` should be positive" + self.inds_dev = inds_dev + + assert warm_start in [self.DO_NOT_START_FROM, self.START_FROM_W0, + self.START_FROM_BENCH], "illegal warm start option" + self.start_from_w0 = (warm_start == self.START_FROM_W0) + self.start_from_bench = (warm_start == self.START_FROM_BENCH) + + self.scale_alpha = scale_alpha + self.verbose = verbose + self.max_iters = max_iters + + def __call__(self, u: np.ndarray, F: np.ndarray, covB: np.ndarray, varU: np.ndarray, w0: np.ndarray, + w_bench: np.ndarray, inds_onehot: np.ndarray + ) -> Union[np.ndarray, pd.Series]: + """ + Args: + u (np.ndarray): expected returns (a.k.a., alpha) + F, covB, varU (np.ndarray): see StructuredCovEstimator + w0 (np.ndarray): initial weights (for turnover control) + w_bench (np.ndarray): benchmark weights + inds_onehot (np.ndarray): industry (onehot) + + Returns: + np.ndarray or pd.Series: optimized portfolio allocation + """ + # scale alpha to match volatility + if self.scale_alpha: + u = u / u.std() + x_variance = np.mean(np.diag(F @ covB @ F.T) + varU) + u *= x_variance ** 0.5 + + w = cp.Variable(len(u)) # num_assets + v = w @ F # num_factors + ret = w @ u + risk = cp.quad_form(v, covB) + cp.sum(cp.multiply(varU, w ** 2)) + obj = cp.Maximize(ret - self.lamb * risk) + d_bench = w - w_bench + d_inds = d_bench @ inds_onehot + cons = [ + w >= 0, + cp.sum(w) == 1, + d_bench >= -self.bench_dev, + d_bench <= self.bench_dev, + d_inds >= -self.inds_dev, + d_inds <= self.inds_dev + ] + if w0 is not None: + turnover = cp.sum(cp.abs(w - w0)) + cons.append(turnover <= self.delta) + + warm_start = False + if self.start_from_w0: + if w0 is None: + print('Warning: try warm start with w0, but w0 is `None`.') + else: + w.value = w0 + warm_start = True + elif self.start_from_bench: + w.value = w_bench + warm_start = True + + prob = cp.Problem(obj, cons) + prob.solve(solver=cp.SCS, verbose=self.verbose, warm_start=warm_start, max_iters=self.max_iters) + + if prob.status != 'optimal': + print('Warning: solve failed.', prob.status) + + return np.asarray(w.value) From b2e2142594d38c0afa4e31d560a17cfad05e2705 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 22 Feb 2021 09:00:12 +0800 Subject: [PATCH 012/134] Applied slight modification to follow PEP 8. --- qlib/portfolio/optimizer.py | 114 +++--------------------------------- 1 file changed, 7 insertions(+), 107 deletions(-) diff --git a/qlib/portfolio/optimizer.py b/qlib/portfolio/optimizer.py index e04923ed6..104e2c441 100644 --- a/qlib/portfolio/optimizer.py +++ b/qlib/portfolio/optimizer.py @@ -42,6 +42,7 @@ class PortfolioOptimizer: lamb (float): risk aversion parameter (larger `lamb` means more focus on return) delta (float): turnover rate limit alpha (float): l2 norm regularizer + scale_alpha (bool): if to scale alpha to match the volatility of the covariance matrix tol (float): tolerance for optimization termination """ assert method in [self.OPT_GMV, self.OPT_MVO, self.OPT_RP, self.OPT_INV], f"method `{method}` is not supported" @@ -57,6 +58,7 @@ class PortfolioOptimizer: self.alpha = alpha self.tol = tol + self.scale_alpha = scale_alpha def __call__( self, @@ -94,7 +96,7 @@ class PortfolioOptimizer: w0 = w0.values # scale alpha to match volatility - if u is not None: + if u is not None and self.scale_alpha: u = u / u.std() u *= np.mean(np.diag(S)) ** 0.5 @@ -247,7 +249,10 @@ class PortfolioOptimizer: # add l2 regularization wrapped_obj = obj if self.alpha > 0: - wrapped_obj = lambda x: obj(x) + self.alpha * np.sum(np.square(x)) + def opt_obj(x): + return obj(x) + self.alpha * np.sum(np.square(x)) + + wrapped_obj = opt_obj # solve x0 = np.ones(n) / n # init results @@ -256,108 +261,3 @@ class PortfolioOptimizer: warnings.warn(f"optimization not success ({sol.status})") return sol.x - - -class EnhancedIndexingOptimizer: - """ - Portfolio Optimizer with Enhanced Indexing - - Note: - This optimizer always assumes full investment and no-shorting. - """ - - START_FROM_W0 = 'w0' - START_FROM_BENCH = 'benchmark' - DO_NOT_START_FROM = '' - - def __init__(self, lamb: float = 10, delta: float = 0.4, bench_dev: float = 0.01, inds_dev: float = 0.01, - scale_alpha=True, verbose: bool = False, warm_start: str = '', max_iters: int = 10000): - """ - Args: - lamb (float): risk aversion parameter (larger `lamb` means less focus on return) - delta (float): turnover rate limit - bench_dev (float): benchmark deviation limit - inds_dev (float): industry deviation limit - verbose (bool): if print detailed information about the solver - warm_start (str): whether try to warm start (`w0`/`benchmark`/``) - (https://www.cvxpy.org/tutorial/advanced/index.html#warm-start) - """ - - assert lamb >= 0, "risk aversion parameter `lamb` should be positive" - self.lamb = lamb - - assert delta >= 0, "turnover limit `delta` should be positive" - self.delta = delta - - assert bench_dev >= 0, "benchmark deviation limit `bench_dev` should be positive" - self.bench_dev = bench_dev - - assert inds_dev >= 0, "industry deviation limit `inds_dev` should be positive" - self.inds_dev = inds_dev - - assert warm_start in [self.DO_NOT_START_FROM, self.START_FROM_W0, - self.START_FROM_BENCH], "illegal warm start option" - self.start_from_w0 = (warm_start == self.START_FROM_W0) - self.start_from_bench = (warm_start == self.START_FROM_BENCH) - - self.scale_alpha = scale_alpha - self.verbose = verbose - self.max_iters = max_iters - - def __call__(self, u: np.ndarray, F: np.ndarray, covB: np.ndarray, varU: np.ndarray, w0: np.ndarray, - w_bench: np.ndarray, inds_onehot: np.ndarray - ) -> Union[np.ndarray, pd.Series]: - """ - Args: - u (np.ndarray): expected returns (a.k.a., alpha) - F, covB, varU (np.ndarray): see StructuredCovEstimator - w0 (np.ndarray): initial weights (for turnover control) - w_bench (np.ndarray): benchmark weights - inds_onehot (np.ndarray): industry (onehot) - - Returns: - np.ndarray or pd.Series: optimized portfolio allocation - """ - # scale alpha to match volatility - if self.scale_alpha: - u = u / u.std() - x_variance = np.mean(np.diag(F @ covB @ F.T) + varU) - u *= x_variance ** 0.5 - - w = cp.Variable(len(u)) # num_assets - v = w @ F # num_factors - ret = w @ u - risk = cp.quad_form(v, covB) + cp.sum(cp.multiply(varU, w ** 2)) - obj = cp.Maximize(ret - self.lamb * risk) - d_bench = w - w_bench - d_inds = d_bench @ inds_onehot - cons = [ - w >= 0, - cp.sum(w) == 1, - d_bench >= -self.bench_dev, - d_bench <= self.bench_dev, - d_inds >= -self.inds_dev, - d_inds <= self.inds_dev - ] - if w0 is not None: - turnover = cp.sum(cp.abs(w - w0)) - cons.append(turnover <= self.delta) - - warm_start = False - if self.start_from_w0: - if w0 is None: - print('Warning: try warm start with w0, but w0 is `None`.') - else: - w.value = w0 - warm_start = True - elif self.start_from_bench: - w.value = w_bench - warm_start = True - - prob = cp.Problem(obj, cons) - prob.solve(solver=cp.SCS, verbose=self.verbose, warm_start=warm_start, max_iters=self.max_iters) - - if prob.status != 'optimal': - print('Warning: solve failed.', prob.status) - - return np.asarray(w.value) From 2cc057e438cb412ff60a39d6e83df6724f29b4fe Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 22 Feb 2021 09:09:03 +0800 Subject: [PATCH 013/134] Fix minor mismatches of type hints. --- qlib/portfolio/optimizer.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/qlib/portfolio/optimizer.py b/qlib/portfolio/optimizer.py index 104e2c441..87a8b7416 100644 --- a/qlib/portfolio/optimizer.py +++ b/qlib/portfolio/optimizer.py @@ -85,14 +85,14 @@ class PortfolioOptimizer: if u is not None: assert len(u) == len(S), "`u` has mismatched shape" if isinstance(u, pd.Series): - assert all(u.index == index), "`u` has mismatched index" + assert u.index.equals(index), "`u` has mismatched index" u = u.values # transform initial weights if w0 is not None: assert len(w0) == len(S), "`w0` has mismatched shape" if isinstance(w0, pd.Series): - assert all(w0.index == index), "`w0` has mismatched index" + assert w0.index.equals(index), "`w0` has mismatched index" w0 = w0.values # scale alpha to match volatility @@ -175,7 +175,7 @@ class PortfolioOptimizer: """ return self._solve(len(S), self._get_objective_rp(S), *self._get_constrains(w0)) - def _get_objective_gmv(self, S: np.ndarray) -> np.ndarray: + def _get_objective_gmv(self, S: np.ndarray) -> Callable: """global minimum variance optimization objective Optimization objective @@ -187,7 +187,7 @@ class PortfolioOptimizer: return func - def _get_objective_mvo(self, S: np.ndarray, u: np.ndarray = None) -> np.ndarray: + def _get_objective_mvo(self, S: np.ndarray, u: np.ndarray = None) -> Callable: """mean-variance optimization objective Optimization objective @@ -201,7 +201,7 @@ class PortfolioOptimizer: return func - def _get_objective_rp(self, S: np.ndarray) -> np.ndarray: + def _get_objective_rp(self, S: np.ndarray) -> Callable: """risk-parity optimization objective Optimization objective From 9448a6e2c79a344516e17abba7060d6e62231582 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 22 Feb 2021 09:23:48 +0800 Subject: [PATCH 014/134] Add a abstract class as the base class for all optimization related portfolio constructions. --- .../portfolio_optimizer/enhanced_indexing.py | 4 +- .../portfolio_optimizer/mean_variance.py | 264 ++++++++++++++++++ qlib/portfolio/optimizer.py | 264 +----------------- 3 files changed, 274 insertions(+), 258 deletions(-) create mode 100644 qlib/contrib/portfolio_optimizer/mean_variance.py diff --git a/qlib/contrib/portfolio_optimizer/enhanced_indexing.py b/qlib/contrib/portfolio_optimizer/enhanced_indexing.py index 0c40a617e..323e3154b 100644 --- a/qlib/contrib/portfolio_optimizer/enhanced_indexing.py +++ b/qlib/contrib/portfolio_optimizer/enhanced_indexing.py @@ -6,8 +6,10 @@ import pandas as pd import cvxpy as cp from typing import Union +from ...portfolio.optimizer import BaseOptimizer -class EnhancedIndexingOptimizer: + +class EnhancedIndexingOptimizer(BaseOptimizer): """ Portfolio Optimizer with Enhanced Indexing diff --git a/qlib/contrib/portfolio_optimizer/mean_variance.py b/qlib/contrib/portfolio_optimizer/mean_variance.py new file mode 100644 index 000000000..c3c4f7a3d --- /dev/null +++ b/qlib/contrib/portfolio_optimizer/mean_variance.py @@ -0,0 +1,264 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +import warnings +import numpy as np +import pandas as pd +import scipy.optimize as so +from typing import Optional, Union, Callable, List + +from ...portfolio.optimizer import BaseOptimizer + + +class PortfolioOptimizer(BaseOptimizer): + """Portfolio Optimizer + + The following optimization algorithms are supported: + - `gmv`: Global Minimum Variance Portfolio + - `mvo`: Mean Variance Optimized Portfolio + - `rp`: Risk Parity + - `inv`: Inverse Volatility + + Note: + This optimizer always assumes full investment and no-shorting. + """ + + OPT_GMV = "gmv" + OPT_MVO = "mvo" + OPT_RP = "rp" + OPT_INV = "inv" + + def __init__( + self, + method: str = "inv", + lamb: float = 0, + delta: float = 0, + alpha: float = 0.0, + scale_alpha: bool = True, + tol: float = 1e-8, + ): + """ + Args: + method (str): portfolio optimization method + lamb (float): risk aversion parameter (larger `lamb` means more focus on return) + delta (float): turnover rate limit + alpha (float): l2 norm regularizer + scale_alpha (bool): if to scale alpha to match the volatility of the covariance matrix + tol (float): tolerance for optimization termination + """ + assert method in [self.OPT_GMV, self.OPT_MVO, self.OPT_RP, self.OPT_INV], f"method `{method}` is not supported" + self.method = method + + assert lamb >= 0, f"risk aversion parameter `lamb` should be positive" + self.lamb = lamb + + assert delta >= 0, f"turnover limit `delta` should be positive" + self.delta = delta + + assert alpha >= 0, f"l2 norm regularizer `alpha` should be positive" + self.alpha = alpha + + self.tol = tol + self.scale_alpha = scale_alpha + + def __call__( + self, + S: Union[np.ndarray, pd.DataFrame], + u: Optional[Union[np.ndarray, pd.Series]] = None, + w0: Optional[Union[np.ndarray, pd.Series]] = None, + ) -> Union[np.ndarray, pd.Series]: + """ + Args: + S (np.ndarray or pd.DataFrame): covariance matrix + u (np.ndarray or pd.Series): expected returns (a.k.a., alpha) + w0 (np.ndarray or pd.Series): initial weights (for turnover control) + + Returns: + np.ndarray or pd.Series: optimized portfolio allocation + """ + # transform dataframe into array + index = None + if isinstance(S, pd.DataFrame): + index = S.index + S = S.values + + # transform alpha + if u is not None: + assert len(u) == len(S), "`u` has mismatched shape" + if isinstance(u, pd.Series): + assert u.index.equals(index), "`u` has mismatched index" + u = u.values + + # transform initial weights + if w0 is not None: + assert len(w0) == len(S), "`w0` has mismatched shape" + if isinstance(w0, pd.Series): + assert w0.index.equals(index), "`w0` has mismatched index" + w0 = w0.values + + # scale alpha to match volatility + if u is not None and self.scale_alpha: + u = u / u.std() + u *= np.mean(np.diag(S)) ** 0.5 + + # optimize + w = self._optimize(S, u, w0) + + # restore index if needed + if index is not None: + w = pd.Series(w, index=index) + + return w + + def _optimize(self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None) -> np.ndarray: + + # inverse volatility + if self.method == self.OPT_INV: + if u is not None: + warnings.warn("`u` is set but will not be used for `inv` portfolio") + if w0 is not None: + warnings.warn("`w0` is set but will not be used for `inv` portfolio") + return self._optimize_inv(S) + + # global minimum variance + if self.method == self.OPT_GMV: + if u is not None: + warnings.warn("`u` is set but will not be used for `gmv` portfolio") + return self._optimize_gmv(S, w0) + + # mean-variance + if self.method == self.OPT_MVO: + return self._optimize_mvo(S, u, w0) + + # risk parity + if self.method == self.OPT_RP: + if u is not None: + warnings.warn("`u` is set but will not be used for `rp` portfolio") + return self._optimize_rp(S, w0) + + def _optimize_inv(self, S: np.ndarray) -> np.ndarray: + """Inverse volatility""" + vola = np.diag(S) ** 0.5 + w = 1 / vola + w /= w.sum() + return w + + def _optimize_gmv(self, S: np.ndarray, w0: Optional[np.ndarray] = None) -> np.ndarray: + """optimize global minimum variance portfolio + + This method solves the following optimization problem + min_w w' S w + s.t. w >= 0, sum(w) == 1 + where `S` is the covariance matrix. + """ + return self._solve(len(S), self._get_objective_gmv(S), *self._get_constrains(w0)) + + def _optimize_mvo( + self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None + ) -> np.ndarray: + """optimize mean-variance portfolio + + This method solves the following optimization problem + min_w - w' u + lamb * w' S w + s.t. w >= 0, sum(w) == 1 + where `S` is the covariance matrix, `u` is the expected returns, + and `lamb` is the risk aversion parameter. + """ + return self._solve(len(S), self._get_objective_mvo(S, u), *self._get_constrains(w0)) + + def _optimize_rp(self, S: np.ndarray, w0: Optional[np.ndarray] = None) -> np.ndarray: + """optimize risk parity portfolio + + This method solves the following optimization problem + min_w sum_i [w_i - (w' S w) / ((S w)_i * N)]**2 + s.t. w >= 0, sum(w) == 1 + where `S` is the covariance matrix and `N` is the number of stocks. + """ + return self._solve(len(S), self._get_objective_rp(S), *self._get_constrains(w0)) + + def _get_objective_gmv(self, S: np.ndarray) -> Callable: + """global minimum variance optimization objective + + Optimization objective + min_w w' S w + """ + + def func(x): + return x @ S @ x + + return func + + def _get_objective_mvo(self, S: np.ndarray, u: np.ndarray = None) -> Callable: + """mean-variance optimization objective + + Optimization objective + min_w - w' u + lamb * w' S w + """ + + def func(x): + risk = x @ S @ x + ret = x @ u + return -ret + self.lamb * risk + + return func + + def _get_objective_rp(self, S: np.ndarray) -> Callable: + """risk-parity optimization objective + + Optimization objective + min_w sum_i [w_i - (w' S w) / ((S w)_i * N)]**2 + """ + + def func(x): + N = len(x) + Sx = S @ x + xSx = x @ Sx + return np.sum((x - xSx / Sx / N) ** 2) + + return func + + def _get_constrains(self, w0: Optional[np.ndarray] = None): + """optimization constraints + + Defines the following constraints: + - no shorting and leverage: 0 <= w <= 1 + - full investment: sum(w) == 1 + - turnover constraint: |w - w0| <= delta + """ + + # no shorting and leverage + bounds = so.Bounds(0.0, 1.0) + + # full investment constraint + cons = [{"type": "eq", "fun": lambda x: np.sum(x) - 1}] # == 0 + + # turnover constraint + if w0 is not None: + cons.append({"type": "ineq", "fun": lambda x: self.delta - np.sum(np.abs(x - w0))}) # >= 0 + + return bounds, cons + + def _solve(self, n: int, obj: Callable, bounds: so.Bounds, cons: List) -> np.ndarray: + """solve optimization + + Args: + n (int): number of parameters + obj (callable): optimization objective + bounds (Bounds): bounds of parameters + cons (list): optimization constraints + """ + # add l2 regularization + wrapped_obj = obj + if self.alpha > 0: + def opt_obj(x): + return obj(x) + self.alpha * np.sum(np.square(x)) + + wrapped_obj = opt_obj + + # solve + x0 = np.ones(n) / n # init results + sol = so.minimize(wrapped_obj, x0, bounds=bounds, constraints=cons, tol=self.tol) + if not sol.success: + warnings.warn(f"optimization not success ({sol.status})") + + return sol.x diff --git a/qlib/portfolio/optimizer.py b/qlib/portfolio/optimizer.py index 87a8b7416..c63d93656 100644 --- a/qlib/portfolio/optimizer.py +++ b/qlib/portfolio/optimizer.py @@ -1,263 +1,13 @@ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. -import warnings -import numpy as np -import pandas as pd -import scipy.optimize as so - -from typing import Optional, Union, Callable, List +import abc -class PortfolioOptimizer: - """Portfolio Optimizer +class BaseOptimizer(abc.ABC): + """Modeling things""" - The following optimization algorithms are supported: - - `gmv`: Global Minimum Variance Portfolio - - `mvo`: Mean Variance Optimized Portfolio - - `rp`: Risk Parity - - `inv`: Inverse Volatility - - Note: - This optimizer always assumes full investment and no-shorting. - """ - - OPT_GMV = "gmv" - OPT_MVO = "mvo" - OPT_RP = "rp" - OPT_INV = "inv" - - def __init__( - self, - method: str = "inv", - lamb: float = 0, - delta: float = 0, - alpha: float = 0.0, - scale_alpha: bool = True, - tol: float = 1e-8, - ): - """ - Args: - method (str): portfolio optimization method - lamb (float): risk aversion parameter (larger `lamb` means more focus on return) - delta (float): turnover rate limit - alpha (float): l2 norm regularizer - scale_alpha (bool): if to scale alpha to match the volatility of the covariance matrix - tol (float): tolerance for optimization termination - """ - assert method in [self.OPT_GMV, self.OPT_MVO, self.OPT_RP, self.OPT_INV], f"method `{method}` is not supported" - self.method = method - - assert lamb >= 0, f"risk aversion parameter `lamb` should be positive" - self.lamb = lamb - - assert delta >= 0, f"turnover limit `delta` should be positive" - self.delta = delta - - assert alpha >= 0, f"l2 norm regularizer `alpha` should be positive" - self.alpha = alpha - - self.tol = tol - self.scale_alpha = scale_alpha - - def __call__( - self, - S: Union[np.ndarray, pd.DataFrame], - u: Optional[Union[np.ndarray, pd.Series]] = None, - w0: Optional[Union[np.ndarray, pd.Series]] = None, - ) -> Union[np.ndarray, pd.Series]: - """ - Args: - S (np.ndarray or pd.DataFrame): covariance matrix - u (np.ndarray or pd.Series): expected returns (a.k.a., alpha) - w0 (np.ndarray or pd.Series): initial weights (for turnover control) - - Returns: - np.ndarray or pd.Series: optimized portfolio allocation - """ - # transform dataframe into array - index = None - if isinstance(S, pd.DataFrame): - index = S.index - S = S.values - - # transform alpha - if u is not None: - assert len(u) == len(S), "`u` has mismatched shape" - if isinstance(u, pd.Series): - assert u.index.equals(index), "`u` has mismatched index" - u = u.values - - # transform initial weights - if w0 is not None: - assert len(w0) == len(S), "`w0` has mismatched shape" - if isinstance(w0, pd.Series): - assert w0.index.equals(index), "`w0` has mismatched index" - w0 = w0.values - - # scale alpha to match volatility - if u is not None and self.scale_alpha: - u = u / u.std() - u *= np.mean(np.diag(S)) ** 0.5 - - # optimize - w = self._optimize(S, u, w0) - - # restore index if needed - if index is not None: - w = pd.Series(w, index=index) - - return w - - def _optimize(self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None) -> np.ndarray: - - # inverse volatility - if self.method == self.OPT_INV: - if u is not None: - warnings.warn("`u` is set but will not be used for `inv` portfolio") - if w0 is not None: - warnings.warn("`w0` is set but will not be used for `inv` portfolio") - return self._optimize_inv(S) - - # global minimum variance - if self.method == self.OPT_GMV: - if u is not None: - warnings.warn("`u` is set but will not be used for `gmv` portfolio") - return self._optimize_gmv(S, w0) - - # mean-variance - if self.method == self.OPT_MVO: - return self._optimize_mvo(S, u, w0) - - # risk parity - if self.method == self.OPT_RP: - if u is not None: - warnings.warn("`u` is set but will not be used for `rp` portfolio") - return self._optimize_rp(S, w0) - - def _optimize_inv(self, S: np.ndarray) -> np.ndarray: - """Inverse volatility""" - vola = np.diag(S) ** 0.5 - w = 1 / vola - w /= w.sum() - return w - - def _optimize_gmv(self, S: np.ndarray, w0: Optional[np.ndarray] = None) -> np.ndarray: - """optimize global minimum variance portfolio - - This method solves the following optimization problem - min_w w' S w - s.t. w >= 0, sum(w) == 1 - where `S` is the covariance matrix. - """ - return self._solve(len(S), self._get_objective_gmv(S), *self._get_constrains(w0)) - - def _optimize_mvo( - self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None - ) -> np.ndarray: - """optimize mean-variance portfolio - - This method solves the following optimization problem - min_w - w' u + lamb * w' S w - s.t. w >= 0, sum(w) == 1 - where `S` is the covariance matrix, `u` is the expected returns, - and `lamb` is the risk aversion parameter. - """ - return self._solve(len(S), self._get_objective_mvo(S, u), *self._get_constrains(w0)) - - def _optimize_rp(self, S: np.ndarray, w0: Optional[np.ndarray] = None) -> np.ndarray: - """optimize risk parity portfolio - - This method solves the following optimization problem - min_w sum_i [w_i - (w' S w) / ((S w)_i * N)]**2 - s.t. w >= 0, sum(w) == 1 - where `S` is the covariance matrix and `N` is the number of stocks. - """ - return self._solve(len(S), self._get_objective_rp(S), *self._get_constrains(w0)) - - def _get_objective_gmv(self, S: np.ndarray) -> Callable: - """global minimum variance optimization objective - - Optimization objective - min_w w' S w - """ - - def func(x): - return x @ S @ x - - return func - - def _get_objective_mvo(self, S: np.ndarray, u: np.ndarray = None) -> Callable: - """mean-variance optimization objective - - Optimization objective - min_w - w' u + lamb * w' S w - """ - - def func(x): - risk = x @ S @ x - ret = x @ u - return -ret + self.lamb * risk - - return func - - def _get_objective_rp(self, S: np.ndarray) -> Callable: - """risk-parity optimization objective - - Optimization objective - min_w sum_i [w_i - (w' S w) / ((S w)_i * N)]**2 - """ - - def func(x): - N = len(x) - Sx = S @ x - xSx = x @ Sx - return np.sum((x - xSx / Sx / N) ** 2) - - return func - - def _get_constrains(self, w0: Optional[np.ndarray] = None): - """optimization constraints - - Defines the following constraints: - - no shorting and leverage: 0 <= w <= 1 - - full investment: sum(w) == 1 - - turnover constraint: |w - w0| <= delta - """ - - # no shorting and leverage - bounds = so.Bounds(0.0, 1.0) - - # full investment constraint - cons = [{"type": "eq", "fun": lambda x: np.sum(x) - 1}] # == 0 - - # turnover constraint - if w0 is not None: - cons.append({"type": "ineq", "fun": lambda x: self.delta - np.sum(np.abs(x - w0))}) # >= 0 - - return bounds, cons - - def _solve(self, n: int, obj: Callable, bounds: so.Bounds, cons: List) -> np.ndarray: - """solve optimization - - Args: - n (int): number of parameters - obj (callable): optimization objective - bounds (Bounds): bounds of parameters - cons (list): optimization constraints - """ - # add l2 regularization - wrapped_obj = obj - if self.alpha > 0: - def opt_obj(x): - return obj(x) + self.alpha * np.sum(np.square(x)) - - wrapped_obj = opt_obj - - # solve - x0 = np.ones(n) / n # init results - sol = so.minimize(wrapped_obj, x0, bounds=bounds, constraints=cons, tol=self.tol) - if not sol.success: - warnings.warn(f"optimization not success ({sol.status})") - - return sol.x + @abc.abstractmethod + def __call__(self, *args, **kwargs) -> object: + """ Generate a optimized portfolio allocation """ + pass From 42f882504e09d36f20c29f3eaafa11f0249144ed Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 22 Feb 2021 09:25:48 +0800 Subject: [PATCH 015/134] Reformat code to follow PEP 8. --- qlib/model/base.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/qlib/model/base.py b/qlib/model/base.py index 5a295787f..a7001f0a6 100644 --- a/qlib/model/base.py +++ b/qlib/model/base.py @@ -43,8 +43,8 @@ class Model(BaseModel): # get weights try: - wdf_train, wdf_valid = dataset.prepare(["train", "valid"], col_set=["weight"], data_key=DataHandlerLP.DK_L) - w_train, w_valid = wdf_train["weight"], wdf_valid["weight"] + wdf_train, wdf_valid = dataset.prepare(["train", "valid"], col_set=["weight"], + data_key=DataHandlerLP.DK_L, w_train, w_valid = wdf_train["weight"], wdf_valid["weight"] except KeyError as e: w_train = pd.DataFrame(np.ones_like(y_train.values), index=y_train.index) w_valid = pd.DataFrame(np.ones_like(y_valid.values), index=y_valid.index) From f7d3e56561d4059bc85dc7922017706bfa322750 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 22 Feb 2021 09:57:41 +0800 Subject: [PATCH 016/134] Merge optimization related portfolio construction back to portfolio/optimizer. --- .../portfolio_optimizer/enhanced_indexing.py | 114 ------ .../portfolio_optimizer/mean_variance.py | 264 ------------- qlib/portfolio/optimizer.py | 367 +++++++++++++++++- 3 files changed, 366 insertions(+), 379 deletions(-) delete mode 100644 qlib/contrib/portfolio_optimizer/enhanced_indexing.py delete mode 100644 qlib/contrib/portfolio_optimizer/mean_variance.py diff --git a/qlib/contrib/portfolio_optimizer/enhanced_indexing.py b/qlib/contrib/portfolio_optimizer/enhanced_indexing.py deleted file mode 100644 index 323e3154b..000000000 --- a/qlib/contrib/portfolio_optimizer/enhanced_indexing.py +++ /dev/null @@ -1,114 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. - -import numpy as np -import pandas as pd -import cvxpy as cp -from typing import Union - -from ...portfolio.optimizer import BaseOptimizer - - -class EnhancedIndexingOptimizer(BaseOptimizer): - """ - Portfolio Optimizer with Enhanced Indexing - - Note: - This optimizer always assumes full investment and no-shorting. - """ - - START_FROM_W0 = 'w0' - START_FROM_BENCH = 'benchmark' - DO_NOT_START_FROM = 'no_warm_start' - - def __init__(self, lamb: float = 10, delta: float = 0.4, bench_dev: float = 0.01, inds_dev: float = 0.01, - scale_alpha=True, verbose: bool = False, warm_start: str = DO_NOT_START_FROM, max_iters: int = 10000): - """ - Args: - lamb (float): risk aversion parameter (larger `lamb` means less focus on return) - delta (float): turnover rate limit - bench_dev (float): benchmark deviation limit - inds_dev (float): industry deviation limit - verbose (bool): if print detailed information about the solver - warm_start (str): whether try to warm start (`w0`/`benchmark`/``) - (https://www.cvxpy.org/tutorial/advanced/index.html#warm-start) - """ - - assert lamb >= 0, "risk aversion parameter `lamb` should be positive" - self.lamb = lamb - - assert delta >= 0, "turnover limit `delta` should be positive" - self.delta = delta - - assert bench_dev >= 0, "benchmark deviation limit `bench_dev` should be positive" - self.bench_dev = bench_dev - - assert inds_dev >= 0, "industry deviation limit `inds_dev` should be positive" - self.inds_dev = inds_dev - - assert warm_start in [self.DO_NOT_START_FROM, self.START_FROM_W0, - self.START_FROM_BENCH], "illegal warm start option" - self.start_from_w0 = (warm_start == self.START_FROM_W0) - self.start_from_bench = (warm_start == self.START_FROM_BENCH) - - self.scale_alpha = scale_alpha - self.verbose = verbose - self.max_iters = max_iters - - def __call__(self, u: np.ndarray, F: np.ndarray, covB: np.ndarray, varU: np.ndarray, w0: np.ndarray, - w_bench: np.ndarray, inds_onehot: np.ndarray - ) -> Union[np.ndarray, pd.Series]: - """ - Args: - u (np.ndarray): expected returns (a.k.a., alpha) - F, covB, varU (np.ndarray): see StructuredCovEstimator - w0 (np.ndarray): initial weights (for turnover control) - w_bench (np.ndarray): benchmark weights - inds_onehot (np.ndarray): industry (onehot) - - Returns: - np.ndarray or pd.Series: optimized portfolio allocation - """ - # scale alpha to match volatility - if self.scale_alpha: - u = u / u.std() - x_variance = np.mean(np.diag(F @ covB @ F.T) + varU) - u *= x_variance ** 0.5 - - w = cp.Variable(len(u)) # num_assets - v = w @ F # num_factors - ret = w @ u - risk = cp.quad_form(v, covB) + cp.sum(cp.multiply(varU, w ** 2)) - obj = cp.Maximize(ret - self.lamb * risk) - d_bench = w - w_bench - d_inds = d_bench @ inds_onehot - cons = [ - w >= 0, - cp.sum(w) == 1, - d_bench >= -self.bench_dev, - d_bench <= self.bench_dev, - d_inds >= -self.inds_dev, - d_inds <= self.inds_dev - ] - if w0 is not None: - turnover = cp.sum(cp.abs(w - w0)) - cons.append(turnover <= self.delta) - - warm_start = False - if self.start_from_w0: - if w0 is None: - print('Warning: try warm start with w0, but w0 is `None`.') - else: - w.value = w0 - warm_start = True - elif self.start_from_bench: - w.value = w_bench - warm_start = True - - prob = cp.Problem(obj, cons) - prob.solve(solver=cp.SCS, verbose=self.verbose, warm_start=warm_start, max_iters=self.max_iters) - - if prob.status != 'optimal': - print('Warning: solve failed.', prob.status) - - return np.asarray(w.value) diff --git a/qlib/contrib/portfolio_optimizer/mean_variance.py b/qlib/contrib/portfolio_optimizer/mean_variance.py deleted file mode 100644 index c3c4f7a3d..000000000 --- a/qlib/contrib/portfolio_optimizer/mean_variance.py +++ /dev/null @@ -1,264 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. - -import warnings -import numpy as np -import pandas as pd -import scipy.optimize as so -from typing import Optional, Union, Callable, List - -from ...portfolio.optimizer import BaseOptimizer - - -class PortfolioOptimizer(BaseOptimizer): - """Portfolio Optimizer - - The following optimization algorithms are supported: - - `gmv`: Global Minimum Variance Portfolio - - `mvo`: Mean Variance Optimized Portfolio - - `rp`: Risk Parity - - `inv`: Inverse Volatility - - Note: - This optimizer always assumes full investment and no-shorting. - """ - - OPT_GMV = "gmv" - OPT_MVO = "mvo" - OPT_RP = "rp" - OPT_INV = "inv" - - def __init__( - self, - method: str = "inv", - lamb: float = 0, - delta: float = 0, - alpha: float = 0.0, - scale_alpha: bool = True, - tol: float = 1e-8, - ): - """ - Args: - method (str): portfolio optimization method - lamb (float): risk aversion parameter (larger `lamb` means more focus on return) - delta (float): turnover rate limit - alpha (float): l2 norm regularizer - scale_alpha (bool): if to scale alpha to match the volatility of the covariance matrix - tol (float): tolerance for optimization termination - """ - assert method in [self.OPT_GMV, self.OPT_MVO, self.OPT_RP, self.OPT_INV], f"method `{method}` is not supported" - self.method = method - - assert lamb >= 0, f"risk aversion parameter `lamb` should be positive" - self.lamb = lamb - - assert delta >= 0, f"turnover limit `delta` should be positive" - self.delta = delta - - assert alpha >= 0, f"l2 norm regularizer `alpha` should be positive" - self.alpha = alpha - - self.tol = tol - self.scale_alpha = scale_alpha - - def __call__( - self, - S: Union[np.ndarray, pd.DataFrame], - u: Optional[Union[np.ndarray, pd.Series]] = None, - w0: Optional[Union[np.ndarray, pd.Series]] = None, - ) -> Union[np.ndarray, pd.Series]: - """ - Args: - S (np.ndarray or pd.DataFrame): covariance matrix - u (np.ndarray or pd.Series): expected returns (a.k.a., alpha) - w0 (np.ndarray or pd.Series): initial weights (for turnover control) - - Returns: - np.ndarray or pd.Series: optimized portfolio allocation - """ - # transform dataframe into array - index = None - if isinstance(S, pd.DataFrame): - index = S.index - S = S.values - - # transform alpha - if u is not None: - assert len(u) == len(S), "`u` has mismatched shape" - if isinstance(u, pd.Series): - assert u.index.equals(index), "`u` has mismatched index" - u = u.values - - # transform initial weights - if w0 is not None: - assert len(w0) == len(S), "`w0` has mismatched shape" - if isinstance(w0, pd.Series): - assert w0.index.equals(index), "`w0` has mismatched index" - w0 = w0.values - - # scale alpha to match volatility - if u is not None and self.scale_alpha: - u = u / u.std() - u *= np.mean(np.diag(S)) ** 0.5 - - # optimize - w = self._optimize(S, u, w0) - - # restore index if needed - if index is not None: - w = pd.Series(w, index=index) - - return w - - def _optimize(self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None) -> np.ndarray: - - # inverse volatility - if self.method == self.OPT_INV: - if u is not None: - warnings.warn("`u` is set but will not be used for `inv` portfolio") - if w0 is not None: - warnings.warn("`w0` is set but will not be used for `inv` portfolio") - return self._optimize_inv(S) - - # global minimum variance - if self.method == self.OPT_GMV: - if u is not None: - warnings.warn("`u` is set but will not be used for `gmv` portfolio") - return self._optimize_gmv(S, w0) - - # mean-variance - if self.method == self.OPT_MVO: - return self._optimize_mvo(S, u, w0) - - # risk parity - if self.method == self.OPT_RP: - if u is not None: - warnings.warn("`u` is set but will not be used for `rp` portfolio") - return self._optimize_rp(S, w0) - - def _optimize_inv(self, S: np.ndarray) -> np.ndarray: - """Inverse volatility""" - vola = np.diag(S) ** 0.5 - w = 1 / vola - w /= w.sum() - return w - - def _optimize_gmv(self, S: np.ndarray, w0: Optional[np.ndarray] = None) -> np.ndarray: - """optimize global minimum variance portfolio - - This method solves the following optimization problem - min_w w' S w - s.t. w >= 0, sum(w) == 1 - where `S` is the covariance matrix. - """ - return self._solve(len(S), self._get_objective_gmv(S), *self._get_constrains(w0)) - - def _optimize_mvo( - self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None - ) -> np.ndarray: - """optimize mean-variance portfolio - - This method solves the following optimization problem - min_w - w' u + lamb * w' S w - s.t. w >= 0, sum(w) == 1 - where `S` is the covariance matrix, `u` is the expected returns, - and `lamb` is the risk aversion parameter. - """ - return self._solve(len(S), self._get_objective_mvo(S, u), *self._get_constrains(w0)) - - def _optimize_rp(self, S: np.ndarray, w0: Optional[np.ndarray] = None) -> np.ndarray: - """optimize risk parity portfolio - - This method solves the following optimization problem - min_w sum_i [w_i - (w' S w) / ((S w)_i * N)]**2 - s.t. w >= 0, sum(w) == 1 - where `S` is the covariance matrix and `N` is the number of stocks. - """ - return self._solve(len(S), self._get_objective_rp(S), *self._get_constrains(w0)) - - def _get_objective_gmv(self, S: np.ndarray) -> Callable: - """global minimum variance optimization objective - - Optimization objective - min_w w' S w - """ - - def func(x): - return x @ S @ x - - return func - - def _get_objective_mvo(self, S: np.ndarray, u: np.ndarray = None) -> Callable: - """mean-variance optimization objective - - Optimization objective - min_w - w' u + lamb * w' S w - """ - - def func(x): - risk = x @ S @ x - ret = x @ u - return -ret + self.lamb * risk - - return func - - def _get_objective_rp(self, S: np.ndarray) -> Callable: - """risk-parity optimization objective - - Optimization objective - min_w sum_i [w_i - (w' S w) / ((S w)_i * N)]**2 - """ - - def func(x): - N = len(x) - Sx = S @ x - xSx = x @ Sx - return np.sum((x - xSx / Sx / N) ** 2) - - return func - - def _get_constrains(self, w0: Optional[np.ndarray] = None): - """optimization constraints - - Defines the following constraints: - - no shorting and leverage: 0 <= w <= 1 - - full investment: sum(w) == 1 - - turnover constraint: |w - w0| <= delta - """ - - # no shorting and leverage - bounds = so.Bounds(0.0, 1.0) - - # full investment constraint - cons = [{"type": "eq", "fun": lambda x: np.sum(x) - 1}] # == 0 - - # turnover constraint - if w0 is not None: - cons.append({"type": "ineq", "fun": lambda x: self.delta - np.sum(np.abs(x - w0))}) # >= 0 - - return bounds, cons - - def _solve(self, n: int, obj: Callable, bounds: so.Bounds, cons: List) -> np.ndarray: - """solve optimization - - Args: - n (int): number of parameters - obj (callable): optimization objective - bounds (Bounds): bounds of parameters - cons (list): optimization constraints - """ - # add l2 regularization - wrapped_obj = obj - if self.alpha > 0: - def opt_obj(x): - return obj(x) + self.alpha * np.sum(np.square(x)) - - wrapped_obj = opt_obj - - # solve - x0 = np.ones(n) / n # init results - sol = so.minimize(wrapped_obj, x0, bounds=bounds, constraints=cons, tol=self.tol) - if not sol.success: - warnings.warn(f"optimization not success ({sol.status})") - - return sol.x diff --git a/qlib/portfolio/optimizer.py b/qlib/portfolio/optimizer.py index c63d93656..728a04ea9 100644 --- a/qlib/portfolio/optimizer.py +++ b/qlib/portfolio/optimizer.py @@ -2,12 +2,377 @@ # Licensed under the MIT License. import abc +import warnings +import numpy as np +import cvxpy as cp +import pandas as pd +import scipy.optimize as so +from typing import Optional, Union, Callable, List class BaseOptimizer(abc.ABC): - """Modeling things""" + """ Construct portfolio with a optimization related method """ @abc.abstractmethod def __call__(self, *args, **kwargs) -> object: """ Generate a optimized portfolio allocation """ pass + + +class PortfolioOptimizer(BaseOptimizer): + """Portfolio Optimizer + + The following optimization algorithms are supported: + - `gmv`: Global Minimum Variance Portfolio + - `mvo`: Mean Variance Optimized Portfolio + - `rp`: Risk Parity + - `inv`: Inverse Volatility + + Note: + This optimizer always assumes full investment and no-shorting. + """ + + OPT_GMV = "gmv" + OPT_MVO = "mvo" + OPT_RP = "rp" + OPT_INV = "inv" + + def __init__( + self, + method: str = "inv", + lamb: float = 0, + delta: float = 0, + alpha: float = 0.0, + scale_alpha: bool = True, + tol: float = 1e-8, + ): + """ + Args: + method (str): portfolio optimization method + lamb (float): risk aversion parameter (larger `lamb` means more focus on return) + delta (float): turnover rate limit + alpha (float): l2 norm regularizer + scale_alpha (bool): if to scale alpha to match the volatility of the covariance matrix + tol (float): tolerance for optimization termination + """ + assert method in [self.OPT_GMV, self.OPT_MVO, self.OPT_RP, self.OPT_INV], f"method `{method}` is not supported" + self.method = method + + assert lamb >= 0, f"risk aversion parameter `lamb` should be positive" + self.lamb = lamb + + assert delta >= 0, f"turnover limit `delta` should be positive" + self.delta = delta + + assert alpha >= 0, f"l2 norm regularizer `alpha` should be positive" + self.alpha = alpha + + self.tol = tol + self.scale_alpha = scale_alpha + + def __call__( + self, + S: Union[np.ndarray, pd.DataFrame], + u: Optional[Union[np.ndarray, pd.Series]] = None, + w0: Optional[Union[np.ndarray, pd.Series]] = None, + ) -> Union[np.ndarray, pd.Series]: + """ + Args: + S (np.ndarray or pd.DataFrame): covariance matrix + u (np.ndarray or pd.Series): expected returns (a.k.a., alpha) + w0 (np.ndarray or pd.Series): initial weights (for turnover control) + + Returns: + np.ndarray or pd.Series: optimized portfolio allocation + """ + # transform dataframe into array + index = None + if isinstance(S, pd.DataFrame): + index = S.index + S = S.values + + # transform alpha + if u is not None: + assert len(u) == len(S), "`u` has mismatched shape" + if isinstance(u, pd.Series): + assert u.index.equals(index), "`u` has mismatched index" + u = u.values + + # transform initial weights + if w0 is not None: + assert len(w0) == len(S), "`w0` has mismatched shape" + if isinstance(w0, pd.Series): + assert w0.index.equals(index), "`w0` has mismatched index" + w0 = w0.values + + # scale alpha to match volatility + if u is not None and self.scale_alpha: + u = u / u.std() + u *= np.mean(np.diag(S)) ** 0.5 + + # optimize + w = self._optimize(S, u, w0) + + # restore index if needed + if index is not None: + w = pd.Series(w, index=index) + + return w + + def _optimize(self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None) -> np.ndarray: + + # inverse volatility + if self.method == self.OPT_INV: + if u is not None: + warnings.warn("`u` is set but will not be used for `inv` portfolio") + if w0 is not None: + warnings.warn("`w0` is set but will not be used for `inv` portfolio") + return self._optimize_inv(S) + + # global minimum variance + if self.method == self.OPT_GMV: + if u is not None: + warnings.warn("`u` is set but will not be used for `gmv` portfolio") + return self._optimize_gmv(S, w0) + + # mean-variance + if self.method == self.OPT_MVO: + return self._optimize_mvo(S, u, w0) + + # risk parity + if self.method == self.OPT_RP: + if u is not None: + warnings.warn("`u` is set but will not be used for `rp` portfolio") + return self._optimize_rp(S, w0) + + def _optimize_inv(self, S: np.ndarray) -> np.ndarray: + """Inverse volatility""" + vola = np.diag(S) ** 0.5 + w = 1 / vola + w /= w.sum() + return w + + def _optimize_gmv(self, S: np.ndarray, w0: Optional[np.ndarray] = None) -> np.ndarray: + """optimize global minimum variance portfolio + + This method solves the following optimization problem + min_w w' S w + s.t. w >= 0, sum(w) == 1 + where `S` is the covariance matrix. + """ + return self._solve(len(S), self._get_objective_gmv(S), *self._get_constrains(w0)) + + def _optimize_mvo( + self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None + ) -> np.ndarray: + """optimize mean-variance portfolio + + This method solves the following optimization problem + min_w - w' u + lamb * w' S w + s.t. w >= 0, sum(w) == 1 + where `S` is the covariance matrix, `u` is the expected returns, + and `lamb` is the risk aversion parameter. + """ + return self._solve(len(S), self._get_objective_mvo(S, u), *self._get_constrains(w0)) + + def _optimize_rp(self, S: np.ndarray, w0: Optional[np.ndarray] = None) -> np.ndarray: + """optimize risk parity portfolio + + This method solves the following optimization problem + min_w sum_i [w_i - (w' S w) / ((S w)_i * N)]**2 + s.t. w >= 0, sum(w) == 1 + where `S` is the covariance matrix and `N` is the number of stocks. + """ + return self._solve(len(S), self._get_objective_rp(S), *self._get_constrains(w0)) + + def _get_objective_gmv(self, S: np.ndarray) -> Callable: + """global minimum variance optimization objective + + Optimization objective + min_w w' S w + """ + + def func(x): + return x @ S @ x + + return func + + def _get_objective_mvo(self, S: np.ndarray, u: np.ndarray = None) -> Callable: + """mean-variance optimization objective + + Optimization objective + min_w - w' u + lamb * w' S w + """ + + def func(x): + risk = x @ S @ x + ret = x @ u + return -ret + self.lamb * risk + + return func + + def _get_objective_rp(self, S: np.ndarray) -> Callable: + """risk-parity optimization objective + + Optimization objective + min_w sum_i [w_i - (w' S w) / ((S w)_i * N)]**2 + """ + + def func(x): + N = len(x) + Sx = S @ x + xSx = x @ Sx + return np.sum((x - xSx / Sx / N) ** 2) + + return func + + def _get_constrains(self, w0: Optional[np.ndarray] = None): + """optimization constraints + + Defines the following constraints: + - no shorting and leverage: 0 <= w <= 1 + - full investment: sum(w) == 1 + - turnover constraint: |w - w0| <= delta + """ + + # no shorting and leverage + bounds = so.Bounds(0.0, 1.0) + + # full investment constraint + cons = [{"type": "eq", "fun": lambda x: np.sum(x) - 1}] # == 0 + + # turnover constraint + if w0 is not None: + cons.append({"type": "ineq", "fun": lambda x: self.delta - np.sum(np.abs(x - w0))}) # >= 0 + + return bounds, cons + + def _solve(self, n: int, obj: Callable, bounds: so.Bounds, cons: List) -> np.ndarray: + """solve optimization + + Args: + n (int): number of parameters + obj (callable): optimization objective + bounds (Bounds): bounds of parameters + cons (list): optimization constraints + """ + # add l2 regularization + wrapped_obj = obj + if self.alpha > 0: + def opt_obj(x): + return obj(x) + self.alpha * np.sum(np.square(x)) + + wrapped_obj = opt_obj + + # solve + x0 = np.ones(n) / n # init results + sol = so.minimize(wrapped_obj, x0, bounds=bounds, constraints=cons, tol=self.tol) + if not sol.success: + warnings.warn(f"optimization not success ({sol.status})") + + return sol.x + + +class EnhancedIndexingOptimizer(BaseOptimizer): + """ + Portfolio Optimizer with Enhanced Indexing + + Note: + This optimizer always assumes full investment and no-shorting. + """ + + START_FROM_W0 = 'w0' + START_FROM_BENCH = 'benchmark' + DO_NOT_START_FROM = 'no_warm_start' + + def __init__(self, lamb: float = 10, delta: float = 0.4, bench_dev: float = 0.01, inds_dev: float = 0.01, + scale_alpha=True, verbose: bool = False, warm_start: str = DO_NOT_START_FROM, max_iters: int = 10000): + """ + Args: + lamb (float): risk aversion parameter (larger `lamb` means less focus on return) + delta (float): turnover rate limit + bench_dev (float): benchmark deviation limit + inds_dev (float): industry deviation limit + verbose (bool): if print detailed information about the solver + warm_start (str): whether try to warm start (`w0`/`benchmark`/``) + (https://www.cvxpy.org/tutorial/advanced/index.html#warm-start) + """ + + assert lamb >= 0, "risk aversion parameter `lamb` should be positive" + self.lamb = lamb + + assert delta >= 0, "turnover limit `delta` should be positive" + self.delta = delta + + assert bench_dev >= 0, "benchmark deviation limit `bench_dev` should be positive" + self.bench_dev = bench_dev + + assert inds_dev >= 0, "industry deviation limit `inds_dev` should be positive" + self.inds_dev = inds_dev + + assert warm_start in [self.DO_NOT_START_FROM, self.START_FROM_W0, + self.START_FROM_BENCH], "illegal warm start option" + self.start_from_w0 = (warm_start == self.START_FROM_W0) + self.start_from_bench = (warm_start == self.START_FROM_BENCH) + + self.scale_alpha = scale_alpha + self.verbose = verbose + self.max_iters = max_iters + + def __call__(self, u: np.ndarray, F: np.ndarray, covB: np.ndarray, varU: np.ndarray, w0: np.ndarray, + w_bench: np.ndarray, inds_onehot: np.ndarray + ) -> Union[np.ndarray, pd.Series]: + """ + Args: + u (np.ndarray): expected returns (a.k.a., alpha) + F, covB, varU (np.ndarray): see StructuredCovEstimator + w0 (np.ndarray): initial weights (for turnover control) + w_bench (np.ndarray): benchmark weights + inds_onehot (np.ndarray): industry (onehot) + + Returns: + np.ndarray or pd.Series: optimized portfolio allocation + """ + # scale alpha to match volatility + if self.scale_alpha: + u = u / u.std() + x_variance = np.mean(np.diag(F @ covB @ F.T) + varU) + u *= x_variance ** 0.5 + + w = cp.Variable(len(u)) # num_assets + v = w @ F # num_factors + ret = w @ u + risk = cp.quad_form(v, covB) + cp.sum(cp.multiply(varU, w ** 2)) + obj = cp.Maximize(ret - self.lamb * risk) + d_bench = w - w_bench + d_inds = d_bench @ inds_onehot + cons = [ + w >= 0, + cp.sum(w) == 1, + d_bench >= -self.bench_dev, + d_bench <= self.bench_dev, + d_inds >= -self.inds_dev, + d_inds <= self.inds_dev + ] + if w0 is not None: + turnover = cp.sum(cp.abs(w - w0)) + cons.append(turnover <= self.delta) + + warm_start = False + if self.start_from_w0: + if w0 is None: + print('Warning: try warm start with w0, but w0 is `None`.') + else: + w.value = w0 + warm_start = True + elif self.start_from_bench: + w.value = w_bench + warm_start = True + + prob = cp.Problem(obj, cons) + prob.solve(solver=cp.SCS, verbose=self.verbose, warm_start=warm_start, max_iters=self.max_iters) + + if prob.status != 'optimal': + print('Warning: solve failed.', prob.status) + + return np.asarray(w.value) From 58f74cfd84b1f7e94de4f47b5c09d063ca8ed507 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 22 Feb 2021 10:07:03 +0800 Subject: [PATCH 017/134] Reformat code to follow PEP 8. --- qlib/model/riskmodel.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/qlib/model/riskmodel.py b/qlib/model/riskmodel.py index 32984ed6a..8eec73e00 100644 --- a/qlib/model/riskmodel.py +++ b/qlib/model/riskmodel.py @@ -1,11 +1,10 @@ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. -import warnings import numpy as np import pandas as pd - from typing import Union +from sklearn.decomposition import PCA, FactorAnalysis from qlib.model.base import BaseModel @@ -124,7 +123,7 @@ class RiskModel(BaseModel): X = np.nan_to_num(X) elif self.nan_option == self.MASK_NAN: X = np.ma.masked_invalid(X) - # centerize + # centralize if not self.assume_centered: X = X - np.nanmean(X, axis=0) return X @@ -162,8 +161,9 @@ class ShrinkCovEstimator(RiskModel): [3] Ledoit, O., & Wolf, M. (2003). Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of Empirical Finance, 10(5), 603–621. https://doi.org/10.1016/S0927-5398(03)00007-0 - [4] Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O. (2010). Shrinkage algorithms for MMSE covariance estimation. - IEEE Transactions on Signal Processing, 58(10), 5016–5029. https://doi.org/10.1109/TSP.2010.2053029 + [4] Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O. (2010). Shrinkage algorithms for MMSE covariance + estimation. IEEE Transactions on Signal Processing, 58(10), 5016–5029. + https://doi.org/10.1109/TSP.2010.2053029 [5] https://www.econ.uzh.ch/dam/jcr:ffffffff-935a-b0d6-0000-00007f64e5b9/cov1para.m.zip [6] https://www.econ.uzh.ch/dam/jcr:ffffffff-935a-b0d6-ffff-ffffde5e2d4e/covCor.m.zip [7] https://www.econ.uzh.ch/dam/jcr:ffffffff-935a-b0d6-0000-0000648dfc98/covMarket.m.zip From 164687d54bfc3ea454eb72e060de16c6dc4a43c1 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 22 Feb 2021 10:13:08 +0800 Subject: [PATCH 018/134] Add scikit-learn to dependencies. --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index f759945fd..6582054b9 100644 --- a/setup.py +++ b/setup.py @@ -55,6 +55,7 @@ REQUIRED = [ "tornado", "joblib>=0.17.0", "ruamel.yaml>=0.16.12", + "scikit-learn>=0.22" ] # Numpy include From b8647c13c78842d8ceb20ffb3788cc034cba6041 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 22 Feb 2021 10:20:51 +0800 Subject: [PATCH 019/134] Reformat code to follow PEP 8. --- qlib/contrib/strategy/strategy.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/qlib/contrib/strategy/strategy.py b/qlib/contrib/strategy/strategy.py index 74df39f3e..550ff649d 100644 --- a/qlib/contrib/strategy/strategy.py +++ b/qlib/contrib/strategy/strategy.py @@ -7,7 +7,6 @@ import numpy as np import pandas as pd from ..backtest.order import Order -from ...utils import get_pre_trading_date from .order_generator import OrderGenWInteract @@ -390,11 +389,11 @@ class TopkDropoutStrategy(BaseStrategy, ListAdjustTimer): current_stock_list = current_temp.get_stock_list() value = cash * self.risk_degree / len(buy) if len(buy) > 0 else 0 - # open_cost should be considered in the real trading environment, while the backtest in evaluate.py does not consider it - # as the aim of demo is to accomplish same strategy as evaluate.py, so comment out this line + # open_cost should be considered in the real trading environment, while the backtest in evaluate.py does not + # consider it as the aim of demo is to accomplish same strategy as evaluate.py, so comment out this line # value = value / (1+trade_exchange.open_cost) # set open_cost limit for code in buy: - # check is stock supended + # check is stock suspended if not trade_exchange.is_stock_tradable(stock_id=code, trade_date=trade_date): continue # buy order From 2f9d45e03ac429d56ab2356e104089c8544316a3 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 22 Feb 2021 10:29:29 +0800 Subject: [PATCH 020/134] Reformat code with black. --- docs/conf.py | 10 +-- examples/benchmarks/TFT/libs/tft_model.py | 12 +-- examples/highfreq/highfreq_handler.py | 33 ++------ examples/highfreq/highfreq_processor.py | 4 +- examples/highfreq/workflow.py | 35 ++------- examples/run_all_model.py | 5 +- examples/workflow_by_code.py | 5 +- qlib/config.py | 24 +----- qlib/contrib/backtest/__init__.py | 18 +---- qlib/contrib/backtest/profit_attribution.py | 23 +----- qlib/contrib/data/handler.py | 10 +-- qlib/contrib/eva/alpha.py | 6 +- qlib/contrib/evaluate.py | 7 +- qlib/contrib/evaluate_portfolio.py | 16 +--- qlib/contrib/model/catboost_model.py | 4 +- qlib/contrib/model/pytorch_alstm.py | 21 ++--- qlib/contrib/model/pytorch_alstm_ts.py | 17 +---- qlib/contrib/model/pytorch_gats.py | 22 +----- qlib/contrib/model/pytorch_gats_ts.py | 18 +---- qlib/contrib/model/pytorch_gru.py | 21 +---- qlib/contrib/model/pytorch_gru_ts.py | 17 +---- qlib/contrib/model/pytorch_lstm.py | 21 +---- qlib/contrib/model/pytorch_lstm_ts.py | 17 +---- qlib/contrib/model/pytorch_nn.py | 6 +- qlib/contrib/model/pytorch_sfm.py | 19 +---- qlib/contrib/model/pytorch_tabnet.py | 14 +--- qlib/contrib/model/xgboost.py | 4 +- qlib/contrib/online/executor.py | 24 +----- qlib/contrib/online/manager.py | 6 +- qlib/contrib/online/operator.py | 8 +- qlib/contrib/online/utils.py | 6 +- .../analysis_model_performance.py | 66 +++------------- .../analysis_position/cumulative_return.py | 36 ++------- .../analysis_position/parse_position.py | 5 +- .../report/analysis_position/rank_label.py | 16 +--- .../report/analysis_position/report.py | 15 +--- qlib/contrib/report/graph.py | 6 +- qlib/contrib/strategy/cost_control.py | 5 +- qlib/contrib/strategy/order_generator.py | 12 +-- qlib/contrib/tuner/launcher.py | 6 +- qlib/contrib/tuner/space.py | 5 +- qlib/contrib/tuner/tuner.py | 26 ++----- qlib/data/client.py | 3 +- qlib/data/data.py | 69 ++--------------- qlib/data/dataset/utils.py | 5 +- qlib/data/filter.py | 7 +- qlib/model/riskmodel.py | 31 +++++--- qlib/portfolio/optimizer.py | 76 ++++++++++++------- qlib/tests/__init__.py | 6 +- qlib/workflow/record_temp.py | 5 +- scripts/data_collector/yahoo/collector.py | 27 ++----- scripts/dump_bin.py | 13 +--- setup.py | 16 +--- tests/test_all_pipeline.py | 9 +-- tests/test_dump_data.py | 9 +-- tests/test_get_data.py | 4 +- 56 files changed, 218 insertions(+), 713 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 6e52b0e34..61fe784e7 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -191,15 +191,7 @@ man_pages = [(master_doc, "qlib", u"QLib Documentation", [author], 1)] # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ - ( - master_doc, - "QLib", - u"QLib Documentation", - author, - "QLib", - "One line description of project.", - "Miscellaneous", - ), + (master_doc, "QLib", u"QLib Documentation", author, "QLib", "One line description of project.", "Miscellaneous",), ] diff --git a/examples/benchmarks/TFT/libs/tft_model.py b/examples/benchmarks/TFT/libs/tft_model.py index b39f17825..f40a1aece 100644 --- a/examples/benchmarks/TFT/libs/tft_model.py +++ b/examples/benchmarks/TFT/libs/tft_model.py @@ -721,12 +721,7 @@ class TemporalFusionTransformer: encoder_steps = self.num_encoder_steps # Inputs. - all_inputs = tf.keras.layers.Input( - shape=( - time_steps, - combined_input_size, - ) - ) + all_inputs = tf.keras.layers.Input(shape=(time_steps, combined_input_size,)) unknown_inputs, known_combined_layer, obs_inputs, static_inputs = self.get_tft_embeddings(all_inputs) @@ -866,10 +861,7 @@ class TemporalFusionTransformer: """Returns LSTM cell initialized with default parameters.""" if self.use_cudnn: lstm = tf.keras.layers.CuDNNLSTM( - self.hidden_layer_size, - return_sequences=True, - return_state=return_state, - stateful=False, + self.hidden_layer_size, return_sequences=True, return_state=return_state, stateful=False, ) else: lstm = tf.keras.layers.LSTM( diff --git a/examples/highfreq/highfreq_handler.py b/examples/highfreq/highfreq_handler.py index d35650514..2fc411ab6 100644 --- a/examples/highfreq/highfreq_handler.py +++ b/examples/highfreq/highfreq_handler.py @@ -20,10 +20,7 @@ class HighFreqHandler(DataHandlerLP): new_l = [] for p in proc_l: p["kwargs"].update( - { - "fit_start_time": fit_start_time, - "fit_end_time": fit_end_time, - } + {"fit_start_time": fit_start_time, "fit_end_time": fit_end_time,} ) new_l.append(p) return new_l @@ -33,11 +30,7 @@ class HighFreqHandler(DataHandlerLP): data_loader = { "class": "QlibDataLoader", - "kwargs": { - "config": self.get_feature_config(), - "swap_level": False, - "freq": "1min", - }, + "kwargs": {"config": self.get_feature_config(), "swap_level": False, "freq": "1min",}, } super().__init__( instruments=instruments, @@ -68,8 +61,7 @@ class HighFreqHandler(DataHandlerLP): feature_ops = template_norm.format( template_if.format( - template_fillnan.format(template_paused.format("$close")), - template_paused.format(price_field), + template_fillnan.format(template_paused.format("$close")), template_paused.format(price_field), ), template_fillnan.format(template_paused.format("$close")), ) @@ -119,24 +111,14 @@ class HighFreqHandler(DataHandlerLP): class HighFreqBacktestHandler(DataHandler): def __init__( - self, - instruments="csi300", - start_time=None, - end_time=None, + self, instruments="csi300", start_time=None, end_time=None, ): data_loader = { "class": "QlibDataLoader", - "kwargs": { - "config": self.get_feature_config(), - "swap_level": False, - "freq": "1min", - }, + "kwargs": {"config": self.get_feature_config(), "swap_level": False, "freq": "1min",}, } super().__init__( - instruments=instruments, - start_time=start_time, - end_time=end_time, - data_loader=data_loader, + instruments=instruments, start_time=start_time, end_time=end_time, data_loader=data_loader, ) def get_feature_config(self): @@ -155,8 +137,7 @@ class HighFreqBacktestHandler(DataHandler): fields += [ "Cut({0}, 240, None)".format( template_if.format( - template_fillnan.format(template_paused.format("$close")), - template_paused.format(simpson_vwap), + template_fillnan.format(template_paused.format("$close")), template_paused.format(simpson_vwap), ) ) ] diff --git a/examples/highfreq/highfreq_processor.py b/examples/highfreq/highfreq_processor.py index f0ab0dec2..73510ef06 100644 --- a/examples/highfreq/highfreq_processor.py +++ b/examples/highfreq/highfreq_processor.py @@ -65,8 +65,6 @@ class HighFreqNorm(Processor): feat = df_values[:, [0, 1, 2, 3, 4, 10]].reshape(-1, 6 * 240) feat_1 = df_values[:, [5, 6, 7, 8, 9, 11]].reshape(-1, 6 * 240) df_new_features = pd.DataFrame( - data=np.concatenate((feat, feat_1), axis=1), - index=idx, - columns=["FEATURE_%d" % i for i in range(12 * 240)], + data=np.concatenate((feat, feat_1), axis=1), index=idx, columns=["FEATURE_%d" % i for i in range(12 * 240)], ).sort_index() return df_new_features diff --git a/examples/highfreq/workflow.py b/examples/highfreq/workflow.py index 01de59c0e..0bfd0c2a0 100644 --- a/examples/highfreq/workflow.py +++ b/examples/highfreq/workflow.py @@ -63,13 +63,7 @@ class HighfreqWorkflow(object): "module_path": "highfreq_handler", "kwargs": DATA_HANDLER_CONFIG0, }, - "segments": { - "train": (start_time, train_end_time), - "test": ( - test_start_time, - end_time, - ), - }, + "segments": {"train": (start_time, train_end_time), "test": (test_start_time, end_time,),}, }, }, "dataset_backtest": { @@ -81,13 +75,7 @@ class HighfreqWorkflow(object): "module_path": "highfreq_handler", "kwargs": DATA_HANDLER_CONFIG1, }, - "segments": { - "train": (start_time, train_end_time), - "test": ( - test_start_time, - end_time, - ), - }, + "segments": {"train": (start_time, train_end_time), "test": (test_start_time, end_time,),}, }, }, } @@ -152,24 +140,11 @@ class HighfreqWorkflow(object): "start_time": "2021-01-19 00:00:00", "end_time": "2021-01-25 16:00:00", }, - segment_kwargs={ - "test": ( - "2021-01-19 00:00:00", - "2021-01-25 16:00:00", - ), - }, + segment_kwargs={"test": ("2021-01-19 00:00:00", "2021-01-25 16:00:00",),}, ) dataset_backtest.init( - handler_kwargs={ - "start_time": "2021-01-19 00:00:00", - "end_time": "2021-01-25 16:00:00", - }, - segment_kwargs={ - "test": ( - "2021-01-19 00:00:00", - "2021-01-25 16:00:00", - ), - }, + handler_kwargs={"start_time": "2021-01-19 00:00:00", "end_time": "2021-01-25 16:00:00",}, + segment_kwargs={"test": ("2021-01-19 00:00:00", "2021-01-25 16:00:00",),}, ) ##=============get data============= diff --git a/examples/run_all_model.py b/examples/run_all_model.py index d587eff15..d356b4128 100644 --- a/examples/run_all_model.py +++ b/examples/run_all_model.py @@ -34,10 +34,7 @@ exp_path = str(Path(os.getcwd()).resolve() / exp_folder_name) exp_manager = { "class": "MLflowExpManager", "module_path": "qlib.workflow.expm", - "kwargs": { - "uri": "file:" + exp_path, - "default_exp_name": "Experiment", - }, + "kwargs": {"uri": "file:" + exp_path, "default_exp_name": "Experiment",}, } if not exists_qlib_data(provider_uri): print(f"Qlib data is not found in {provider_uri}") diff --git a/examples/workflow_by_code.py b/examples/workflow_by_code.py index d5dab8917..6f5c11dc0 100644 --- a/examples/workflow_by_code.py +++ b/examples/workflow_by_code.py @@ -81,10 +81,7 @@ if __name__ == "__main__": "strategy": { "class": "TopkDropoutStrategy", "module_path": "qlib.contrib.strategy.strategy", - "kwargs": { - "topk": 50, - "n_drop": 5, - }, + "kwargs": {"topk": 50, "n_drop": 5,}, }, "backtest": { "verbose": False, diff --git a/qlib/config.py b/qlib/config.py index 52b05568d..344eb8527 100644 --- a/qlib/config.py +++ b/qlib/config.py @@ -115,12 +115,7 @@ _default_config = { "format": "[%(process)s:%(threadName)s](%(asctime)s) %(levelname)s - %(name)s - [%(filename)s:%(lineno)d] - %(message)s" } }, - "filters": { - "field_not_found": { - "()": "qlib.log.LogFilter", - "param": [".*?WARN: data not found for.*?"], - } - }, + "filters": {"field_not_found": {"()": "qlib.log.LogFilter", "param": [".*?WARN: data not found for.*?"],}}, "handlers": { "console": { "class": "logging.StreamHandler", @@ -135,10 +130,7 @@ _default_config = { "exp_manager": { "class": "MLflowExpManager", "module_path": "qlib.workflow.expm", - "kwargs": { - "uri": "file:" + str(Path(os.getcwd()).resolve() / "mlruns"), - "default_exp_name": "Experiment", - }, + "kwargs": {"uri": "file:" + str(Path(os.getcwd()).resolve() / "mlruns"), "default_exp_name": "Experiment",}, }, } @@ -200,16 +192,8 @@ HIGH_FREQ_CONFIG = { } _default_region_config = { - REG_CN: { - "trade_unit": 100, - "limit_threshold": 0.099, - "deal_price": "vwap", - }, - REG_US: { - "trade_unit": 1, - "limit_threshold": None, - "deal_price": "close", - }, + REG_CN: {"trade_unit": 100, "limit_threshold": 0.099, "deal_price": "vwap",}, + REG_US: {"trade_unit": 1, "limit_threshold": None, "deal_price": "close",}, } diff --git a/qlib/contrib/backtest/__init__.py b/qlib/contrib/backtest/__init__.py index aa24ffb0c..bd3494abf 100644 --- a/qlib/contrib/backtest/__init__.py +++ b/qlib/contrib/backtest/__init__.py @@ -18,13 +18,7 @@ logger = get_module_logger("backtest caller") def get_strategy( - strategy=None, - topk=50, - margin=0.5, - n_drop=5, - risk_degree=0.95, - str_type="dropout", - adjust_dates=None, + strategy=None, topk=50, margin=0.5, n_drop=5, risk_degree=0.95, str_type="dropout", adjust_dates=None, ): """get_strategy @@ -75,11 +69,7 @@ def get_strategy( str_cls = getattr(strategy_pool, str_cls_dict.get(str_type)) strategy = str_cls( - topk=topk, - buffer_margin=margin, - n_drop=n_drop, - risk_degree=risk_degree, - adjust_dates=adjust_dates, + topk=topk, buffer_margin=margin, n_drop=n_drop, risk_degree=risk_degree, adjust_dates=adjust_dates, ) elif isinstance(strategy, (dict, str)): # 2) create strategy with init_instance_by_config @@ -172,9 +162,7 @@ def get_exchange( def get_executor( - executor=None, - trade_exchange=None, - verbose=True, + executor=None, trade_exchange=None, verbose=True, ): """get_executor diff --git a/qlib/contrib/backtest/profit_attribution.py b/qlib/contrib/backtest/profit_attribution.py index 20c6f638f..355f06373 100644 --- a/qlib/contrib/backtest/profit_attribution.py +++ b/qlib/contrib/backtest/profit_attribution.py @@ -12,10 +12,7 @@ from pathlib import Path def get_benchmark_weight( - bench, - start_date=None, - end_date=None, - path=None, + bench, start_date=None, end_date=None, path=None, ): """get_benchmark_weight @@ -216,12 +213,7 @@ def get_stock_group(stock_group_field_df, bench_stock_weight_df, group_method, g def brinson_pa( - positions, - bench="SH000905", - group_field="industry", - group_method="category", - group_n=None, - deal_price="vwap", + positions, bench="SH000905", group_field="industry", group_method="category", group_n=None, deal_price="vwap", ): """brinson profit attribution @@ -255,17 +247,10 @@ def brinson_pa( # suspend stock is NAN. So we have to get more date to forward fill the NAN shift_start_date = start_date - datetime.timedelta(days=250) instruments = D.list_instruments( - D.instruments(market="all"), - start_time=shift_start_date, - end_time=end_date, - as_list=True, + D.instruments(market="all"), start_time=shift_start_date, end_time=end_date, as_list=True, ) stock_df = D.features( - instruments, - [group_field, deal_price], - start_time=shift_start_date, - end_time=end_date, - freq="day", + instruments, [group_field, deal_price], start_time=shift_start_date, end_time=end_date, freq="day", ) stock_df.columns = [group_field, "deal_price"] diff --git a/qlib/contrib/data/handler.py b/qlib/contrib/data/handler.py index 970b032d6..574287819 100644 --- a/qlib/contrib/data/handler.py +++ b/qlib/contrib/data/handler.py @@ -21,10 +21,7 @@ def check_transform_proc(proc_l, fit_start_time, fit_end_time): fit_start_time is not None and fit_end_time is not None ), "Make sure `fit_start_time` and `fit_end_time` are not None." pkwargs.update( - { - "fit_start_time": fit_start_time, - "fit_end_time": fit_end_time, - } + {"fit_start_time": fit_start_time, "fit_end_time": fit_end_time,} ) new_l.append({"class": klass.__name__, "kwargs": pkwargs}) else: @@ -170,10 +167,7 @@ class Alpha158(DataHandlerLP): def get_feature_config(self): conf = { "kbar": {}, - "price": { - "windows": [0], - "feature": ["OPEN", "HIGH", "LOW", "VWAP"], - }, + "price": {"windows": [0], "feature": ["OPEN", "HIGH", "LOW", "VWAP"],}, "rolling": {}, } return self.parse_config_to_fields(conf) diff --git a/qlib/contrib/eva/alpha.py b/qlib/contrib/eva/alpha.py index c68571853..363a18458 100644 --- a/qlib/contrib/eva/alpha.py +++ b/qlib/contrib/eva/alpha.py @@ -35,11 +35,7 @@ def calc_ic(pred: pd.Series, label: pd.Series, date_col="datetime", dropna=False def calc_long_short_return( - pred: pd.Series, - label: pd.Series, - date_col: str = "datetime", - quantile: float = 0.2, - dropna: bool = False, + pred: pd.Series, label: pd.Series, date_col: str = "datetime", quantile: float = 0.2, dropna: bool = False, ) -> Tuple[pd.Series, pd.Series]: """ calculate long-short return diff --git a/qlib/contrib/evaluate.py b/qlib/contrib/evaluate.py index 4aa5b5515..5cb1ce4eb 100644 --- a/qlib/contrib/evaluate.py +++ b/qlib/contrib/evaluate.py @@ -244,12 +244,7 @@ def long_short_backtest( short_returns[date] = np.mean(short_profit) + np.mean(all_profit) ls_returns[date] = np.mean(short_profit) + np.mean(long_profit) - return dict( - zip( - ["long", "short", "long_short"], - map(pd.Series, [long_returns, short_returns, ls_returns]), - ) - ) + return dict(zip(["long", "short", "long_short"], map(pd.Series, [long_returns, short_returns, ls_returns]),)) def t_run(): diff --git a/qlib/contrib/evaluate_portfolio.py b/qlib/contrib/evaluate_portfolio.py index 04ddd8db0..2d94105e4 100644 --- a/qlib/contrib/evaluate_portfolio.py +++ b/qlib/contrib/evaluate_portfolio.py @@ -64,12 +64,7 @@ def get_position_value(evaluate_date, position): instruments = list(set(instruments) - set(["cash"])) # filter 'cash' fields = ["$close"] close_data_df = D.features( - instruments, - fields, - start_time=evaluate_date, - end_time=evaluate_date, - freq="day", - disk_cache=0, + instruments, fields, start_time=evaluate_date, end_time=evaluate_date, freq="day", disk_cache=0, ) value = _get_position_value_from_df(evaluate_date, position, close_data_df) return value @@ -87,14 +82,7 @@ def get_position_list_value(positions): start_date, end_date = day_list[0], day_list[-1] # load data fields = ["$close"] - close_data_df = D.features( - instruments, - fields, - start_time=start_date, - end_time=end_date, - freq="day", - disk_cache=0, - ) + close_data_df = D.features(instruments, fields, start_time=start_date, end_time=end_date, freq="day", disk_cache=0,) # generate value # return dict for time:position_value value_dict = OrderedDict() diff --git a/qlib/contrib/model/catboost_model.py b/qlib/contrib/model/catboost_model.py index d57c32b70..2840c2cef 100644 --- a/qlib/contrib/model/catboost_model.py +++ b/qlib/contrib/model/catboost_model.py @@ -32,9 +32,7 @@ class CatBoostModel(Model): **kwargs ): df_train, df_valid = dataset.prepare( - ["train", "valid"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] diff --git a/qlib/contrib/model/pytorch_alstm.py b/qlib/contrib/model/pytorch_alstm.py index bbbb61851..306e68aad 100644 --- a/qlib/contrib/model/pytorch_alstm.py +++ b/qlib/contrib/model/pytorch_alstm.py @@ -118,10 +118,7 @@ class ALSTM(Model): torch.manual_seed(self.seed) self.ALSTM_model = ALSTMModel( - d_feat=self.d_feat, - hidden_size=self.hidden_size, - num_layers=self.num_layers, - dropout=self.dropout, + d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, ) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.ALSTM_model.parameters(), lr=self.lr) @@ -211,17 +208,11 @@ class ALSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset: DatasetH, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -328,14 +319,12 @@ class ALSTMModel(nn.Module): self.fc_out = nn.Linear(in_features=self.hid_size * 2, out_features=1) self.att_net = nn.Sequential() self.att_net.add_module( - "att_fc_in", - nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), + "att_fc_in", nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), ) self.att_net.add_module("att_dropout", torch.nn.Dropout(self.dropout)) self.att_net.add_module("att_act", nn.Tanh()) self.att_net.add_module( - "att_fc_out", - nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), + "att_fc_out", nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), ) self.att_net.add_module("att_softmax", nn.Softmax(dim=1)) diff --git a/qlib/contrib/model/pytorch_alstm_ts.py b/qlib/contrib/model/pytorch_alstm_ts.py index 725568de8..612bacbec 100644 --- a/qlib/contrib/model/pytorch_alstm_ts.py +++ b/qlib/contrib/model/pytorch_alstm_ts.py @@ -123,10 +123,7 @@ class ALSTM(Model): torch.manual_seed(self.seed) self.ALSTM_model = ALSTMModel( - d_feat=self.d_feat, - hidden_size=self.hidden_size, - num_layers=self.num_layers, - dropout=self.dropout, + d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, ).to(self.device) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.ALSTM_model.parameters(), lr=self.lr) @@ -198,11 +195,7 @@ class ALSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset, evals_result=dict(), verbose=True, save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -309,14 +302,12 @@ class ALSTMModel(nn.Module): self.fc_out = nn.Linear(in_features=self.hid_size * 2, out_features=1) self.att_net = nn.Sequential() self.att_net.add_module( - "att_fc_in", - nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), + "att_fc_in", nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), ) self.att_net.add_module("att_dropout", torch.nn.Dropout(self.dropout)) self.att_net.add_module("att_act", nn.Tanh()) self.att_net.add_module( - "att_fc_out", - nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), + "att_fc_out", nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), ) self.att_net.add_module("att_softmax", nn.Softmax(dim=1)) diff --git a/qlib/contrib/model/pytorch_gats.py b/qlib/contrib/model/pytorch_gats.py index 07048e1bc..c59dc9197 100644 --- a/qlib/contrib/model/pytorch_gats.py +++ b/qlib/contrib/model/pytorch_gats.py @@ -229,17 +229,11 @@ class GATs(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset: DatasetH, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -340,19 +334,11 @@ class GATModel(nn.Module): if base_model == "GRU": self.rnn = nn.GRU( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) elif base_model == "LSTM": self.rnn = nn.LSTM( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) else: raise ValueError("unknown base model name `%s`" % base_model) diff --git a/qlib/contrib/model/pytorch_gats_ts.py b/qlib/contrib/model/pytorch_gats_ts.py index 1e94f56e4..dfc5f4ab5 100644 --- a/qlib/contrib/model/pytorch_gats_ts.py +++ b/qlib/contrib/model/pytorch_gats_ts.py @@ -242,11 +242,7 @@ class GATs(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset, evals_result=dict(), verbose=True, save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -361,19 +357,11 @@ class GATModel(nn.Module): if base_model == "GRU": self.rnn = nn.GRU( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) elif base_model == "LSTM": self.rnn = nn.LSTM( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) else: raise ValueError("unknown base model name `%s`" % base_model) diff --git a/qlib/contrib/model/pytorch_gru.py b/qlib/contrib/model/pytorch_gru.py index 84f863b9f..d2a774b65 100755 --- a/qlib/contrib/model/pytorch_gru.py +++ b/qlib/contrib/model/pytorch_gru.py @@ -118,10 +118,7 @@ class GRU(Model): torch.manual_seed(self.seed) self.gru_model = GRUModel( - d_feat=self.d_feat, - hidden_size=self.hidden_size, - num_layers=self.num_layers, - dropout=self.dropout, + d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, ) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.gru_model.parameters(), lr=self.lr) @@ -211,17 +208,11 @@ class GRU(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset: DatasetH, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -305,11 +296,7 @@ class GRUModel(nn.Module): super().__init__() self.rnn = nn.GRU( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_gru_ts.py b/qlib/contrib/model/pytorch_gru_ts.py index bb6618b85..49f438cc3 100755 --- a/qlib/contrib/model/pytorch_gru_ts.py +++ b/qlib/contrib/model/pytorch_gru_ts.py @@ -123,10 +123,7 @@ class GRU(Model): torch.manual_seed(self.seed) self.GRU_model = GRUModel( - d_feat=self.d_feat, - hidden_size=self.hidden_size, - num_layers=self.num_layers, - dropout=self.dropout, + d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, ).to(self.device) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.GRU_model.parameters(), lr=self.lr) @@ -198,11 +195,7 @@ class GRU(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset, evals_result=dict(), verbose=True, save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -286,11 +279,7 @@ class GRUModel(nn.Module): super().__init__() self.rnn = nn.GRU( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_lstm.py b/qlib/contrib/model/pytorch_lstm.py index 163d500ec..02ca16e36 100755 --- a/qlib/contrib/model/pytorch_lstm.py +++ b/qlib/contrib/model/pytorch_lstm.py @@ -118,10 +118,7 @@ class LSTM(Model): torch.manual_seed(self.seed) self.lstm_model = LSTMModel( - d_feat=self.d_feat, - hidden_size=self.hidden_size, - num_layers=self.num_layers, - dropout=self.dropout, + d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, ) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.lstm_model.parameters(), lr=self.lr) @@ -211,17 +208,11 @@ class LSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset: DatasetH, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -305,11 +296,7 @@ class LSTMModel(nn.Module): super().__init__() self.rnn = nn.LSTM( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_lstm_ts.py b/qlib/contrib/model/pytorch_lstm_ts.py index cf4f8fb9f..2ec36f96e 100755 --- a/qlib/contrib/model/pytorch_lstm_ts.py +++ b/qlib/contrib/model/pytorch_lstm_ts.py @@ -123,10 +123,7 @@ class LSTM(Model): torch.manual_seed(self.seed) self.LSTM_model = LSTMModel( - d_feat=self.d_feat, - hidden_size=self.hidden_size, - num_layers=self.num_layers, - dropout=self.dropout, + d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, ).to(self.device) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.LSTM_model.parameters(), lr=self.lr) @@ -198,11 +195,7 @@ class LSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset, evals_result=dict(), verbose=True, save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -286,11 +279,7 @@ class LSTMModel(nn.Module): super().__init__() self.rnn = nn.LSTM( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_nn.py b/qlib/contrib/model/pytorch_nn.py index 16fcea9ff..8c1a77ec3 100644 --- a/qlib/contrib/model/pytorch_nn.py +++ b/qlib/contrib/model/pytorch_nn.py @@ -154,11 +154,7 @@ class DNNModelPytorch(Model): self.dnn_model.to(self.device) def fit( - self, - dataset: DatasetH, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, ): df_train, df_valid = dataset.prepare( ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L diff --git a/qlib/contrib/model/pytorch_sfm.py b/qlib/contrib/model/pytorch_sfm.py index d5169e6c7..1f7433e05 100644 --- a/qlib/contrib/model/pytorch_sfm.py +++ b/qlib/contrib/model/pytorch_sfm.py @@ -30,14 +30,7 @@ from ...data.dataset.handler import DataHandlerLP class SFM_Model(nn.Module): def __init__( - self, - d_feat=6, - output_dim=1, - freq_dim=10, - hidden_size=64, - dropout_W=0.0, - dropout_U=0.0, - device="cpu", + self, d_feat=6, output_dim=1, freq_dim=10, hidden_size=64, dropout_W=0.0, dropout_U=0.0, device="cpu", ): super().__init__() @@ -362,17 +355,11 @@ class SFM(Model): self.train_optimizer.step() def fit( - self, - dataset: DatasetH, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, ): df_train, df_valid = dataset.prepare( - ["train", "valid"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] diff --git a/qlib/contrib/model/pytorch_tabnet.py b/qlib/contrib/model/pytorch_tabnet.py index 62e32d701..18e9d8eb4 100644 --- a/qlib/contrib/model/pytorch_tabnet.py +++ b/qlib/contrib/model/pytorch_tabnet.py @@ -120,9 +120,7 @@ class TabnetModel(Model): os.makedirs("pretrain") [df_train, df_valid] = dataset.prepare( - ["pretrain", "pretrain_validation"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["pretrain", "pretrain_validation"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) df_train.fillna(df_train.mean(), inplace=True) @@ -156,11 +154,7 @@ class TabnetModel(Model): break def fit( - self, - dataset: DatasetH, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, ): if self.pretrain: # there is a pretrained model, load the model @@ -172,9 +166,7 @@ class TabnetModel(Model): # adding one more linear layer to fit the final output dimension self.tabnet_model = FinetuneModel(self.out_dim, self.final_out_dim, self.tabnet_model).to(self.device) df_train, df_valid = dataset.prepare( - ["train", "valid"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) df_train.fillna(df_train.mean(), inplace=True) x_train, y_train = df_train["feature"], df_train["label"] diff --git a/qlib/contrib/model/xgboost.py b/qlib/contrib/model/xgboost.py index ba2e5789b..e37725c2e 100755 --- a/qlib/contrib/model/xgboost.py +++ b/qlib/contrib/model/xgboost.py @@ -29,9 +29,7 @@ class XGBModel(Model): ): df_train, df_valid = dataset.prepare( - ["train", "valid"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] diff --git a/qlib/contrib/online/executor.py b/qlib/contrib/online/executor.py index 2bd0937a0..52b868881 100644 --- a/qlib/contrib/online/executor.py +++ b/qlib/contrib/online/executor.py @@ -150,21 +150,13 @@ class SimulatorExecutor(BaseExecutor): if order.direction == Order.SELL: # sell print( "[I {:%Y-%m-%d}]: sell {}, price {:.2f}, amount {}, value {:.2f}.".format( - trade_date, - order.stock_id, - trade_price, - order.deal_amount, - trade_val, + trade_date, order.stock_id, trade_price, order.deal_amount, trade_val, ) ) else: print( "[I {:%Y-%m-%d}]: buy {}, price {:.2f}, amount {}, value {:.2f}.".format( - trade_date, - order.stock_id, - trade_price, - order.deal_amount, - trade_val, + trade_date, order.stock_id, trade_price, order.deal_amount, trade_val, ) ) @@ -271,21 +263,13 @@ def load_order_list(user_path, trade_date): for stock_id in order_dict["sell"]: amount, factor = order_dict["sell"][stock_id] order = Order( - stock_id=stock_id, - amount=amount, - trade_date=pd.Timestamp(trade_date), - direction=Order.SELL, - factor=factor, + stock_id=stock_id, amount=amount, trade_date=pd.Timestamp(trade_date), direction=Order.SELL, factor=factor, ) order_list.append(order) for stock_id in order_dict["buy"]: amount, factor = order_dict["buy"][stock_id] order = Order( - stock_id=stock_id, - amount=amount, - trade_date=pd.Timestamp(trade_date), - direction=Order.BUY, - factor=factor, + stock_id=stock_id, amount=amount, trade_date=pd.Timestamp(trade_date), direction=Order.BUY, factor=factor, ) order_list.append(order) return order_list diff --git a/qlib/contrib/online/manager.py b/qlib/contrib/online/manager.py index cf850b9da..a4476709d 100644 --- a/qlib/contrib/online/manager.py +++ b/qlib/contrib/online/manager.py @@ -84,12 +84,10 @@ class UserManager: raise ValueError("Cannot find user {}".format(user_id)) self.users[user_id].account.save_account(self.data_path / user_id) save_instance( - self.users[user_id].strategy, - self.data_path / user_id / "strategy_{}.pickle".format(user_id), + self.users[user_id].strategy, self.data_path / user_id / "strategy_{}.pickle".format(user_id), ) save_instance( - self.users[user_id].model, - self.data_path / user_id / "model_{}.pickle".format(user_id), + self.users[user_id].model, self.data_path / user_id / "model_{}.pickle".format(user_id), ) def add_user(self, user_id, config_file, add_date): diff --git a/qlib/contrib/online/operator.py b/qlib/contrib/online/operator.py index c8b44f578..c82deb394 100644 --- a/qlib/contrib/online/operator.py +++ b/qlib/contrib/online/operator.py @@ -125,9 +125,7 @@ class Operator: trade_date=trade_date, ) save_order_list( - order_list=order_list, - user_path=(pathlib.Path(path) / user_id), - trade_date=trade_date, + order_list=order_list, user_path=(pathlib.Path(path) / user_id), trade_date=trade_date, ) self.logger.info("Generate order list at {} for {}".format(trade_date, user_id)) um.save_user_data(user_id) @@ -160,9 +158,7 @@ class Operator: order_list = load_order_list(user_path=(pathlib.Path(path) / user_id), trade_date=trade_date) trade_info = executor.execute(order_list=order_list, trade_account=user.account, trade_date=trade_date) executor.save_executed_file_from_trade_info( - trade_info=trade_info, - user_path=(pathlib.Path(path) / user_id), - trade_date=trade_date, + trade_info=trade_info, user_path=(pathlib.Path(path) / user_id), trade_date=trade_date, ) self.logger.info("execute order list at {} for {}".format(trade_date.date(), user_id)) diff --git a/qlib/contrib/online/utils.py b/qlib/contrib/online/utils.py index 611af63e4..fb96c87bd 100644 --- a/qlib/contrib/online/utils.py +++ b/qlib/contrib/online/utils.py @@ -79,11 +79,7 @@ def prepare(um, today, user_id, exchange_config=None): log.warning("user_id:{}, last trading date {} after today {}".format(user_id, latest_trading_date, today)) return [pd.Timestamp(latest_trading_date)], None - dates = D.calendar( - start_time=pd.Timestamp(latest_trading_date), - end_time=pd.Timestamp(today), - future=True, - ) + dates = D.calendar(start_time=pd.Timestamp(latest_trading_date), end_time=pd.Timestamp(today), future=True,) dates = list(dates) dates.append(get_next_trading_date(dates[-1], future=True)) if exchange_config: diff --git a/qlib/contrib/report/analysis_model/analysis_model_performance.py b/qlib/contrib/report/analysis_model/analysis_model_performance.py index 1cb14d261..ef1447a12 100644 --- a/qlib/contrib/report/analysis_model/analysis_model_performance.py +++ b/qlib/contrib/report/analysis_model/analysis_model_performance.py @@ -53,8 +53,7 @@ def _group_return(pred_label: pd.DataFrame = None, reverse: bool = False, N: int t_df.index = t_df.index.strftime("%Y-%m-%d") # Cumulative Return By Group group_scatter_figure = ScatterGraph( - t_df.cumsum(), - layout=dict(title="Cumulative Return", xaxis=dict(type="category", tickangle=45)), + t_df.cumsum(), layout=dict(title="Cumulative Return", xaxis=dict(type="category", tickangle=45)), ).figure t_df = t_df.loc[:, ["long-short", "long-average"]] @@ -62,12 +61,7 @@ def _group_return(pred_label: pd.DataFrame = None, reverse: bool = False, N: int group_hist_figure = SubplotsGraph( t_df, kind_map=dict(kind="DistplotGraph", kwargs=dict(bin_size=_bin_size)), - subplots_kwargs=dict( - rows=1, - cols=2, - print_grid=False, - subplot_titles=["long-short", "long-average"], - ), + subplots_kwargs=dict(rows=1, cols=2, print_grid=False, subplot_titles=["long-short", "long-average"],), ).figure return group_scatter_figure, group_hist_figure @@ -102,15 +96,12 @@ def _pred_ic(pred_label: pd.DataFrame = None, rank: bool = False, **kwargs) -> t _index = ic.index.get_level_values(0).astype("str").str.replace("-", "").str.slice(0, 6) _monthly_ic = ic.groupby(_index).mean() _monthly_ic.index = pd.MultiIndex.from_arrays( - [_monthly_ic.index.str.slice(0, 4), _monthly_ic.index.str.slice(4, 6)], - names=["year", "month"], + [_monthly_ic.index.str.slice(0, 4), _monthly_ic.index.str.slice(4, 6)], names=["year", "month"], ) # fill month _month_list = pd.date_range( - start=pd.Timestamp(f"{_index.min()[:4]}0101"), - end=pd.Timestamp(f"{_index.max()[:4]}1231"), - freq="1M", + start=pd.Timestamp(f"{_index.min()[:4]}0101"), end=pd.Timestamp(f"{_index.max()[:4]}1231"), freq="1M", ) _years = [] _month = [] @@ -142,32 +133,15 @@ def _pred_ic(pred_label: pd.DataFrame = None, rank: bool = False, **kwargs) -> t _bin_size = ((_ic_df.max() - _ic_df.min()) / 20).min() _sub_graph_data = [ - ( - "ic", - dict( - row=1, - col=1, - name="", - kind="DistplotGraph", - graph_kwargs=dict(bin_size=_bin_size), - ), - ), + ("ic", dict(row=1, col=1, name="", kind="DistplotGraph", graph_kwargs=dict(bin_size=_bin_size),),), (_qqplot_fig, dict(row=1, col=2)), ] ic_hist_figure = SubplotsGraph( _ic_df.dropna(), kind_map=dict(kind="HistogramGraph", kwargs=dict()), - subplots_kwargs=dict( - rows=1, - cols=2, - print_grid=False, - subplot_titles=["IC", "IC %s Dist. Q-Q" % dist_name], - ), + subplots_kwargs=dict(rows=1, cols=2, print_grid=False, subplot_titles=["IC", "IC %s Dist. Q-Q" % dist_name],), sub_graph_data=_sub_graph_data, - layout=dict( - yaxis2=dict(title="Observed Quantile"), - xaxis2=dict(title=f"{dist_name} Distribution Quantile"), - ), + layout=dict(yaxis2=dict(title="Observed Quantile"), xaxis2=dict(title=f"{dist_name} Distribution Quantile"),), ).figure return ic_bar_figure, ic_heatmap_figure, ic_hist_figure @@ -181,8 +155,7 @@ def _pred_autocorr(pred_label: pd.DataFrame, lag=1, **kwargs) -> tuple: _df = ac.to_frame("value") _df.index = _df.index.strftime("%Y-%m-%d") ac_figure = ScatterGraph( - _df, - layout=dict(title="Auto Correlation", xaxis=dict(type="category", tickangle=45)), + _df, layout=dict(title="Auto Correlation", xaxis=dict(type="category", tickangle=45)), ).figure return (ac_figure,) @@ -202,17 +175,11 @@ def _pred_turnover(pred_label: pd.DataFrame, N=5, lag=1, **kwargs) -> tuple: .sum() / (len(x) // N) ) - r_df = pd.DataFrame( - { - "Top": top, - "Bottom": bottom, - } - ) + r_df = pd.DataFrame({"Top": top, "Bottom": bottom,}) # FIXME: support HIGH-FREQ r_df.index = r_df.index.strftime("%Y-%m-%d") turnover_figure = ScatterGraph( - r_df, - layout=dict(title="Top-Bottom Turnover", xaxis=dict(type="category", tickangle=45)), + r_df, layout=dict(title="Top-Bottom Turnover", xaxis=dict(type="category", tickangle=45)), ).figure return (turnover_figure,) @@ -230,11 +197,7 @@ def ic_figure(ic_df: pd.DataFrame, show_nature_day=True, **kwargs) -> go.Figure: # FIXME: support HIGH-FREQ ic_df.index = ic_df.index.strftime("%Y-%m-%d") ic_bar_figure = BarGraph( - ic_df, - layout=dict( - title="Information Coefficient (IC)", - xaxis=dict(type="category", tickangle=45), - ), + ic_df, layout=dict(title="Information Coefficient (IC)", xaxis=dict(type="category", tickangle=45),), ).figure return ic_bar_figure @@ -277,12 +240,7 @@ def model_performance_graph( figure_list = [] for graph_name in graph_names: fun_res = eval(f"_{graph_name}")( - pred_label=pred_label, - lag=lag, - N=N, - reverse=reverse, - rank=rank, - show_nature_day=show_nature_day, + pred_label=pred_label, lag=lag, N=N, reverse=reverse, rank=rank, show_nature_day=show_nature_day, ) figure_list += fun_res diff --git a/qlib/contrib/report/analysis_position/cumulative_return.py b/qlib/contrib/report/analysis_position/cumulative_return.py index abb68ea60..604189c94 100644 --- a/qlib/contrib/report/analysis_position/cumulative_return.py +++ b/qlib/contrib/report/analysis_position/cumulative_return.py @@ -13,11 +13,7 @@ from ..analysis_position.parse_position import get_position_data def _get_cum_return_data_with_position( - position: dict, - report_normal: pd.DataFrame, - label_data: pd.DataFrame, - start_date=None, - end_date=None, + position: dict, report_normal: pd.DataFrame, label_data: pd.DataFrame, start_date=None, end_date=None, ): """ @@ -29,11 +25,7 @@ def _get_cum_return_data_with_position( :return: """ _cumulative_return_df = get_position_data( - position=position, - report_normal=report_normal, - label_data=label_data, - start_date=start_date, - end_date=end_date, + position=position, report_normal=report_normal, label_data=label_data, start_date=start_date, end_date=end_date, ).copy() _cumulative_return_df["label"] = _cumulative_return_df["label"] - _cumulative_return_df["bench"] @@ -87,11 +79,7 @@ def _get_cum_return_data_with_position( def _get_figure_with_position( - position: dict, - report_normal: pd.DataFrame, - label_data: pd.DataFrame, - start_date=None, - end_date=None, + position: dict, report_normal: pd.DataFrame, label_data: pd.DataFrame, start_date=None, end_date=None, ) -> Iterable[go.Figure]: """Get average analysis figures @@ -111,18 +99,12 @@ def _get_figure_with_position( # Create figures for _t_name in ["buy", "sell", "buy_minus_sell", "hold"]: sub_graph_data = [ - ( - "cum_{}".format(_t_name), - dict(row=1, col=1, graph_kwargs={"mode": "lines+markers", "xaxis": "x3"}), - ), + ("cum_{}".format(_t_name), dict(row=1, col=1, graph_kwargs={"mode": "lines+markers", "xaxis": "x3"}),), ( "{}_weight".format(_t_name.replace("minus", "plus") if "minus" in _t_name else _t_name), dict(row=2, col=1), ), - ( - "{}_value".format(_t_name), - dict(row=1, col=2, kind="HistogramGraph", graph_kwargs={}), - ), + ("{}_value".format(_t_name), dict(row=1, col=2, kind="HistogramGraph", graph_kwargs={}),), ] _default_xaxis = dict(showline=False, zeroline=True, tickangle=45) @@ -161,13 +143,7 @@ def _get_figure_with_position( [{"rowspan": 1}, None], ] subplots_kwargs = dict( - vertical_spacing=0.01, - rows=2, - cols=2, - row_width=[1, 2], - column_width=[3, 1], - print_grid=False, - specs=specs, + vertical_spacing=0.01, rows=2, cols=2, row_width=[1, 2], column_width=[3, 1], print_grid=False, specs=specs, ) yield SubplotsGraph( cum_return_df, diff --git a/qlib/contrib/report/analysis_position/parse_position.py b/qlib/contrib/report/analysis_position/parse_position.py index fe1d61137..23f9c592c 100644 --- a/qlib/contrib/report/analysis_position/parse_position.py +++ b/qlib/contrib/report/analysis_position/parse_position.py @@ -72,10 +72,7 @@ def parse_position(position: dict = None) -> pd.DataFrame: result_df = result_df.append(_trading_day_df, sort=True) - previous_data = dict( - date=_trading_date, - code_list=_trading_day_df[_trading_day_df["status"] != -1].index, - ) + previous_data = dict(date=_trading_date, code_list=_trading_day_df[_trading_day_df["status"] != -1].index,) result_df.reset_index(inplace=True) result_df.rename(columns={"date": "datetime", "index": "instrument"}, inplace=True) diff --git a/qlib/contrib/report/analysis_position/rank_label.py b/qlib/contrib/report/analysis_position/rank_label.py index 72a358adc..9a4d834ed 100644 --- a/qlib/contrib/report/analysis_position/rank_label.py +++ b/qlib/contrib/report/analysis_position/rank_label.py @@ -23,11 +23,7 @@ def _get_figure_with_position( :return: """ _position_df = get_position_data( - position, - label_data, - calculate_label_rank=True, - start_date=start_date, - end_date=end_date, + position, label_data, calculate_label_rank=True, start_date=start_date, end_date=end_date, ) res_dict = dict() @@ -51,20 +47,14 @@ def _get_figure_with_position( yield ScatterGraph( _res_df.loc[:, [_col]], layout=dict( - title=_col, - xaxis=dict(type="category", tickangle=45), - yaxis=dict(title="lable-rank-ratio: %"), + title=_col, xaxis=dict(type="category", tickangle=45), yaxis=dict(title="lable-rank-ratio: %"), ), graph_kwargs=dict(mode="lines+markers"), ).figure def rank_label_graph( - position: dict, - label_data: pd.DataFrame, - start_date=None, - end_date=None, - show_notebook=True, + position: dict, label_data: pd.DataFrame, start_date=None, end_date=None, show_notebook=True, ) -> Iterable[go.Figure]: """Ranking percentage of stocks buy, sell, and holding on the trading day. Average rank-ratio(similar to **sell_df['label'].rank(ascending=False) / len(sell_df)**) of daily trading diff --git a/qlib/contrib/report/analysis_position/report.py b/qlib/contrib/report/analysis_position/report.py index f82e654c4..8e2c05c0a 100644 --- a/qlib/contrib/report/analysis_position/report.py +++ b/qlib/contrib/report/analysis_position/report.py @@ -123,9 +123,7 @@ def _report_figure(df: pd.DataFrame) -> [list, tuple]: "y1": 1, "fillcolor": "#d3d3d3", "opacity": 0.3, - "line": { - "width": 0, - }, + "line": {"width": 0,}, }, { "type": "rect", @@ -137,20 +135,13 @@ def _report_figure(df: pd.DataFrame) -> [list, tuple]: "y1": 0.55, "fillcolor": "#d3d3d3", "opacity": 0.3, - "line": { - "width": 0, - }, + "line": {"width": 0,}, }, ], ) _subplot_kwargs = dict( - shared_xaxes=True, - vertical_spacing=0.01, - rows=7, - cols=1, - row_width=[1, 1, 1, 3, 1, 1, 3], - print_grid=False, + shared_xaxes=True, vertical_spacing=0.01, rows=7, cols=1, row_width=[1, 1, 1, 3, 1, 1, 3], print_grid=False, ) figure = SubplotsGraph( df=report_df, diff --git a/qlib/contrib/report/graph.py b/qlib/contrib/report/graph.py index 70e382fb1..dbbc41110 100644 --- a/qlib/contrib/report/graph.py +++ b/qlib/contrib/report/graph.py @@ -311,11 +311,7 @@ class SubplotsGraph: _temp_row_data = ( column_name, dict( - row=row, - col=col, - name=res_name, - kind=self._kind_map["kind"], - graph_kwargs=self._kind_map["kwargs"], + row=row, col=col, name=res_name, kind=self._kind_map["kind"], graph_kwargs=self._kind_map["kwargs"], ), ) self._sub_graph_data.append(_temp_row_data) diff --git a/qlib/contrib/strategy/cost_control.py b/qlib/contrib/strategy/cost_control.py index dd90437b0..ee3ee03ec 100644 --- a/qlib/contrib/strategy/cost_control.py +++ b/qlib/contrib/strategy/cost_control.py @@ -57,10 +57,7 @@ class SoftTopkStrategy(WeightStrategyBase): final_stock_weight[stock_id] -= sw if self.buy_method == "first_fill": for stock_id in buy_signal_stocks: - add_weight = min( - max(1 / self.topk - final_stock_weight.get(stock_id, 0), 0.0), - sold_stock_weight, - ) + add_weight = min(max(1 / self.topk - final_stock_weight.get(stock_id, 0), 0.0), sold_stock_weight,) final_stock_weight[stock_id] = final_stock_weight.get(stock_id, 0.0) + add_weight sold_stock_weight -= add_weight elif self.buy_method == "average_fill": diff --git a/qlib/contrib/strategy/order_generator.py b/qlib/contrib/strategy/order_generator.py index 494981ecc..6f168b4dd 100644 --- a/qlib/contrib/strategy/order_generator.py +++ b/qlib/contrib/strategy/order_generator.py @@ -102,14 +102,10 @@ class OrderGenWInteract(OrderGenerator): # strategy 1 : generate amount_position by weight_position # Use API in Exchange() target_amount_dict = trade_exchange.generate_amount_position_from_weight_position( - weight_position=target_weight_position, - cash=current_tradable_value, - trade_date=trade_date, + weight_position=target_weight_position, cash=current_tradable_value, trade_date=trade_date, ) order_list = trade_exchange.generate_order_for_target_amount_position( - target_position=target_amount_dict, - current_position=current_amount_dict, - trade_date=trade_date, + target_position=target_amount_dict, current_position=current_amount_dict, trade_date=trade_date, ) return order_list @@ -164,8 +160,6 @@ class OrderGenWOInteract(OrderGenerator): else: continue order_list = trade_exchange.generate_order_for_target_amount_position( - target_position=amount_dict, - current_position=current.get_stock_amount_dict(), - trade_date=trade_date, + target_position=amount_dict, current_position=current.get_stock_amount_dict(), trade_date=trade_date, ) return order_list diff --git a/qlib/contrib/tuner/launcher.py b/qlib/contrib/tuner/launcher.py index 711658c9a..409410a2a 100644 --- a/qlib/contrib/tuner/launcher.py +++ b/qlib/contrib/tuner/launcher.py @@ -13,11 +13,7 @@ from .config import TunerConfigManager args_parser = argparse.ArgumentParser(prog="tuner") args_parser.add_argument( - "-c", - "--config_path", - required=True, - type=str, - help="config path indicates where to load yaml config.", + "-c", "--config_path", required=True, type=str, help="config path indicates where to load yaml config.", ) args = args_parser.parse_args() diff --git a/qlib/contrib/tuner/space.py b/qlib/contrib/tuner/space.py index 76f101671..57f57a6c3 100644 --- a/qlib/contrib/tuner/space.py +++ b/qlib/contrib/tuner/space.py @@ -10,8 +10,5 @@ TopkAmountStrategySpace = { } QLibDataLabelSpace = { - "labels": hp.choice( - "labels", - [["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["Ref($close, -5)/$close - 1"]], - ) + "labels": hp.choice("labels", [["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["Ref($close, -5)/$close - 1"]],) } diff --git a/qlib/contrib/tuner/tuner.py b/qlib/contrib/tuner/tuner.py index 2ce957859..e81d41a9a 100644 --- a/qlib/contrib/tuner/tuner.py +++ b/qlib/contrib/tuner/tuner.py @@ -28,10 +28,7 @@ class Tuner: self.optim_config = optim_config self.max_evals = self.tuner_config.get("max_evals", 10) - self.ex_dir = os.path.join( - self.tuner_config["experiment"]["dir"], - self.tuner_config["experiment"]["name"], - ) + self.ex_dir = os.path.join(self.tuner_config["experiment"]["dir"], self.tuner_config["experiment"]["name"],) self.best_params = None self.best_res = None @@ -42,10 +39,7 @@ class Tuner: TimeInspector.set_time_mark() fmin( - fn=self.objective, - space=self.space, - algo=tpe.suggest, - max_evals=self.max_evals, + fn=self.objective, space=self.space, algo=tpe.suggest, max_evals=self.max_evals, ) self.logger.info("Local best params: {} ".format(self.best_params)) TimeInspector.log_cost_time( @@ -159,8 +153,7 @@ class QLibTuner(Tuner): estimator_config["data"]["args"].update(params["data_label_space"]) estimator_path = os.path.join( - self.tuner_config["experiment"].get("dir", "../"), - QLibTuner.ESTIMATOR_CONFIG_NAME, + self.tuner_config["experiment"].get("dir", "../"), QLibTuner.ESTIMATOR_CONFIG_NAME, ) with open(estimator_path, "w") as fp: @@ -173,27 +166,20 @@ class QLibTuner(Tuner): model_space_name = self.tuner_config["model"].get("space", None) if model_space_name is None: raise ValueError("Please give the search space of model.") - model_space = getattr( - importlib.import_module(".space", package="qlib.contrib.tuner"), - model_space_name, - ) + model_space = getattr(importlib.import_module(".space", package="qlib.contrib.tuner"), model_space_name,) # 2. Setup strategy space strategy_space_name = self.tuner_config["strategy"].get("space", None) if strategy_space_name is None: raise ValueError("Please give the search space of strategy.") - strategy_space = getattr( - importlib.import_module(".space", package="qlib.contrib.tuner"), - strategy_space_name, - ) + strategy_space = getattr(importlib.import_module(".space", package="qlib.contrib.tuner"), strategy_space_name,) # 3. Setup data label space if given if self.tuner_config.get("data_label", None) is not None: data_label_space_name = self.tuner_config["data_label"].get("space", None) if data_label_space_name is not None: data_label_space = getattr( - importlib.import_module(".space", package="qlib.contrib.tuner"), - data_label_space_name, + importlib.import_module(".space", package="qlib.contrib.tuner"), data_label_space_name, ) else: data_label_space_name = None diff --git a/qlib/data/client.py b/qlib/data/client.py index 5244a7e45..d1a68cb38 100644 --- a/qlib/data/client.py +++ b/qlib/data/client.py @@ -26,8 +26,7 @@ class Client: self.logger = get_module_logger(self.__class__.__name__) # bind connect/disconnect callbacks self.sio.on( - "connect", - lambda: self.logger.debug("Connect to server {}".format(self.sio.connection_url)), + "connect", lambda: self.logger.debug("Connect to server {}".format(self.sio.connection_url)), ) self.sio.on("disconnect", lambda: self.logger.debug("Disconnect from server!")) diff --git a/qlib/data/data.py b/qlib/data/data.py index 762467da3..47cded79c 100644 --- a/qlib/data/data.py +++ b/qlib/data/data.py @@ -328,14 +328,7 @@ class DatasetProvider(abc.ABC): raise NotImplementedError("Subclass of DatasetProvider must implement `Dataset` method") def _uri( - self, - instruments, - fields, - start_time=None, - end_time=None, - freq="day", - disk_cache=1, - **kwargs, + self, instruments, fields, start_time=None, end_time=None, freq="day", disk_cache=1, **kwargs, ): """Get task uri, used when generating rabbitmq task in qlib_server @@ -414,29 +407,13 @@ class DatasetProvider(abc.ABC): for inst, spans in instruments_d.items(): data[inst] = p.apply_async( DatasetProvider.expression_calculator, - args=( - inst, - start_time, - end_time, - freq, - normalize_column_names, - spans, - C, - ), + args=(inst, start_time, end_time, freq, normalize_column_names, spans, C,), ) else: for inst in instruments_d: data[inst] = p.apply_async( DatasetProvider.expression_calculator, - args=( - inst, - start_time, - end_time, - freq, - normalize_column_names, - None, - C, - ), + args=(inst, start_time, end_time, freq, normalize_column_names, None, C,), ) p.close() @@ -598,12 +575,7 @@ class LocalInstrumentProvider(InstrumentProvider): start_time = pd.Timestamp(start_time or cal[0]) end_time = pd.Timestamp(end_time or cal[-1]) _instruments_filtered = { - inst: list( - filter( - lambda x: x[0] <= x[1], - [(max(start_time, x[0]), min(end_time, x[1])) for x in spans], - ) - ) + inst: list(filter(lambda x: x[0] <= x[1], [(max(start_time, x[0]), min(end_time, x[1])) for x in spans],)) for inst, spans in _instruments.items() } _instruments_filtered = {key: value for key, value in _instruments_filtered.items() if value} @@ -723,14 +695,7 @@ class LocalDatasetProvider(DatasetProvider): for inst in instruments_d: p.apply_async( - LocalDatasetProvider.cache_walker, - args=( - inst, - start_time, - end_time, - freq, - column_names, - ), + LocalDatasetProvider.cache_walker, args=(inst, start_time, end_time, freq, column_names,), ) p.close() @@ -763,12 +728,7 @@ class ClientCalendarProvider(CalendarProvider): def calendar(self, start_time=None, end_time=None, freq="day", future=False): self.conn.send_request( request_type="calendar", - request_content={ - "start_time": str(start_time), - "end_time": str(end_time), - "freq": freq, - "future": future, - }, + request_content={"start_time": str(start_time), "end_time": str(end_time), "freq": freq, "future": future,}, msg_queue=self.queue, msg_proc_func=lambda response_content: [pd.Timestamp(c) for c in response_content], ) @@ -832,14 +792,7 @@ class ClientDatasetProvider(DatasetProvider): self.queue = queue.Queue() def dataset( - self, - instruments, - fields, - start_time=None, - end_time=None, - freq="day", - disk_cache=0, - return_uri=False, + self, instruments, fields, start_time=None, end_time=None, freq="day", disk_cache=0, return_uri=False, ): if Inst.get_inst_type(instruments) == Inst.DICT: get_module_logger("data").warning( @@ -942,13 +895,7 @@ class BaseProvider: return Inst.list_instruments(instruments, start_time, end_time, freq, as_list) def features( - self, - instruments, - fields, - start_time=None, - end_time=None, - freq="day", - disk_cache=None, + self, instruments, fields, start_time=None, end_time=None, freq="day", disk_cache=None, ): """ Parameters: diff --git a/qlib/data/dataset/utils.py b/qlib/data/dataset/utils.py index feda19044..58e2bd968 100644 --- a/qlib/data/dataset/utils.py +++ b/qlib/data/dataset/utils.py @@ -32,10 +32,7 @@ def get_level_index(df: pd.DataFrame, level=Union[str, int]) -> int: def fetch_df_by_index( - df: pd.DataFrame, - selector: Union[pd.Timestamp, slice, str, list], - level: Union[str, int], - fetch_orig=True, + df: pd.DataFrame, selector: Union[pd.Timestamp, slice, str, list], level: Union[str, int], fetch_orig=True, ) -> pd.DataFrame: """ fetch data from `data` with `selector` and `level` diff --git a/qlib/data/filter.py b/qlib/data/filter.py index 70f9d3278..811fd387f 100644 --- a/qlib/data/filter.py +++ b/qlib/data/filter.py @@ -341,12 +341,7 @@ class ExpressionDFilter(SeriesDFilter): # do not use dataset cache try: _features = DatasetD.dataset( - instruments, - [self.rule_expression], - fstart, - fend, - freq=self.filter_freq, - disk_cache=0, + instruments, [self.rule_expression], fstart, fend, freq=self.filter_freq, disk_cache=0, ) except TypeError: # use LocalDatasetProvider diff --git a/qlib/model/riskmodel.py b/qlib/model/riskmodel.py index 8eec73e00..f19c60fc9 100644 --- a/qlib/model/riskmodel.py +++ b/qlib/model/riskmodel.py @@ -38,7 +38,7 @@ class RiskModel(BaseModel): self.scale_return = scale_return def predict( - self, X: Union[pd.Series, pd.DataFrame, np.ndarray], return_corr: bool = False, is_price: bool = True + self, X: Union[pd.Series, pd.DataFrame, np.ndarray], return_corr: bool = False, is_price: bool = True ) -> Union[pd.DataFrame, np.ndarray]: """ Args: @@ -373,8 +373,7 @@ class ShrinkCovEstimator(RiskModel): roff1 = np.sum(v1 * cov_mkt[:, None].T) / var_mkt - np.sum(np.diag(v1) * cov_mkt) / var_mkt v3 = z.T.dot(z) / t - var_mkt * S roff3 = ( - np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt ** 2 - np.sum( - np.diag(v3) * cov_mkt ** 2) / var_mkt ** 2 + np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt ** 2 - np.sum(np.diag(v3) * cov_mkt ** 2) / var_mkt ** 2 ) roff = 2 * roff1 - roff3 rho = rdiag + roff @@ -434,7 +433,7 @@ class POETCovEstimator(RiskModel): if self.num_factors > 0: Dd, V = np.linalg.eig(Y.T.dot(Y)) V = V[:, np.argsort(Dd)] - F = V[:, -self.num_factors:][:, ::-1] * np.sqrt(n) + F = V[:, -self.num_factors :][:, ::-1] * np.sqrt(n) LamPCA = Y.dot(F) / n uhat = np.asarray(Y - LamPCA.dot(F.T)) Lowrank = np.asarray(LamPCA.dot(LamPCA.T)) @@ -490,8 +489,14 @@ class StructuredCovEstimator(RiskModel): FACTOR_MODEL_PCA = "pca" FACTOR_MODEL_FA = "fa" - def __init__(self, factor_model: str = 'pca', num_factors: int = 10, nan_option: str = "ignore", - assume_centered: bool = False, scale_return: bool = True): + def __init__( + self, + factor_model: str = "pca", + num_factors: int = 10, + nan_option: str = "ignore", + assume_centered: bool = False, + scale_return: bool = True, + ): """ Args: factor_model (str): the latent factor models used to estimate the structured covariance (`pca`/`fa`). @@ -505,14 +510,17 @@ class StructuredCovEstimator(RiskModel): assert factor_model in [ self.FACTOR_MODEL_PCA, self.FACTOR_MODEL_FA, - ], 'factor_model={} is not supported'.format(factor_model) + ], "factor_model={} is not supported".format(factor_model) self.solver = PCA if factor_model == self.FACTOR_MODEL_PCA else FactorAnalysis self.num_factors = num_factors def predict( - self, X: Union[pd.Series, pd.DataFrame, np.ndarray], return_corr: bool = False, is_price: bool = True, - return_decomposed_components=False + self, + X: Union[pd.Series, pd.DataFrame, np.ndarray], + return_corr: bool = False, + is_price: bool = True, + return_decomposed_components=False, ) -> Union[pd.DataFrame, np.ndarray, tuple]: """ Args: @@ -525,8 +533,9 @@ class StructuredCovEstimator(RiskModel): Returns: tuple or pd.DataFrame or np.ndarray: decomposed covariance matrix or estimated covariance or correlation. """ - assert not return_corr or not return_decomposed_components, \ - 'Can only return either correlation matrix or decomposed components.' + assert ( + not return_corr or not return_decomposed_components + ), "Can only return either correlation matrix or decomposed components." # transform input into 2D array if not isinstance(X, (pd.Series, pd.DataFrame)): diff --git a/qlib/portfolio/optimizer.py b/qlib/portfolio/optimizer.py index 728a04ea9..391242127 100644 --- a/qlib/portfolio/optimizer.py +++ b/qlib/portfolio/optimizer.py @@ -38,13 +38,13 @@ class PortfolioOptimizer(BaseOptimizer): OPT_INV = "inv" def __init__( - self, - method: str = "inv", - lamb: float = 0, - delta: float = 0, - alpha: float = 0.0, - scale_alpha: bool = True, - tol: float = 1e-8, + self, + method: str = "inv", + lamb: float = 0, + delta: float = 0, + alpha: float = 0.0, + scale_alpha: bool = True, + tol: float = 1e-8, ): """ Args: @@ -71,10 +71,10 @@ class PortfolioOptimizer(BaseOptimizer): self.scale_alpha = scale_alpha def __call__( - self, - S: Union[np.ndarray, pd.DataFrame], - u: Optional[Union[np.ndarray, pd.Series]] = None, - w0: Optional[Union[np.ndarray, pd.Series]] = None, + self, + S: Union[np.ndarray, pd.DataFrame], + u: Optional[Union[np.ndarray, pd.Series]] = None, + w0: Optional[Union[np.ndarray, pd.Series]] = None, ) -> Union[np.ndarray, pd.Series]: """ Args: @@ -163,7 +163,7 @@ class PortfolioOptimizer(BaseOptimizer): return self._solve(len(S), self._get_objective_gmv(S), *self._get_constrains(w0)) def _optimize_mvo( - self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None + self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None ) -> np.ndarray: """optimize mean-variance portfolio @@ -259,6 +259,7 @@ class PortfolioOptimizer(BaseOptimizer): # add l2 regularization wrapped_obj = obj if self.alpha > 0: + def opt_obj(x): return obj(x) + self.alpha * np.sum(np.square(x)) @@ -281,12 +282,21 @@ class EnhancedIndexingOptimizer(BaseOptimizer): This optimizer always assumes full investment and no-shorting. """ - START_FROM_W0 = 'w0' - START_FROM_BENCH = 'benchmark' - DO_NOT_START_FROM = 'no_warm_start' + START_FROM_W0 = "w0" + START_FROM_BENCH = "benchmark" + DO_NOT_START_FROM = "no_warm_start" - def __init__(self, lamb: float = 10, delta: float = 0.4, bench_dev: float = 0.01, inds_dev: float = 0.01, - scale_alpha=True, verbose: bool = False, warm_start: str = DO_NOT_START_FROM, max_iters: int = 10000): + def __init__( + self, + lamb: float = 10, + delta: float = 0.4, + bench_dev: float = 0.01, + inds_dev: float = 0.01, + scale_alpha=True, + verbose: bool = False, + warm_start: str = DO_NOT_START_FROM, + max_iters: int = 10000, + ): """ Args: lamb (float): risk aversion parameter (larger `lamb` means less focus on return) @@ -310,18 +320,28 @@ class EnhancedIndexingOptimizer(BaseOptimizer): assert inds_dev >= 0, "industry deviation limit `inds_dev` should be positive" self.inds_dev = inds_dev - assert warm_start in [self.DO_NOT_START_FROM, self.START_FROM_W0, - self.START_FROM_BENCH], "illegal warm start option" - self.start_from_w0 = (warm_start == self.START_FROM_W0) - self.start_from_bench = (warm_start == self.START_FROM_BENCH) + assert warm_start in [ + self.DO_NOT_START_FROM, + self.START_FROM_W0, + self.START_FROM_BENCH, + ], "illegal warm start option" + self.start_from_w0 = warm_start == self.START_FROM_W0 + self.start_from_bench = warm_start == self.START_FROM_BENCH self.scale_alpha = scale_alpha self.verbose = verbose self.max_iters = max_iters - def __call__(self, u: np.ndarray, F: np.ndarray, covB: np.ndarray, varU: np.ndarray, w0: np.ndarray, - w_bench: np.ndarray, inds_onehot: np.ndarray - ) -> Union[np.ndarray, pd.Series]: + def __call__( + self, + u: np.ndarray, + F: np.ndarray, + covB: np.ndarray, + varU: np.ndarray, + w0: np.ndarray, + w_bench: np.ndarray, + inds_onehot: np.ndarray, + ) -> Union[np.ndarray, pd.Series]: """ Args: u (np.ndarray): expected returns (a.k.a., alpha) @@ -352,7 +372,7 @@ class EnhancedIndexingOptimizer(BaseOptimizer): d_bench >= -self.bench_dev, d_bench <= self.bench_dev, d_inds >= -self.inds_dev, - d_inds <= self.inds_dev + d_inds <= self.inds_dev, ] if w0 is not None: turnover = cp.sum(cp.abs(w - w0)) @@ -361,7 +381,7 @@ class EnhancedIndexingOptimizer(BaseOptimizer): warm_start = False if self.start_from_w0: if w0 is None: - print('Warning: try warm start with w0, but w0 is `None`.') + print("Warning: try warm start with w0, but w0 is `None`.") else: w.value = w0 warm_start = True @@ -372,7 +392,7 @@ class EnhancedIndexingOptimizer(BaseOptimizer): prob = cp.Problem(obj, cons) prob.solve(solver=cp.SCS, verbose=self.verbose, warm_start=warm_start, max_iters=self.max_iters) - if prob.status != 'optimal': - print('Warning: solve failed.', prob.status) + if prob.status != "optimal": + print("Warning: solve failed.", prob.status) return np.asarray(w.value) diff --git a/qlib/tests/__init__.py b/qlib/tests/__init__.py index f92e72787..eb6f9c5ed 100644 --- a/qlib/tests/__init__.py +++ b/qlib/tests/__init__.py @@ -18,10 +18,6 @@ class TestAutoData(unittest.TestCase): print(f"Qlib data is not found in {provider_uri}") GetData().qlib_data( - name="qlib_data_simple", - region="cn", - interval="1d", - target_dir=provider_uri, - delete_old=False, + name="qlib_data_simple", region="cn", interval="1d", target_dir=provider_uri, delete_old=False, ) init(provider_uri=provider_uri, region=REG_CN, **cls._setup_kwargs) diff --git a/qlib/workflow/record_temp.py b/qlib/workflow/record_temp.py index be458a24d..0c704b896 100644 --- a/qlib/workflow/record_temp.py +++ b/qlib/workflow/record_temp.py @@ -193,10 +193,7 @@ class SigAnaRecord(SignalRecord): } ) objects.update( - { - "long_short_r.pkl": long_short_r, - "long_avg_r.pkl": long_avg_r, - } + {"long_short_r.pkl": long_short_r, "long_avg_r.pkl": long_avg_r,} ) self.recorder.log_metrics(**metrics) self.recorder.save_objects(**objects, artifact_path=self.get_path()) diff --git a/scripts/data_collector/yahoo/collector.py b/scripts/data_collector/yahoo/collector.py index 743f89462..24526e332 100644 --- a/scripts/data_collector/yahoo/collector.py +++ b/scripts/data_collector/yahoo/collector.py @@ -39,13 +39,7 @@ class YahooData: INTERVAL_1d = "1d" def __init__( - self, - timezone: str = None, - start=None, - end=None, - interval="1d", - delay=0, - show_1min_logging: bool = False, + self, timezone: str = None, start=None, end=None, interval="1d", delay=0, show_1min_logging: bool = False, ): """ @@ -125,11 +119,7 @@ class YahooData: self._sleep() _remote_interval = "1m" if self._interval == self.INTERVAL_1min else self._interval return self.get_data_from_remote( - symbol, - interval=_remote_interval, - start=start_, - end=end_, - show_1min_logging=self._show_1min_logging, + symbol, interval=_remote_interval, start=start_, end=end_, show_1min_logging=self._show_1min_logging, ) _result = None @@ -438,9 +428,7 @@ class YahooNormalize: DAILY_FORMAT = "%Y-%m-%d" def __init__( - self, - date_field_name: str = "date", - symbol_field_name: str = "symbol", + self, date_field_name: str = "date", symbol_field_name: str = "symbol", ): """ @@ -458,10 +446,7 @@ class YahooNormalize: @staticmethod def normalize_yahoo( - df: pd.DataFrame, - calendar_list: list = None, - date_field_name: str = "date", - symbol_field_name: str = "symbol", + df: pd.DataFrame, calendar_list: list = None, date_field_name: str = "date", symbol_field_name: str = "symbol", ): if df.empty: return df @@ -566,9 +551,7 @@ class YahooNormalize1min(YahooNormalize, ABC): CONSISTENT_1d = False def __init__( - self, - date_field_name: str = "date", - symbol_field_name: str = "symbol", + self, date_field_name: str = "date", symbol_field_name: str = "symbol", ): """ diff --git a/scripts/dump_bin.py b/scripts/dump_bin.py index 4811fd486..ab24fa9ca 100644 --- a/scripts/dump_bin.py +++ b/scripts/dump_bin.py @@ -153,22 +153,13 @@ class DumpDataBase: @staticmethod def _read_calendars(calendar_path: Path) -> List[pd.Timestamp]: - return sorted( - map( - pd.Timestamp, - pd.read_csv(calendar_path, header=None).loc[:, 0].tolist(), - ) - ) + return sorted(map(pd.Timestamp, pd.read_csv(calendar_path, header=None).loc[:, 0].tolist(),)) def _read_instruments(self, instrument_path: Path) -> pd.DataFrame: df = pd.read_csv( instrument_path, sep=self.INSTRUMENTS_SEP, - names=[ - self.symbol_field_name, - self.INSTRUMENTS_START_FIELD, - self.INSTRUMENTS_END_FIELD, - ], + names=[self.symbol_field_name, self.INSTRUMENTS_START_FIELD, self.INSTRUMENTS_END_FIELD,], ) return df diff --git a/setup.py b/setup.py index 6582054b9..d8a9d9efa 100644 --- a/setup.py +++ b/setup.py @@ -55,7 +55,7 @@ REQUIRED = [ "tornado", "joblib>=0.17.0", "ruamel.yaml>=0.16.12", - "scikit-learn>=0.22" + "scikit-learn>=0.22", ] # Numpy include @@ -70,16 +70,10 @@ with open(os.path.join(here, "README.md"), encoding="utf-8") as f: # Cython Extensions extensions = [ Extension( - "qlib.data._libs.rolling", - ["qlib/data/_libs/rolling.pyx"], - language="c++", - include_dirs=[NUMPY_INCLUDE], + "qlib.data._libs.rolling", ["qlib/data/_libs/rolling.pyx"], language="c++", include_dirs=[NUMPY_INCLUDE], ), Extension( - "qlib.data._libs.expanding", - ["qlib/data/_libs/expanding.pyx"], - language="c++", - include_dirs=[NUMPY_INCLUDE], + "qlib.data._libs.expanding", ["qlib/data/_libs/expanding.pyx"], language="c++", include_dirs=[NUMPY_INCLUDE], ), ] @@ -98,9 +92,7 @@ setup( # py_modules=['qlib'], entry_points={ # 'console_scripts': ['mycli=mymodule:cli'], - "console_scripts": [ - "qrun=qlib.workflow.cli:run", - ], + "console_scripts": ["qrun=qlib.workflow.cli:run",], }, ext_modules=extensions, install_requires=REQUIRED, diff --git a/tests/test_all_pipeline.py b/tests/test_all_pipeline.py index f6e77cba4..8b3819c83 100644 --- a/tests/test_all_pipeline.py +++ b/tests/test_all_pipeline.py @@ -78,10 +78,7 @@ port_analysis_config = { "strategy": { "class": "TopkDropoutStrategy", "module_path": "qlib.contrib.strategy.strategy", - "kwargs": { - "topk": 50, - "n_drop": 5, - }, + "kwargs": {"topk": 50, "n_drop": 5,}, }, "backtest": { "verbose": False, @@ -176,9 +173,7 @@ class TestAllFlow(TestAutoData): def test_1_backtest(self): analyze_df = backtest_analysis(TestAllFlow.PRED_SCORE, TestAllFlow.RID) self.assertGreaterEqual( - analyze_df.loc(axis=0)["excess_return_with_cost", "annualized_return"].values[0], - 0.10, - "backtest failed", + analyze_df.loc(axis=0)["excess_return_with_cost", "annualized_return"].values[0], 0.10, "backtest failed", ) diff --git a/tests/test_dump_data.py b/tests/test_dump_data.py index dfa7f8556..de649c37e 100644 --- a/tests/test_dump_data.py +++ b/tests/test_dump_data.py @@ -40,9 +40,7 @@ class TestDumpData(unittest.TestCase): TestDumpData.STOCK_NAMES = list(map(lambda x: x.name[:-4].upper(), SOURCE_DIR.glob("*.csv"))) provider_uri = str(QLIB_DIR.resolve()) qlib.init( - provider_uri=provider_uri, - expression_cache=None, - dataset_cache=None, + provider_uri=provider_uri, expression_cache=None, dataset_cache=None, ) @classmethod @@ -54,10 +52,7 @@ class TestDumpData(unittest.TestCase): def test_1_dump_calendars(self): ori_calendars = set( - map( - pd.Timestamp, - pd.read_csv(QLIB_DIR.joinpath("calendars", "day.txt"), header=None).loc[:, 0].values, - ) + map(pd.Timestamp, pd.read_csv(QLIB_DIR.joinpath("calendars", "day.txt"), header=None).loc[:, 0].values,) ) res_calendars = set(D.calendar()) assert len(ori_calendars - res_calendars) == len(res_calendars - ori_calendars) == 0, "dump calendars failed" diff --git a/tests/test_get_data.py b/tests/test_get_data.py index c511d1b91..d5637b025 100644 --- a/tests/test_get_data.py +++ b/tests/test_get_data.py @@ -26,9 +26,7 @@ class TestGetData(unittest.TestCase): def setUpClass(cls) -> None: provider_uri = str(QLIB_DIR.resolve()) qlib.init( - provider_uri=provider_uri, - expression_cache=None, - dataset_cache=None, + provider_uri=provider_uri, expression_cache=None, dataset_cache=None, ) @classmethod From 37871389b98fe34da35cdd2e996469ac3c7434ff Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 22 Feb 2021 11:25:42 +0800 Subject: [PATCH 021/134] Format code with the latest version of black. --- qlib/config.py | 24 +++++-- qlib/contrib/backtest/__init__.py | 18 ++++- qlib/contrib/backtest/profit_attribution.py | 23 +++++-- qlib/contrib/data/handler.py | 10 ++- qlib/contrib/eva/alpha.py | 6 +- qlib/contrib/evaluate.py | 7 +- qlib/contrib/evaluate_portfolio.py | 16 ++++- qlib/contrib/model/catboost_model.py | 4 +- qlib/contrib/model/pytorch_alstm.py | 21 ++++-- qlib/contrib/model/pytorch_alstm_ts.py | 17 +++-- qlib/contrib/model/pytorch_gats.py | 22 ++++-- qlib/contrib/model/pytorch_gats_ts.py | 18 ++++- qlib/contrib/model/pytorch_gru.py | 21 ++++-- qlib/contrib/model/pytorch_gru_ts.py | 17 ++++- qlib/contrib/model/pytorch_lstm.py | 21 ++++-- qlib/contrib/model/pytorch_lstm_ts.py | 17 ++++- qlib/contrib/model/pytorch_nn.py | 6 +- qlib/contrib/model/pytorch_sfm.py | 19 ++++- qlib/contrib/model/pytorch_tabnet.py | 14 +++- qlib/contrib/model/xgboost.py | 4 +- qlib/contrib/online/executor.py | 24 +++++-- qlib/contrib/online/manager.py | 6 +- qlib/contrib/online/operator.py | 8 ++- qlib/contrib/online/utils.py | 6 +- .../analysis_model_performance.py | 66 ++++++++++++++---- .../analysis_position/cumulative_return.py | 36 ++++++++-- .../analysis_position/parse_position.py | 5 +- .../report/analysis_position/rank_label.py | 16 ++++- .../report/analysis_position/report.py | 15 +++- qlib/contrib/report/graph.py | 6 +- qlib/contrib/strategy/cost_control.py | 5 +- qlib/contrib/strategy/order_generator.py | 12 +++- qlib/contrib/tuner/launcher.py | 6 +- qlib/contrib/tuner/space.py | 5 +- qlib/contrib/tuner/tuner.py | 26 +++++-- qlib/data/client.py | 3 +- qlib/data/data.py | 69 ++++++++++++++++--- qlib/data/dataset/utils.py | 5 +- qlib/data/filter.py | 7 +- qlib/tests/__init__.py | 6 +- qlib/workflow/record_temp.py | 5 +- 41 files changed, 526 insertions(+), 116 deletions(-) diff --git a/qlib/config.py b/qlib/config.py index 344eb8527..52b05568d 100644 --- a/qlib/config.py +++ b/qlib/config.py @@ -115,7 +115,12 @@ _default_config = { "format": "[%(process)s:%(threadName)s](%(asctime)s) %(levelname)s - %(name)s - [%(filename)s:%(lineno)d] - %(message)s" } }, - "filters": {"field_not_found": {"()": "qlib.log.LogFilter", "param": [".*?WARN: data not found for.*?"],}}, + "filters": { + "field_not_found": { + "()": "qlib.log.LogFilter", + "param": [".*?WARN: data not found for.*?"], + } + }, "handlers": { "console": { "class": "logging.StreamHandler", @@ -130,7 +135,10 @@ _default_config = { "exp_manager": { "class": "MLflowExpManager", "module_path": "qlib.workflow.expm", - "kwargs": {"uri": "file:" + str(Path(os.getcwd()).resolve() / "mlruns"), "default_exp_name": "Experiment",}, + "kwargs": { + "uri": "file:" + str(Path(os.getcwd()).resolve() / "mlruns"), + "default_exp_name": "Experiment", + }, }, } @@ -192,8 +200,16 @@ HIGH_FREQ_CONFIG = { } _default_region_config = { - REG_CN: {"trade_unit": 100, "limit_threshold": 0.099, "deal_price": "vwap",}, - REG_US: {"trade_unit": 1, "limit_threshold": None, "deal_price": "close",}, + REG_CN: { + "trade_unit": 100, + "limit_threshold": 0.099, + "deal_price": "vwap", + }, + REG_US: { + "trade_unit": 1, + "limit_threshold": None, + "deal_price": "close", + }, } diff --git a/qlib/contrib/backtest/__init__.py b/qlib/contrib/backtest/__init__.py index bd3494abf..aa24ffb0c 100644 --- a/qlib/contrib/backtest/__init__.py +++ b/qlib/contrib/backtest/__init__.py @@ -18,7 +18,13 @@ logger = get_module_logger("backtest caller") def get_strategy( - strategy=None, topk=50, margin=0.5, n_drop=5, risk_degree=0.95, str_type="dropout", adjust_dates=None, + strategy=None, + topk=50, + margin=0.5, + n_drop=5, + risk_degree=0.95, + str_type="dropout", + adjust_dates=None, ): """get_strategy @@ -69,7 +75,11 @@ def get_strategy( str_cls = getattr(strategy_pool, str_cls_dict.get(str_type)) strategy = str_cls( - topk=topk, buffer_margin=margin, n_drop=n_drop, risk_degree=risk_degree, adjust_dates=adjust_dates, + topk=topk, + buffer_margin=margin, + n_drop=n_drop, + risk_degree=risk_degree, + adjust_dates=adjust_dates, ) elif isinstance(strategy, (dict, str)): # 2) create strategy with init_instance_by_config @@ -162,7 +172,9 @@ def get_exchange( def get_executor( - executor=None, trade_exchange=None, verbose=True, + executor=None, + trade_exchange=None, + verbose=True, ): """get_executor diff --git a/qlib/contrib/backtest/profit_attribution.py b/qlib/contrib/backtest/profit_attribution.py index 355f06373..20c6f638f 100644 --- a/qlib/contrib/backtest/profit_attribution.py +++ b/qlib/contrib/backtest/profit_attribution.py @@ -12,7 +12,10 @@ from pathlib import Path def get_benchmark_weight( - bench, start_date=None, end_date=None, path=None, + bench, + start_date=None, + end_date=None, + path=None, ): """get_benchmark_weight @@ -213,7 +216,12 @@ def get_stock_group(stock_group_field_df, bench_stock_weight_df, group_method, g def brinson_pa( - positions, bench="SH000905", group_field="industry", group_method="category", group_n=None, deal_price="vwap", + positions, + bench="SH000905", + group_field="industry", + group_method="category", + group_n=None, + deal_price="vwap", ): """brinson profit attribution @@ -247,10 +255,17 @@ def brinson_pa( # suspend stock is NAN. So we have to get more date to forward fill the NAN shift_start_date = start_date - datetime.timedelta(days=250) instruments = D.list_instruments( - D.instruments(market="all"), start_time=shift_start_date, end_time=end_date, as_list=True, + D.instruments(market="all"), + start_time=shift_start_date, + end_time=end_date, + as_list=True, ) stock_df = D.features( - instruments, [group_field, deal_price], start_time=shift_start_date, end_time=end_date, freq="day", + instruments, + [group_field, deal_price], + start_time=shift_start_date, + end_time=end_date, + freq="day", ) stock_df.columns = [group_field, "deal_price"] diff --git a/qlib/contrib/data/handler.py b/qlib/contrib/data/handler.py index 574287819..970b032d6 100644 --- a/qlib/contrib/data/handler.py +++ b/qlib/contrib/data/handler.py @@ -21,7 +21,10 @@ def check_transform_proc(proc_l, fit_start_time, fit_end_time): fit_start_time is not None and fit_end_time is not None ), "Make sure `fit_start_time` and `fit_end_time` are not None." pkwargs.update( - {"fit_start_time": fit_start_time, "fit_end_time": fit_end_time,} + { + "fit_start_time": fit_start_time, + "fit_end_time": fit_end_time, + } ) new_l.append({"class": klass.__name__, "kwargs": pkwargs}) else: @@ -167,7 +170,10 @@ class Alpha158(DataHandlerLP): def get_feature_config(self): conf = { "kbar": {}, - "price": {"windows": [0], "feature": ["OPEN", "HIGH", "LOW", "VWAP"],}, + "price": { + "windows": [0], + "feature": ["OPEN", "HIGH", "LOW", "VWAP"], + }, "rolling": {}, } return self.parse_config_to_fields(conf) diff --git a/qlib/contrib/eva/alpha.py b/qlib/contrib/eva/alpha.py index 363a18458..c68571853 100644 --- a/qlib/contrib/eva/alpha.py +++ b/qlib/contrib/eva/alpha.py @@ -35,7 +35,11 @@ def calc_ic(pred: pd.Series, label: pd.Series, date_col="datetime", dropna=False def calc_long_short_return( - pred: pd.Series, label: pd.Series, date_col: str = "datetime", quantile: float = 0.2, dropna: bool = False, + pred: pd.Series, + label: pd.Series, + date_col: str = "datetime", + quantile: float = 0.2, + dropna: bool = False, ) -> Tuple[pd.Series, pd.Series]: """ calculate long-short return diff --git a/qlib/contrib/evaluate.py b/qlib/contrib/evaluate.py index 5cb1ce4eb..4aa5b5515 100644 --- a/qlib/contrib/evaluate.py +++ b/qlib/contrib/evaluate.py @@ -244,7 +244,12 @@ def long_short_backtest( short_returns[date] = np.mean(short_profit) + np.mean(all_profit) ls_returns[date] = np.mean(short_profit) + np.mean(long_profit) - return dict(zip(["long", "short", "long_short"], map(pd.Series, [long_returns, short_returns, ls_returns]),)) + return dict( + zip( + ["long", "short", "long_short"], + map(pd.Series, [long_returns, short_returns, ls_returns]), + ) + ) def t_run(): diff --git a/qlib/contrib/evaluate_portfolio.py b/qlib/contrib/evaluate_portfolio.py index 2d94105e4..04ddd8db0 100644 --- a/qlib/contrib/evaluate_portfolio.py +++ b/qlib/contrib/evaluate_portfolio.py @@ -64,7 +64,12 @@ def get_position_value(evaluate_date, position): instruments = list(set(instruments) - set(["cash"])) # filter 'cash' fields = ["$close"] close_data_df = D.features( - instruments, fields, start_time=evaluate_date, end_time=evaluate_date, freq="day", disk_cache=0, + instruments, + fields, + start_time=evaluate_date, + end_time=evaluate_date, + freq="day", + disk_cache=0, ) value = _get_position_value_from_df(evaluate_date, position, close_data_df) return value @@ -82,7 +87,14 @@ def get_position_list_value(positions): start_date, end_date = day_list[0], day_list[-1] # load data fields = ["$close"] - close_data_df = D.features(instruments, fields, start_time=start_date, end_time=end_date, freq="day", disk_cache=0,) + close_data_df = D.features( + instruments, + fields, + start_time=start_date, + end_time=end_date, + freq="day", + disk_cache=0, + ) # generate value # return dict for time:position_value value_dict = OrderedDict() diff --git a/qlib/contrib/model/catboost_model.py b/qlib/contrib/model/catboost_model.py index 2840c2cef..d57c32b70 100644 --- a/qlib/contrib/model/catboost_model.py +++ b/qlib/contrib/model/catboost_model.py @@ -32,7 +32,9 @@ class CatBoostModel(Model): **kwargs ): df_train, df_valid = dataset.prepare( - ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] diff --git a/qlib/contrib/model/pytorch_alstm.py b/qlib/contrib/model/pytorch_alstm.py index 306e68aad..bbbb61851 100644 --- a/qlib/contrib/model/pytorch_alstm.py +++ b/qlib/contrib/model/pytorch_alstm.py @@ -118,7 +118,10 @@ class ALSTM(Model): torch.manual_seed(self.seed) self.ALSTM_model = ALSTMModel( - d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, + d_feat=self.d_feat, + hidden_size=self.hidden_size, + num_layers=self.num_layers, + dropout=self.dropout, ) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.ALSTM_model.parameters(), lr=self.lr) @@ -208,11 +211,17 @@ class ALSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, + self, + dataset: DatasetH, + evals_result=dict(), + verbose=True, + save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -319,12 +328,14 @@ class ALSTMModel(nn.Module): self.fc_out = nn.Linear(in_features=self.hid_size * 2, out_features=1) self.att_net = nn.Sequential() self.att_net.add_module( - "att_fc_in", nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), + "att_fc_in", + nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), ) self.att_net.add_module("att_dropout", torch.nn.Dropout(self.dropout)) self.att_net.add_module("att_act", nn.Tanh()) self.att_net.add_module( - "att_fc_out", nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), + "att_fc_out", + nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), ) self.att_net.add_module("att_softmax", nn.Softmax(dim=1)) diff --git a/qlib/contrib/model/pytorch_alstm_ts.py b/qlib/contrib/model/pytorch_alstm_ts.py index 612bacbec..725568de8 100644 --- a/qlib/contrib/model/pytorch_alstm_ts.py +++ b/qlib/contrib/model/pytorch_alstm_ts.py @@ -123,7 +123,10 @@ class ALSTM(Model): torch.manual_seed(self.seed) self.ALSTM_model = ALSTMModel( - d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, + d_feat=self.d_feat, + hidden_size=self.hidden_size, + num_layers=self.num_layers, + dropout=self.dropout, ).to(self.device) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.ALSTM_model.parameters(), lr=self.lr) @@ -195,7 +198,11 @@ class ALSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset, evals_result=dict(), verbose=True, save_path=None, + self, + dataset, + evals_result=dict(), + verbose=True, + save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -302,12 +309,14 @@ class ALSTMModel(nn.Module): self.fc_out = nn.Linear(in_features=self.hid_size * 2, out_features=1) self.att_net = nn.Sequential() self.att_net.add_module( - "att_fc_in", nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), + "att_fc_in", + nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), ) self.att_net.add_module("att_dropout", torch.nn.Dropout(self.dropout)) self.att_net.add_module("att_act", nn.Tanh()) self.att_net.add_module( - "att_fc_out", nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), + "att_fc_out", + nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), ) self.att_net.add_module("att_softmax", nn.Softmax(dim=1)) diff --git a/qlib/contrib/model/pytorch_gats.py b/qlib/contrib/model/pytorch_gats.py index c59dc9197..07048e1bc 100644 --- a/qlib/contrib/model/pytorch_gats.py +++ b/qlib/contrib/model/pytorch_gats.py @@ -229,11 +229,17 @@ class GATs(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, + self, + dataset: DatasetH, + evals_result=dict(), + verbose=True, + save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -334,11 +340,19 @@ class GATModel(nn.Module): if base_model == "GRU": self.rnn = nn.GRU( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) elif base_model == "LSTM": self.rnn = nn.LSTM( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) else: raise ValueError("unknown base model name `%s`" % base_model) diff --git a/qlib/contrib/model/pytorch_gats_ts.py b/qlib/contrib/model/pytorch_gats_ts.py index dfc5f4ab5..1e94f56e4 100644 --- a/qlib/contrib/model/pytorch_gats_ts.py +++ b/qlib/contrib/model/pytorch_gats_ts.py @@ -242,7 +242,11 @@ class GATs(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset, evals_result=dict(), verbose=True, save_path=None, + self, + dataset, + evals_result=dict(), + verbose=True, + save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -357,11 +361,19 @@ class GATModel(nn.Module): if base_model == "GRU": self.rnn = nn.GRU( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) elif base_model == "LSTM": self.rnn = nn.LSTM( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) else: raise ValueError("unknown base model name `%s`" % base_model) diff --git a/qlib/contrib/model/pytorch_gru.py b/qlib/contrib/model/pytorch_gru.py index d2a774b65..84f863b9f 100755 --- a/qlib/contrib/model/pytorch_gru.py +++ b/qlib/contrib/model/pytorch_gru.py @@ -118,7 +118,10 @@ class GRU(Model): torch.manual_seed(self.seed) self.gru_model = GRUModel( - d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, + d_feat=self.d_feat, + hidden_size=self.hidden_size, + num_layers=self.num_layers, + dropout=self.dropout, ) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.gru_model.parameters(), lr=self.lr) @@ -208,11 +211,17 @@ class GRU(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, + self, + dataset: DatasetH, + evals_result=dict(), + verbose=True, + save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -296,7 +305,11 @@ class GRUModel(nn.Module): super().__init__() self.rnn = nn.GRU( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_gru_ts.py b/qlib/contrib/model/pytorch_gru_ts.py index 49f438cc3..bb6618b85 100755 --- a/qlib/contrib/model/pytorch_gru_ts.py +++ b/qlib/contrib/model/pytorch_gru_ts.py @@ -123,7 +123,10 @@ class GRU(Model): torch.manual_seed(self.seed) self.GRU_model = GRUModel( - d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, + d_feat=self.d_feat, + hidden_size=self.hidden_size, + num_layers=self.num_layers, + dropout=self.dropout, ).to(self.device) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.GRU_model.parameters(), lr=self.lr) @@ -195,7 +198,11 @@ class GRU(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset, evals_result=dict(), verbose=True, save_path=None, + self, + dataset, + evals_result=dict(), + verbose=True, + save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -279,7 +286,11 @@ class GRUModel(nn.Module): super().__init__() self.rnn = nn.GRU( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_lstm.py b/qlib/contrib/model/pytorch_lstm.py index 02ca16e36..163d500ec 100755 --- a/qlib/contrib/model/pytorch_lstm.py +++ b/qlib/contrib/model/pytorch_lstm.py @@ -118,7 +118,10 @@ class LSTM(Model): torch.manual_seed(self.seed) self.lstm_model = LSTMModel( - d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, + d_feat=self.d_feat, + hidden_size=self.hidden_size, + num_layers=self.num_layers, + dropout=self.dropout, ) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.lstm_model.parameters(), lr=self.lr) @@ -208,11 +211,17 @@ class LSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, + self, + dataset: DatasetH, + evals_result=dict(), + verbose=True, + save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -296,7 +305,11 @@ class LSTMModel(nn.Module): super().__init__() self.rnn = nn.LSTM( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_lstm_ts.py b/qlib/contrib/model/pytorch_lstm_ts.py index 2ec36f96e..cf4f8fb9f 100755 --- a/qlib/contrib/model/pytorch_lstm_ts.py +++ b/qlib/contrib/model/pytorch_lstm_ts.py @@ -123,7 +123,10 @@ class LSTM(Model): torch.manual_seed(self.seed) self.LSTM_model = LSTMModel( - d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, + d_feat=self.d_feat, + hidden_size=self.hidden_size, + num_layers=self.num_layers, + dropout=self.dropout, ).to(self.device) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.LSTM_model.parameters(), lr=self.lr) @@ -195,7 +198,11 @@ class LSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset, evals_result=dict(), verbose=True, save_path=None, + self, + dataset, + evals_result=dict(), + verbose=True, + save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -279,7 +286,11 @@ class LSTMModel(nn.Module): super().__init__() self.rnn = nn.LSTM( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_nn.py b/qlib/contrib/model/pytorch_nn.py index 8c1a77ec3..16fcea9ff 100644 --- a/qlib/contrib/model/pytorch_nn.py +++ b/qlib/contrib/model/pytorch_nn.py @@ -154,7 +154,11 @@ class DNNModelPytorch(Model): self.dnn_model.to(self.device) def fit( - self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, + self, + dataset: DatasetH, + evals_result=dict(), + verbose=True, + save_path=None, ): df_train, df_valid = dataset.prepare( ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L diff --git a/qlib/contrib/model/pytorch_sfm.py b/qlib/contrib/model/pytorch_sfm.py index 1f7433e05..d5169e6c7 100644 --- a/qlib/contrib/model/pytorch_sfm.py +++ b/qlib/contrib/model/pytorch_sfm.py @@ -30,7 +30,14 @@ from ...data.dataset.handler import DataHandlerLP class SFM_Model(nn.Module): def __init__( - self, d_feat=6, output_dim=1, freq_dim=10, hidden_size=64, dropout_W=0.0, dropout_U=0.0, device="cpu", + self, + d_feat=6, + output_dim=1, + freq_dim=10, + hidden_size=64, + dropout_W=0.0, + dropout_U=0.0, + device="cpu", ): super().__init__() @@ -355,11 +362,17 @@ class SFM(Model): self.train_optimizer.step() def fit( - self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, + self, + dataset: DatasetH, + evals_result=dict(), + verbose=True, + save_path=None, ): df_train, df_valid = dataset.prepare( - ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] diff --git a/qlib/contrib/model/pytorch_tabnet.py b/qlib/contrib/model/pytorch_tabnet.py index 18e9d8eb4..62e32d701 100644 --- a/qlib/contrib/model/pytorch_tabnet.py +++ b/qlib/contrib/model/pytorch_tabnet.py @@ -120,7 +120,9 @@ class TabnetModel(Model): os.makedirs("pretrain") [df_train, df_valid] = dataset.prepare( - ["pretrain", "pretrain_validation"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["pretrain", "pretrain_validation"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) df_train.fillna(df_train.mean(), inplace=True) @@ -154,7 +156,11 @@ class TabnetModel(Model): break def fit( - self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, + self, + dataset: DatasetH, + evals_result=dict(), + verbose=True, + save_path=None, ): if self.pretrain: # there is a pretrained model, load the model @@ -166,7 +172,9 @@ class TabnetModel(Model): # adding one more linear layer to fit the final output dimension self.tabnet_model = FinetuneModel(self.out_dim, self.final_out_dim, self.tabnet_model).to(self.device) df_train, df_valid = dataset.prepare( - ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) df_train.fillna(df_train.mean(), inplace=True) x_train, y_train = df_train["feature"], df_train["label"] diff --git a/qlib/contrib/model/xgboost.py b/qlib/contrib/model/xgboost.py index e37725c2e..ba2e5789b 100755 --- a/qlib/contrib/model/xgboost.py +++ b/qlib/contrib/model/xgboost.py @@ -29,7 +29,9 @@ class XGBModel(Model): ): df_train, df_valid = dataset.prepare( - ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] diff --git a/qlib/contrib/online/executor.py b/qlib/contrib/online/executor.py index 52b868881..2bd0937a0 100644 --- a/qlib/contrib/online/executor.py +++ b/qlib/contrib/online/executor.py @@ -150,13 +150,21 @@ class SimulatorExecutor(BaseExecutor): if order.direction == Order.SELL: # sell print( "[I {:%Y-%m-%d}]: sell {}, price {:.2f}, amount {}, value {:.2f}.".format( - trade_date, order.stock_id, trade_price, order.deal_amount, trade_val, + trade_date, + order.stock_id, + trade_price, + order.deal_amount, + trade_val, ) ) else: print( "[I {:%Y-%m-%d}]: buy {}, price {:.2f}, amount {}, value {:.2f}.".format( - trade_date, order.stock_id, trade_price, order.deal_amount, trade_val, + trade_date, + order.stock_id, + trade_price, + order.deal_amount, + trade_val, ) ) @@ -263,13 +271,21 @@ def load_order_list(user_path, trade_date): for stock_id in order_dict["sell"]: amount, factor = order_dict["sell"][stock_id] order = Order( - stock_id=stock_id, amount=amount, trade_date=pd.Timestamp(trade_date), direction=Order.SELL, factor=factor, + stock_id=stock_id, + amount=amount, + trade_date=pd.Timestamp(trade_date), + direction=Order.SELL, + factor=factor, ) order_list.append(order) for stock_id in order_dict["buy"]: amount, factor = order_dict["buy"][stock_id] order = Order( - stock_id=stock_id, amount=amount, trade_date=pd.Timestamp(trade_date), direction=Order.BUY, factor=factor, + stock_id=stock_id, + amount=amount, + trade_date=pd.Timestamp(trade_date), + direction=Order.BUY, + factor=factor, ) order_list.append(order) return order_list diff --git a/qlib/contrib/online/manager.py b/qlib/contrib/online/manager.py index a4476709d..cf850b9da 100644 --- a/qlib/contrib/online/manager.py +++ b/qlib/contrib/online/manager.py @@ -84,10 +84,12 @@ class UserManager: raise ValueError("Cannot find user {}".format(user_id)) self.users[user_id].account.save_account(self.data_path / user_id) save_instance( - self.users[user_id].strategy, self.data_path / user_id / "strategy_{}.pickle".format(user_id), + self.users[user_id].strategy, + self.data_path / user_id / "strategy_{}.pickle".format(user_id), ) save_instance( - self.users[user_id].model, self.data_path / user_id / "model_{}.pickle".format(user_id), + self.users[user_id].model, + self.data_path / user_id / "model_{}.pickle".format(user_id), ) def add_user(self, user_id, config_file, add_date): diff --git a/qlib/contrib/online/operator.py b/qlib/contrib/online/operator.py index c82deb394..c8b44f578 100644 --- a/qlib/contrib/online/operator.py +++ b/qlib/contrib/online/operator.py @@ -125,7 +125,9 @@ class Operator: trade_date=trade_date, ) save_order_list( - order_list=order_list, user_path=(pathlib.Path(path) / user_id), trade_date=trade_date, + order_list=order_list, + user_path=(pathlib.Path(path) / user_id), + trade_date=trade_date, ) self.logger.info("Generate order list at {} for {}".format(trade_date, user_id)) um.save_user_data(user_id) @@ -158,7 +160,9 @@ class Operator: order_list = load_order_list(user_path=(pathlib.Path(path) / user_id), trade_date=trade_date) trade_info = executor.execute(order_list=order_list, trade_account=user.account, trade_date=trade_date) executor.save_executed_file_from_trade_info( - trade_info=trade_info, user_path=(pathlib.Path(path) / user_id), trade_date=trade_date, + trade_info=trade_info, + user_path=(pathlib.Path(path) / user_id), + trade_date=trade_date, ) self.logger.info("execute order list at {} for {}".format(trade_date.date(), user_id)) diff --git a/qlib/contrib/online/utils.py b/qlib/contrib/online/utils.py index fb96c87bd..611af63e4 100644 --- a/qlib/contrib/online/utils.py +++ b/qlib/contrib/online/utils.py @@ -79,7 +79,11 @@ def prepare(um, today, user_id, exchange_config=None): log.warning("user_id:{}, last trading date {} after today {}".format(user_id, latest_trading_date, today)) return [pd.Timestamp(latest_trading_date)], None - dates = D.calendar(start_time=pd.Timestamp(latest_trading_date), end_time=pd.Timestamp(today), future=True,) + dates = D.calendar( + start_time=pd.Timestamp(latest_trading_date), + end_time=pd.Timestamp(today), + future=True, + ) dates = list(dates) dates.append(get_next_trading_date(dates[-1], future=True)) if exchange_config: diff --git a/qlib/contrib/report/analysis_model/analysis_model_performance.py b/qlib/contrib/report/analysis_model/analysis_model_performance.py index ef1447a12..1cb14d261 100644 --- a/qlib/contrib/report/analysis_model/analysis_model_performance.py +++ b/qlib/contrib/report/analysis_model/analysis_model_performance.py @@ -53,7 +53,8 @@ def _group_return(pred_label: pd.DataFrame = None, reverse: bool = False, N: int t_df.index = t_df.index.strftime("%Y-%m-%d") # Cumulative Return By Group group_scatter_figure = ScatterGraph( - t_df.cumsum(), layout=dict(title="Cumulative Return", xaxis=dict(type="category", tickangle=45)), + t_df.cumsum(), + layout=dict(title="Cumulative Return", xaxis=dict(type="category", tickangle=45)), ).figure t_df = t_df.loc[:, ["long-short", "long-average"]] @@ -61,7 +62,12 @@ def _group_return(pred_label: pd.DataFrame = None, reverse: bool = False, N: int group_hist_figure = SubplotsGraph( t_df, kind_map=dict(kind="DistplotGraph", kwargs=dict(bin_size=_bin_size)), - subplots_kwargs=dict(rows=1, cols=2, print_grid=False, subplot_titles=["long-short", "long-average"],), + subplots_kwargs=dict( + rows=1, + cols=2, + print_grid=False, + subplot_titles=["long-short", "long-average"], + ), ).figure return group_scatter_figure, group_hist_figure @@ -96,12 +102,15 @@ def _pred_ic(pred_label: pd.DataFrame = None, rank: bool = False, **kwargs) -> t _index = ic.index.get_level_values(0).astype("str").str.replace("-", "").str.slice(0, 6) _monthly_ic = ic.groupby(_index).mean() _monthly_ic.index = pd.MultiIndex.from_arrays( - [_monthly_ic.index.str.slice(0, 4), _monthly_ic.index.str.slice(4, 6)], names=["year", "month"], + [_monthly_ic.index.str.slice(0, 4), _monthly_ic.index.str.slice(4, 6)], + names=["year", "month"], ) # fill month _month_list = pd.date_range( - start=pd.Timestamp(f"{_index.min()[:4]}0101"), end=pd.Timestamp(f"{_index.max()[:4]}1231"), freq="1M", + start=pd.Timestamp(f"{_index.min()[:4]}0101"), + end=pd.Timestamp(f"{_index.max()[:4]}1231"), + freq="1M", ) _years = [] _month = [] @@ -133,15 +142,32 @@ def _pred_ic(pred_label: pd.DataFrame = None, rank: bool = False, **kwargs) -> t _bin_size = ((_ic_df.max() - _ic_df.min()) / 20).min() _sub_graph_data = [ - ("ic", dict(row=1, col=1, name="", kind="DistplotGraph", graph_kwargs=dict(bin_size=_bin_size),),), + ( + "ic", + dict( + row=1, + col=1, + name="", + kind="DistplotGraph", + graph_kwargs=dict(bin_size=_bin_size), + ), + ), (_qqplot_fig, dict(row=1, col=2)), ] ic_hist_figure = SubplotsGraph( _ic_df.dropna(), kind_map=dict(kind="HistogramGraph", kwargs=dict()), - subplots_kwargs=dict(rows=1, cols=2, print_grid=False, subplot_titles=["IC", "IC %s Dist. Q-Q" % dist_name],), + subplots_kwargs=dict( + rows=1, + cols=2, + print_grid=False, + subplot_titles=["IC", "IC %s Dist. Q-Q" % dist_name], + ), sub_graph_data=_sub_graph_data, - layout=dict(yaxis2=dict(title="Observed Quantile"), xaxis2=dict(title=f"{dist_name} Distribution Quantile"),), + layout=dict( + yaxis2=dict(title="Observed Quantile"), + xaxis2=dict(title=f"{dist_name} Distribution Quantile"), + ), ).figure return ic_bar_figure, ic_heatmap_figure, ic_hist_figure @@ -155,7 +181,8 @@ def _pred_autocorr(pred_label: pd.DataFrame, lag=1, **kwargs) -> tuple: _df = ac.to_frame("value") _df.index = _df.index.strftime("%Y-%m-%d") ac_figure = ScatterGraph( - _df, layout=dict(title="Auto Correlation", xaxis=dict(type="category", tickangle=45)), + _df, + layout=dict(title="Auto Correlation", xaxis=dict(type="category", tickangle=45)), ).figure return (ac_figure,) @@ -175,11 +202,17 @@ def _pred_turnover(pred_label: pd.DataFrame, N=5, lag=1, **kwargs) -> tuple: .sum() / (len(x) // N) ) - r_df = pd.DataFrame({"Top": top, "Bottom": bottom,}) + r_df = pd.DataFrame( + { + "Top": top, + "Bottom": bottom, + } + ) # FIXME: support HIGH-FREQ r_df.index = r_df.index.strftime("%Y-%m-%d") turnover_figure = ScatterGraph( - r_df, layout=dict(title="Top-Bottom Turnover", xaxis=dict(type="category", tickangle=45)), + r_df, + layout=dict(title="Top-Bottom Turnover", xaxis=dict(type="category", tickangle=45)), ).figure return (turnover_figure,) @@ -197,7 +230,11 @@ def ic_figure(ic_df: pd.DataFrame, show_nature_day=True, **kwargs) -> go.Figure: # FIXME: support HIGH-FREQ ic_df.index = ic_df.index.strftime("%Y-%m-%d") ic_bar_figure = BarGraph( - ic_df, layout=dict(title="Information Coefficient (IC)", xaxis=dict(type="category", tickangle=45),), + ic_df, + layout=dict( + title="Information Coefficient (IC)", + xaxis=dict(type="category", tickangle=45), + ), ).figure return ic_bar_figure @@ -240,7 +277,12 @@ def model_performance_graph( figure_list = [] for graph_name in graph_names: fun_res = eval(f"_{graph_name}")( - pred_label=pred_label, lag=lag, N=N, reverse=reverse, rank=rank, show_nature_day=show_nature_day, + pred_label=pred_label, + lag=lag, + N=N, + reverse=reverse, + rank=rank, + show_nature_day=show_nature_day, ) figure_list += fun_res diff --git a/qlib/contrib/report/analysis_position/cumulative_return.py b/qlib/contrib/report/analysis_position/cumulative_return.py index 604189c94..abb68ea60 100644 --- a/qlib/contrib/report/analysis_position/cumulative_return.py +++ b/qlib/contrib/report/analysis_position/cumulative_return.py @@ -13,7 +13,11 @@ from ..analysis_position.parse_position import get_position_data def _get_cum_return_data_with_position( - position: dict, report_normal: pd.DataFrame, label_data: pd.DataFrame, start_date=None, end_date=None, + position: dict, + report_normal: pd.DataFrame, + label_data: pd.DataFrame, + start_date=None, + end_date=None, ): """ @@ -25,7 +29,11 @@ def _get_cum_return_data_with_position( :return: """ _cumulative_return_df = get_position_data( - position=position, report_normal=report_normal, label_data=label_data, start_date=start_date, end_date=end_date, + position=position, + report_normal=report_normal, + label_data=label_data, + start_date=start_date, + end_date=end_date, ).copy() _cumulative_return_df["label"] = _cumulative_return_df["label"] - _cumulative_return_df["bench"] @@ -79,7 +87,11 @@ def _get_cum_return_data_with_position( def _get_figure_with_position( - position: dict, report_normal: pd.DataFrame, label_data: pd.DataFrame, start_date=None, end_date=None, + position: dict, + report_normal: pd.DataFrame, + label_data: pd.DataFrame, + start_date=None, + end_date=None, ) -> Iterable[go.Figure]: """Get average analysis figures @@ -99,12 +111,18 @@ def _get_figure_with_position( # Create figures for _t_name in ["buy", "sell", "buy_minus_sell", "hold"]: sub_graph_data = [ - ("cum_{}".format(_t_name), dict(row=1, col=1, graph_kwargs={"mode": "lines+markers", "xaxis": "x3"}),), + ( + "cum_{}".format(_t_name), + dict(row=1, col=1, graph_kwargs={"mode": "lines+markers", "xaxis": "x3"}), + ), ( "{}_weight".format(_t_name.replace("minus", "plus") if "minus" in _t_name else _t_name), dict(row=2, col=1), ), - ("{}_value".format(_t_name), dict(row=1, col=2, kind="HistogramGraph", graph_kwargs={}),), + ( + "{}_value".format(_t_name), + dict(row=1, col=2, kind="HistogramGraph", graph_kwargs={}), + ), ] _default_xaxis = dict(showline=False, zeroline=True, tickangle=45) @@ -143,7 +161,13 @@ def _get_figure_with_position( [{"rowspan": 1}, None], ] subplots_kwargs = dict( - vertical_spacing=0.01, rows=2, cols=2, row_width=[1, 2], column_width=[3, 1], print_grid=False, specs=specs, + vertical_spacing=0.01, + rows=2, + cols=2, + row_width=[1, 2], + column_width=[3, 1], + print_grid=False, + specs=specs, ) yield SubplotsGraph( cum_return_df, diff --git a/qlib/contrib/report/analysis_position/parse_position.py b/qlib/contrib/report/analysis_position/parse_position.py index 23f9c592c..fe1d61137 100644 --- a/qlib/contrib/report/analysis_position/parse_position.py +++ b/qlib/contrib/report/analysis_position/parse_position.py @@ -72,7 +72,10 @@ def parse_position(position: dict = None) -> pd.DataFrame: result_df = result_df.append(_trading_day_df, sort=True) - previous_data = dict(date=_trading_date, code_list=_trading_day_df[_trading_day_df["status"] != -1].index,) + previous_data = dict( + date=_trading_date, + code_list=_trading_day_df[_trading_day_df["status"] != -1].index, + ) result_df.reset_index(inplace=True) result_df.rename(columns={"date": "datetime", "index": "instrument"}, inplace=True) diff --git a/qlib/contrib/report/analysis_position/rank_label.py b/qlib/contrib/report/analysis_position/rank_label.py index 9a4d834ed..72a358adc 100644 --- a/qlib/contrib/report/analysis_position/rank_label.py +++ b/qlib/contrib/report/analysis_position/rank_label.py @@ -23,7 +23,11 @@ def _get_figure_with_position( :return: """ _position_df = get_position_data( - position, label_data, calculate_label_rank=True, start_date=start_date, end_date=end_date, + position, + label_data, + calculate_label_rank=True, + start_date=start_date, + end_date=end_date, ) res_dict = dict() @@ -47,14 +51,20 @@ def _get_figure_with_position( yield ScatterGraph( _res_df.loc[:, [_col]], layout=dict( - title=_col, xaxis=dict(type="category", tickangle=45), yaxis=dict(title="lable-rank-ratio: %"), + title=_col, + xaxis=dict(type="category", tickangle=45), + yaxis=dict(title="lable-rank-ratio: %"), ), graph_kwargs=dict(mode="lines+markers"), ).figure def rank_label_graph( - position: dict, label_data: pd.DataFrame, start_date=None, end_date=None, show_notebook=True, + position: dict, + label_data: pd.DataFrame, + start_date=None, + end_date=None, + show_notebook=True, ) -> Iterable[go.Figure]: """Ranking percentage of stocks buy, sell, and holding on the trading day. Average rank-ratio(similar to **sell_df['label'].rank(ascending=False) / len(sell_df)**) of daily trading diff --git a/qlib/contrib/report/analysis_position/report.py b/qlib/contrib/report/analysis_position/report.py index 8e2c05c0a..f82e654c4 100644 --- a/qlib/contrib/report/analysis_position/report.py +++ b/qlib/contrib/report/analysis_position/report.py @@ -123,7 +123,9 @@ def _report_figure(df: pd.DataFrame) -> [list, tuple]: "y1": 1, "fillcolor": "#d3d3d3", "opacity": 0.3, - "line": {"width": 0,}, + "line": { + "width": 0, + }, }, { "type": "rect", @@ -135,13 +137,20 @@ def _report_figure(df: pd.DataFrame) -> [list, tuple]: "y1": 0.55, "fillcolor": "#d3d3d3", "opacity": 0.3, - "line": {"width": 0,}, + "line": { + "width": 0, + }, }, ], ) _subplot_kwargs = dict( - shared_xaxes=True, vertical_spacing=0.01, rows=7, cols=1, row_width=[1, 1, 1, 3, 1, 1, 3], print_grid=False, + shared_xaxes=True, + vertical_spacing=0.01, + rows=7, + cols=1, + row_width=[1, 1, 1, 3, 1, 1, 3], + print_grid=False, ) figure = SubplotsGraph( df=report_df, diff --git a/qlib/contrib/report/graph.py b/qlib/contrib/report/graph.py index dbbc41110..70e382fb1 100644 --- a/qlib/contrib/report/graph.py +++ b/qlib/contrib/report/graph.py @@ -311,7 +311,11 @@ class SubplotsGraph: _temp_row_data = ( column_name, dict( - row=row, col=col, name=res_name, kind=self._kind_map["kind"], graph_kwargs=self._kind_map["kwargs"], + row=row, + col=col, + name=res_name, + kind=self._kind_map["kind"], + graph_kwargs=self._kind_map["kwargs"], ), ) self._sub_graph_data.append(_temp_row_data) diff --git a/qlib/contrib/strategy/cost_control.py b/qlib/contrib/strategy/cost_control.py index ee3ee03ec..dd90437b0 100644 --- a/qlib/contrib/strategy/cost_control.py +++ b/qlib/contrib/strategy/cost_control.py @@ -57,7 +57,10 @@ class SoftTopkStrategy(WeightStrategyBase): final_stock_weight[stock_id] -= sw if self.buy_method == "first_fill": for stock_id in buy_signal_stocks: - add_weight = min(max(1 / self.topk - final_stock_weight.get(stock_id, 0), 0.0), sold_stock_weight,) + add_weight = min( + max(1 / self.topk - final_stock_weight.get(stock_id, 0), 0.0), + sold_stock_weight, + ) final_stock_weight[stock_id] = final_stock_weight.get(stock_id, 0.0) + add_weight sold_stock_weight -= add_weight elif self.buy_method == "average_fill": diff --git a/qlib/contrib/strategy/order_generator.py b/qlib/contrib/strategy/order_generator.py index 6f168b4dd..494981ecc 100644 --- a/qlib/contrib/strategy/order_generator.py +++ b/qlib/contrib/strategy/order_generator.py @@ -102,10 +102,14 @@ class OrderGenWInteract(OrderGenerator): # strategy 1 : generate amount_position by weight_position # Use API in Exchange() target_amount_dict = trade_exchange.generate_amount_position_from_weight_position( - weight_position=target_weight_position, cash=current_tradable_value, trade_date=trade_date, + weight_position=target_weight_position, + cash=current_tradable_value, + trade_date=trade_date, ) order_list = trade_exchange.generate_order_for_target_amount_position( - target_position=target_amount_dict, current_position=current_amount_dict, trade_date=trade_date, + target_position=target_amount_dict, + current_position=current_amount_dict, + trade_date=trade_date, ) return order_list @@ -160,6 +164,8 @@ class OrderGenWOInteract(OrderGenerator): else: continue order_list = trade_exchange.generate_order_for_target_amount_position( - target_position=amount_dict, current_position=current.get_stock_amount_dict(), trade_date=trade_date, + target_position=amount_dict, + current_position=current.get_stock_amount_dict(), + trade_date=trade_date, ) return order_list diff --git a/qlib/contrib/tuner/launcher.py b/qlib/contrib/tuner/launcher.py index 409410a2a..711658c9a 100644 --- a/qlib/contrib/tuner/launcher.py +++ b/qlib/contrib/tuner/launcher.py @@ -13,7 +13,11 @@ from .config import TunerConfigManager args_parser = argparse.ArgumentParser(prog="tuner") args_parser.add_argument( - "-c", "--config_path", required=True, type=str, help="config path indicates where to load yaml config.", + "-c", + "--config_path", + required=True, + type=str, + help="config path indicates where to load yaml config.", ) args = args_parser.parse_args() diff --git a/qlib/contrib/tuner/space.py b/qlib/contrib/tuner/space.py index 57f57a6c3..76f101671 100644 --- a/qlib/contrib/tuner/space.py +++ b/qlib/contrib/tuner/space.py @@ -10,5 +10,8 @@ TopkAmountStrategySpace = { } QLibDataLabelSpace = { - "labels": hp.choice("labels", [["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["Ref($close, -5)/$close - 1"]],) + "labels": hp.choice( + "labels", + [["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["Ref($close, -5)/$close - 1"]], + ) } diff --git a/qlib/contrib/tuner/tuner.py b/qlib/contrib/tuner/tuner.py index e81d41a9a..2ce957859 100644 --- a/qlib/contrib/tuner/tuner.py +++ b/qlib/contrib/tuner/tuner.py @@ -28,7 +28,10 @@ class Tuner: self.optim_config = optim_config self.max_evals = self.tuner_config.get("max_evals", 10) - self.ex_dir = os.path.join(self.tuner_config["experiment"]["dir"], self.tuner_config["experiment"]["name"],) + self.ex_dir = os.path.join( + self.tuner_config["experiment"]["dir"], + self.tuner_config["experiment"]["name"], + ) self.best_params = None self.best_res = None @@ -39,7 +42,10 @@ class Tuner: TimeInspector.set_time_mark() fmin( - fn=self.objective, space=self.space, algo=tpe.suggest, max_evals=self.max_evals, + fn=self.objective, + space=self.space, + algo=tpe.suggest, + max_evals=self.max_evals, ) self.logger.info("Local best params: {} ".format(self.best_params)) TimeInspector.log_cost_time( @@ -153,7 +159,8 @@ class QLibTuner(Tuner): estimator_config["data"]["args"].update(params["data_label_space"]) estimator_path = os.path.join( - self.tuner_config["experiment"].get("dir", "../"), QLibTuner.ESTIMATOR_CONFIG_NAME, + self.tuner_config["experiment"].get("dir", "../"), + QLibTuner.ESTIMATOR_CONFIG_NAME, ) with open(estimator_path, "w") as fp: @@ -166,20 +173,27 @@ class QLibTuner(Tuner): model_space_name = self.tuner_config["model"].get("space", None) if model_space_name is None: raise ValueError("Please give the search space of model.") - model_space = getattr(importlib.import_module(".space", package="qlib.contrib.tuner"), model_space_name,) + model_space = getattr( + importlib.import_module(".space", package="qlib.contrib.tuner"), + model_space_name, + ) # 2. Setup strategy space strategy_space_name = self.tuner_config["strategy"].get("space", None) if strategy_space_name is None: raise ValueError("Please give the search space of strategy.") - strategy_space = getattr(importlib.import_module(".space", package="qlib.contrib.tuner"), strategy_space_name,) + strategy_space = getattr( + importlib.import_module(".space", package="qlib.contrib.tuner"), + strategy_space_name, + ) # 3. Setup data label space if given if self.tuner_config.get("data_label", None) is not None: data_label_space_name = self.tuner_config["data_label"].get("space", None) if data_label_space_name is not None: data_label_space = getattr( - importlib.import_module(".space", package="qlib.contrib.tuner"), data_label_space_name, + importlib.import_module(".space", package="qlib.contrib.tuner"), + data_label_space_name, ) else: data_label_space_name = None diff --git a/qlib/data/client.py b/qlib/data/client.py index d1a68cb38..5244a7e45 100644 --- a/qlib/data/client.py +++ b/qlib/data/client.py @@ -26,7 +26,8 @@ class Client: self.logger = get_module_logger(self.__class__.__name__) # bind connect/disconnect callbacks self.sio.on( - "connect", lambda: self.logger.debug("Connect to server {}".format(self.sio.connection_url)), + "connect", + lambda: self.logger.debug("Connect to server {}".format(self.sio.connection_url)), ) self.sio.on("disconnect", lambda: self.logger.debug("Disconnect from server!")) diff --git a/qlib/data/data.py b/qlib/data/data.py index 47cded79c..762467da3 100644 --- a/qlib/data/data.py +++ b/qlib/data/data.py @@ -328,7 +328,14 @@ class DatasetProvider(abc.ABC): raise NotImplementedError("Subclass of DatasetProvider must implement `Dataset` method") def _uri( - self, instruments, fields, start_time=None, end_time=None, freq="day", disk_cache=1, **kwargs, + self, + instruments, + fields, + start_time=None, + end_time=None, + freq="day", + disk_cache=1, + **kwargs, ): """Get task uri, used when generating rabbitmq task in qlib_server @@ -407,13 +414,29 @@ class DatasetProvider(abc.ABC): for inst, spans in instruments_d.items(): data[inst] = p.apply_async( DatasetProvider.expression_calculator, - args=(inst, start_time, end_time, freq, normalize_column_names, spans, C,), + args=( + inst, + start_time, + end_time, + freq, + normalize_column_names, + spans, + C, + ), ) else: for inst in instruments_d: data[inst] = p.apply_async( DatasetProvider.expression_calculator, - args=(inst, start_time, end_time, freq, normalize_column_names, None, C,), + args=( + inst, + start_time, + end_time, + freq, + normalize_column_names, + None, + C, + ), ) p.close() @@ -575,7 +598,12 @@ class LocalInstrumentProvider(InstrumentProvider): start_time = pd.Timestamp(start_time or cal[0]) end_time = pd.Timestamp(end_time or cal[-1]) _instruments_filtered = { - inst: list(filter(lambda x: x[0] <= x[1], [(max(start_time, x[0]), min(end_time, x[1])) for x in spans],)) + inst: list( + filter( + lambda x: x[0] <= x[1], + [(max(start_time, x[0]), min(end_time, x[1])) for x in spans], + ) + ) for inst, spans in _instruments.items() } _instruments_filtered = {key: value for key, value in _instruments_filtered.items() if value} @@ -695,7 +723,14 @@ class LocalDatasetProvider(DatasetProvider): for inst in instruments_d: p.apply_async( - LocalDatasetProvider.cache_walker, args=(inst, start_time, end_time, freq, column_names,), + LocalDatasetProvider.cache_walker, + args=( + inst, + start_time, + end_time, + freq, + column_names, + ), ) p.close() @@ -728,7 +763,12 @@ class ClientCalendarProvider(CalendarProvider): def calendar(self, start_time=None, end_time=None, freq="day", future=False): self.conn.send_request( request_type="calendar", - request_content={"start_time": str(start_time), "end_time": str(end_time), "freq": freq, "future": future,}, + request_content={ + "start_time": str(start_time), + "end_time": str(end_time), + "freq": freq, + "future": future, + }, msg_queue=self.queue, msg_proc_func=lambda response_content: [pd.Timestamp(c) for c in response_content], ) @@ -792,7 +832,14 @@ class ClientDatasetProvider(DatasetProvider): self.queue = queue.Queue() def dataset( - self, instruments, fields, start_time=None, end_time=None, freq="day", disk_cache=0, return_uri=False, + self, + instruments, + fields, + start_time=None, + end_time=None, + freq="day", + disk_cache=0, + return_uri=False, ): if Inst.get_inst_type(instruments) == Inst.DICT: get_module_logger("data").warning( @@ -895,7 +942,13 @@ class BaseProvider: return Inst.list_instruments(instruments, start_time, end_time, freq, as_list) def features( - self, instruments, fields, start_time=None, end_time=None, freq="day", disk_cache=None, + self, + instruments, + fields, + start_time=None, + end_time=None, + freq="day", + disk_cache=None, ): """ Parameters: diff --git a/qlib/data/dataset/utils.py b/qlib/data/dataset/utils.py index 58e2bd968..feda19044 100644 --- a/qlib/data/dataset/utils.py +++ b/qlib/data/dataset/utils.py @@ -32,7 +32,10 @@ def get_level_index(df: pd.DataFrame, level=Union[str, int]) -> int: def fetch_df_by_index( - df: pd.DataFrame, selector: Union[pd.Timestamp, slice, str, list], level: Union[str, int], fetch_orig=True, + df: pd.DataFrame, + selector: Union[pd.Timestamp, slice, str, list], + level: Union[str, int], + fetch_orig=True, ) -> pd.DataFrame: """ fetch data from `data` with `selector` and `level` diff --git a/qlib/data/filter.py b/qlib/data/filter.py index 811fd387f..70f9d3278 100644 --- a/qlib/data/filter.py +++ b/qlib/data/filter.py @@ -341,7 +341,12 @@ class ExpressionDFilter(SeriesDFilter): # do not use dataset cache try: _features = DatasetD.dataset( - instruments, [self.rule_expression], fstart, fend, freq=self.filter_freq, disk_cache=0, + instruments, + [self.rule_expression], + fstart, + fend, + freq=self.filter_freq, + disk_cache=0, ) except TypeError: # use LocalDatasetProvider diff --git a/qlib/tests/__init__.py b/qlib/tests/__init__.py index eb6f9c5ed..f92e72787 100644 --- a/qlib/tests/__init__.py +++ b/qlib/tests/__init__.py @@ -18,6 +18,10 @@ class TestAutoData(unittest.TestCase): print(f"Qlib data is not found in {provider_uri}") GetData().qlib_data( - name="qlib_data_simple", region="cn", interval="1d", target_dir=provider_uri, delete_old=False, + name="qlib_data_simple", + region="cn", + interval="1d", + target_dir=provider_uri, + delete_old=False, ) init(provider_uri=provider_uri, region=REG_CN, **cls._setup_kwargs) diff --git a/qlib/workflow/record_temp.py b/qlib/workflow/record_temp.py index 0c704b896..be458a24d 100644 --- a/qlib/workflow/record_temp.py +++ b/qlib/workflow/record_temp.py @@ -193,7 +193,10 @@ class SigAnaRecord(SignalRecord): } ) objects.update( - {"long_short_r.pkl": long_short_r, "long_avg_r.pkl": long_avg_r,} + { + "long_short_r.pkl": long_short_r, + "long_avg_r.pkl": long_avg_r, + } ) self.recorder.log_metrics(**metrics) self.recorder.save_objects(**objects, artifact_path=self.get_path()) From dc4aa675034724a9d2815763fd575b3ec56e76e2 Mon Sep 17 00:00:00 2001 From: Jactus Date: Mon, 22 Feb 2021 11:42:36 +0800 Subject: [PATCH 022/134] Black format --- docs/conf.py | 10 ++++++- examples/benchmarks/TFT/libs/tft_model.py | 12 ++++++-- examples/highfreq/highfreq_handler.py | 33 ++++++++++++++++----- examples/highfreq/highfreq_processor.py | 4 ++- examples/highfreq/workflow.py | 35 +++++++++++++++++++---- examples/run_all_model.py | 5 +++- examples/workflow_by_code.py | 5 +++- scripts/data_collector/yahoo/collector.py | 27 +++++++++++++---- scripts/dump_bin.py | 13 +++++++-- setup.py | 14 +++++++-- tests/test_all_pipeline.py | 9 ++++-- tests/test_dump_data.py | 9 ++++-- tests/test_get_data.py | 4 ++- 13 files changed, 147 insertions(+), 33 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 61fe784e7..6e52b0e34 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -191,7 +191,15 @@ man_pages = [(master_doc, "qlib", u"QLib Documentation", [author], 1)] # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ - (master_doc, "QLib", u"QLib Documentation", author, "QLib", "One line description of project.", "Miscellaneous",), + ( + master_doc, + "QLib", + u"QLib Documentation", + author, + "QLib", + "One line description of project.", + "Miscellaneous", + ), ] diff --git a/examples/benchmarks/TFT/libs/tft_model.py b/examples/benchmarks/TFT/libs/tft_model.py index f40a1aece..b39f17825 100644 --- a/examples/benchmarks/TFT/libs/tft_model.py +++ b/examples/benchmarks/TFT/libs/tft_model.py @@ -721,7 +721,12 @@ class TemporalFusionTransformer: encoder_steps = self.num_encoder_steps # Inputs. - all_inputs = tf.keras.layers.Input(shape=(time_steps, combined_input_size,)) + all_inputs = tf.keras.layers.Input( + shape=( + time_steps, + combined_input_size, + ) + ) unknown_inputs, known_combined_layer, obs_inputs, static_inputs = self.get_tft_embeddings(all_inputs) @@ -861,7 +866,10 @@ class TemporalFusionTransformer: """Returns LSTM cell initialized with default parameters.""" if self.use_cudnn: lstm = tf.keras.layers.CuDNNLSTM( - self.hidden_layer_size, return_sequences=True, return_state=return_state, stateful=False, + self.hidden_layer_size, + return_sequences=True, + return_state=return_state, + stateful=False, ) else: lstm = tf.keras.layers.LSTM( diff --git a/examples/highfreq/highfreq_handler.py b/examples/highfreq/highfreq_handler.py index 2fc411ab6..d35650514 100644 --- a/examples/highfreq/highfreq_handler.py +++ b/examples/highfreq/highfreq_handler.py @@ -20,7 +20,10 @@ class HighFreqHandler(DataHandlerLP): new_l = [] for p in proc_l: p["kwargs"].update( - {"fit_start_time": fit_start_time, "fit_end_time": fit_end_time,} + { + "fit_start_time": fit_start_time, + "fit_end_time": fit_end_time, + } ) new_l.append(p) return new_l @@ -30,7 +33,11 @@ class HighFreqHandler(DataHandlerLP): data_loader = { "class": "QlibDataLoader", - "kwargs": {"config": self.get_feature_config(), "swap_level": False, "freq": "1min",}, + "kwargs": { + "config": self.get_feature_config(), + "swap_level": False, + "freq": "1min", + }, } super().__init__( instruments=instruments, @@ -61,7 +68,8 @@ class HighFreqHandler(DataHandlerLP): feature_ops = template_norm.format( template_if.format( - template_fillnan.format(template_paused.format("$close")), template_paused.format(price_field), + template_fillnan.format(template_paused.format("$close")), + template_paused.format(price_field), ), template_fillnan.format(template_paused.format("$close")), ) @@ -111,14 +119,24 @@ class HighFreqHandler(DataHandlerLP): class HighFreqBacktestHandler(DataHandler): def __init__( - self, instruments="csi300", start_time=None, end_time=None, + self, + instruments="csi300", + start_time=None, + end_time=None, ): data_loader = { "class": "QlibDataLoader", - "kwargs": {"config": self.get_feature_config(), "swap_level": False, "freq": "1min",}, + "kwargs": { + "config": self.get_feature_config(), + "swap_level": False, + "freq": "1min", + }, } super().__init__( - instruments=instruments, start_time=start_time, end_time=end_time, data_loader=data_loader, + instruments=instruments, + start_time=start_time, + end_time=end_time, + data_loader=data_loader, ) def get_feature_config(self): @@ -137,7 +155,8 @@ class HighFreqBacktestHandler(DataHandler): fields += [ "Cut({0}, 240, None)".format( template_if.format( - template_fillnan.format(template_paused.format("$close")), template_paused.format(simpson_vwap), + template_fillnan.format(template_paused.format("$close")), + template_paused.format(simpson_vwap), ) ) ] diff --git a/examples/highfreq/highfreq_processor.py b/examples/highfreq/highfreq_processor.py index 73510ef06..f0ab0dec2 100644 --- a/examples/highfreq/highfreq_processor.py +++ b/examples/highfreq/highfreq_processor.py @@ -65,6 +65,8 @@ class HighFreqNorm(Processor): feat = df_values[:, [0, 1, 2, 3, 4, 10]].reshape(-1, 6 * 240) feat_1 = df_values[:, [5, 6, 7, 8, 9, 11]].reshape(-1, 6 * 240) df_new_features = pd.DataFrame( - data=np.concatenate((feat, feat_1), axis=1), index=idx, columns=["FEATURE_%d" % i for i in range(12 * 240)], + data=np.concatenate((feat, feat_1), axis=1), + index=idx, + columns=["FEATURE_%d" % i for i in range(12 * 240)], ).sort_index() return df_new_features diff --git a/examples/highfreq/workflow.py b/examples/highfreq/workflow.py index 0bfd0c2a0..01de59c0e 100644 --- a/examples/highfreq/workflow.py +++ b/examples/highfreq/workflow.py @@ -63,7 +63,13 @@ class HighfreqWorkflow(object): "module_path": "highfreq_handler", "kwargs": DATA_HANDLER_CONFIG0, }, - "segments": {"train": (start_time, train_end_time), "test": (test_start_time, end_time,),}, + "segments": { + "train": (start_time, train_end_time), + "test": ( + test_start_time, + end_time, + ), + }, }, }, "dataset_backtest": { @@ -75,7 +81,13 @@ class HighfreqWorkflow(object): "module_path": "highfreq_handler", "kwargs": DATA_HANDLER_CONFIG1, }, - "segments": {"train": (start_time, train_end_time), "test": (test_start_time, end_time,),}, + "segments": { + "train": (start_time, train_end_time), + "test": ( + test_start_time, + end_time, + ), + }, }, }, } @@ -140,11 +152,24 @@ class HighfreqWorkflow(object): "start_time": "2021-01-19 00:00:00", "end_time": "2021-01-25 16:00:00", }, - segment_kwargs={"test": ("2021-01-19 00:00:00", "2021-01-25 16:00:00",),}, + segment_kwargs={ + "test": ( + "2021-01-19 00:00:00", + "2021-01-25 16:00:00", + ), + }, ) dataset_backtest.init( - handler_kwargs={"start_time": "2021-01-19 00:00:00", "end_time": "2021-01-25 16:00:00",}, - segment_kwargs={"test": ("2021-01-19 00:00:00", "2021-01-25 16:00:00",),}, + handler_kwargs={ + "start_time": "2021-01-19 00:00:00", + "end_time": "2021-01-25 16:00:00", + }, + segment_kwargs={ + "test": ( + "2021-01-19 00:00:00", + "2021-01-25 16:00:00", + ), + }, ) ##=============get data============= diff --git a/examples/run_all_model.py b/examples/run_all_model.py index d356b4128..d587eff15 100644 --- a/examples/run_all_model.py +++ b/examples/run_all_model.py @@ -34,7 +34,10 @@ exp_path = str(Path(os.getcwd()).resolve() / exp_folder_name) exp_manager = { "class": "MLflowExpManager", "module_path": "qlib.workflow.expm", - "kwargs": {"uri": "file:" + exp_path, "default_exp_name": "Experiment",}, + "kwargs": { + "uri": "file:" + exp_path, + "default_exp_name": "Experiment", + }, } if not exists_qlib_data(provider_uri): print(f"Qlib data is not found in {provider_uri}") diff --git a/examples/workflow_by_code.py b/examples/workflow_by_code.py index 6f5c11dc0..d5dab8917 100644 --- a/examples/workflow_by_code.py +++ b/examples/workflow_by_code.py @@ -81,7 +81,10 @@ if __name__ == "__main__": "strategy": { "class": "TopkDropoutStrategy", "module_path": "qlib.contrib.strategy.strategy", - "kwargs": {"topk": 50, "n_drop": 5,}, + "kwargs": { + "topk": 50, + "n_drop": 5, + }, }, "backtest": { "verbose": False, diff --git a/scripts/data_collector/yahoo/collector.py b/scripts/data_collector/yahoo/collector.py index 24526e332..743f89462 100644 --- a/scripts/data_collector/yahoo/collector.py +++ b/scripts/data_collector/yahoo/collector.py @@ -39,7 +39,13 @@ class YahooData: INTERVAL_1d = "1d" def __init__( - self, timezone: str = None, start=None, end=None, interval="1d", delay=0, show_1min_logging: bool = False, + self, + timezone: str = None, + start=None, + end=None, + interval="1d", + delay=0, + show_1min_logging: bool = False, ): """ @@ -119,7 +125,11 @@ class YahooData: self._sleep() _remote_interval = "1m" if self._interval == self.INTERVAL_1min else self._interval return self.get_data_from_remote( - symbol, interval=_remote_interval, start=start_, end=end_, show_1min_logging=self._show_1min_logging, + symbol, + interval=_remote_interval, + start=start_, + end=end_, + show_1min_logging=self._show_1min_logging, ) _result = None @@ -428,7 +438,9 @@ class YahooNormalize: DAILY_FORMAT = "%Y-%m-%d" def __init__( - self, date_field_name: str = "date", symbol_field_name: str = "symbol", + self, + date_field_name: str = "date", + symbol_field_name: str = "symbol", ): """ @@ -446,7 +458,10 @@ class YahooNormalize: @staticmethod def normalize_yahoo( - df: pd.DataFrame, calendar_list: list = None, date_field_name: str = "date", symbol_field_name: str = "symbol", + df: pd.DataFrame, + calendar_list: list = None, + date_field_name: str = "date", + symbol_field_name: str = "symbol", ): if df.empty: return df @@ -551,7 +566,9 @@ class YahooNormalize1min(YahooNormalize, ABC): CONSISTENT_1d = False def __init__( - self, date_field_name: str = "date", symbol_field_name: str = "symbol", + self, + date_field_name: str = "date", + symbol_field_name: str = "symbol", ): """ diff --git a/scripts/dump_bin.py b/scripts/dump_bin.py index ab24fa9ca..4811fd486 100644 --- a/scripts/dump_bin.py +++ b/scripts/dump_bin.py @@ -153,13 +153,22 @@ class DumpDataBase: @staticmethod def _read_calendars(calendar_path: Path) -> List[pd.Timestamp]: - return sorted(map(pd.Timestamp, pd.read_csv(calendar_path, header=None).loc[:, 0].tolist(),)) + return sorted( + map( + pd.Timestamp, + pd.read_csv(calendar_path, header=None).loc[:, 0].tolist(), + ) + ) def _read_instruments(self, instrument_path: Path) -> pd.DataFrame: df = pd.read_csv( instrument_path, sep=self.INSTRUMENTS_SEP, - names=[self.symbol_field_name, self.INSTRUMENTS_START_FIELD, self.INSTRUMENTS_END_FIELD,], + names=[ + self.symbol_field_name, + self.INSTRUMENTS_START_FIELD, + self.INSTRUMENTS_END_FIELD, + ], ) return df diff --git a/setup.py b/setup.py index d8a9d9efa..83cf6e1b6 100644 --- a/setup.py +++ b/setup.py @@ -70,10 +70,16 @@ with open(os.path.join(here, "README.md"), encoding="utf-8") as f: # Cython Extensions extensions = [ Extension( - "qlib.data._libs.rolling", ["qlib/data/_libs/rolling.pyx"], language="c++", include_dirs=[NUMPY_INCLUDE], + "qlib.data._libs.rolling", + ["qlib/data/_libs/rolling.pyx"], + language="c++", + include_dirs=[NUMPY_INCLUDE], ), Extension( - "qlib.data._libs.expanding", ["qlib/data/_libs/expanding.pyx"], language="c++", include_dirs=[NUMPY_INCLUDE], + "qlib.data._libs.expanding", + ["qlib/data/_libs/expanding.pyx"], + language="c++", + include_dirs=[NUMPY_INCLUDE], ), ] @@ -92,7 +98,9 @@ setup( # py_modules=['qlib'], entry_points={ # 'console_scripts': ['mycli=mymodule:cli'], - "console_scripts": ["qrun=qlib.workflow.cli:run",], + "console_scripts": [ + "qrun=qlib.workflow.cli:run", + ], }, ext_modules=extensions, install_requires=REQUIRED, diff --git a/tests/test_all_pipeline.py b/tests/test_all_pipeline.py index 8b3819c83..f6e77cba4 100644 --- a/tests/test_all_pipeline.py +++ b/tests/test_all_pipeline.py @@ -78,7 +78,10 @@ port_analysis_config = { "strategy": { "class": "TopkDropoutStrategy", "module_path": "qlib.contrib.strategy.strategy", - "kwargs": {"topk": 50, "n_drop": 5,}, + "kwargs": { + "topk": 50, + "n_drop": 5, + }, }, "backtest": { "verbose": False, @@ -173,7 +176,9 @@ class TestAllFlow(TestAutoData): def test_1_backtest(self): analyze_df = backtest_analysis(TestAllFlow.PRED_SCORE, TestAllFlow.RID) self.assertGreaterEqual( - analyze_df.loc(axis=0)["excess_return_with_cost", "annualized_return"].values[0], 0.10, "backtest failed", + analyze_df.loc(axis=0)["excess_return_with_cost", "annualized_return"].values[0], + 0.10, + "backtest failed", ) diff --git a/tests/test_dump_data.py b/tests/test_dump_data.py index de649c37e..dfa7f8556 100644 --- a/tests/test_dump_data.py +++ b/tests/test_dump_data.py @@ -40,7 +40,9 @@ class TestDumpData(unittest.TestCase): TestDumpData.STOCK_NAMES = list(map(lambda x: x.name[:-4].upper(), SOURCE_DIR.glob("*.csv"))) provider_uri = str(QLIB_DIR.resolve()) qlib.init( - provider_uri=provider_uri, expression_cache=None, dataset_cache=None, + provider_uri=provider_uri, + expression_cache=None, + dataset_cache=None, ) @classmethod @@ -52,7 +54,10 @@ class TestDumpData(unittest.TestCase): def test_1_dump_calendars(self): ori_calendars = set( - map(pd.Timestamp, pd.read_csv(QLIB_DIR.joinpath("calendars", "day.txt"), header=None).loc[:, 0].values,) + map( + pd.Timestamp, + pd.read_csv(QLIB_DIR.joinpath("calendars", "day.txt"), header=None).loc[:, 0].values, + ) ) res_calendars = set(D.calendar()) assert len(ori_calendars - res_calendars) == len(res_calendars - ori_calendars) == 0, "dump calendars failed" diff --git a/tests/test_get_data.py b/tests/test_get_data.py index d5637b025..c511d1b91 100644 --- a/tests/test_get_data.py +++ b/tests/test_get_data.py @@ -26,7 +26,9 @@ class TestGetData(unittest.TestCase): def setUpClass(cls) -> None: provider_uri = str(QLIB_DIR.resolve()) qlib.init( - provider_uri=provider_uri, expression_cache=None, dataset_cache=None, + provider_uri=provider_uri, + expression_cache=None, + dataset_cache=None, ) @classmethod From f947a2fdef294ec927466ec7a287da83604c0bc8 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 22 Feb 2021 15:15:51 +0800 Subject: [PATCH 023/134] Correct two mistakes in annotation. --- qlib/model/base.py | 3 ++- qlib/portfolio/optimizer.py | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/qlib/model/base.py b/qlib/model/base.py index a7001f0a6..3708298d5 100644 --- a/qlib/model/base.py +++ b/qlib/model/base.py @@ -44,7 +44,8 @@ class Model(BaseModel): # get weights try: wdf_train, wdf_valid = dataset.prepare(["train", "valid"], col_set=["weight"], - data_key=DataHandlerLP.DK_L, w_train, w_valid = wdf_train["weight"], wdf_valid["weight"] + data_key=DataHandlerLP.DK_L) + w_train, w_valid = wdf_train["weight"], wdf_valid["weight"] except KeyError as e: w_train = pd.DataFrame(np.ones_like(y_train.values), index=y_train.index) w_valid = pd.DataFrame(np.ones_like(y_valid.values), index=y_valid.index) diff --git a/qlib/portfolio/optimizer.py b/qlib/portfolio/optimizer.py index 391242127..75c6c51f5 100644 --- a/qlib/portfolio/optimizer.py +++ b/qlib/portfolio/optimizer.py @@ -292,7 +292,7 @@ class EnhancedIndexingOptimizer(BaseOptimizer): delta: float = 0.4, bench_dev: float = 0.01, inds_dev: float = 0.01, - scale_alpha=True, + scale_alpha: bool = True, verbose: bool = False, warm_start: str = DO_NOT_START_FROM, max_iters: int = 10000, @@ -303,6 +303,7 @@ class EnhancedIndexingOptimizer(BaseOptimizer): delta (float): turnover rate limit bench_dev (float): benchmark deviation limit inds_dev (float): industry deviation limit + scale_alpha (bool): if to scale alpha to match the volatility of the covariance matrix verbose (bool): if print detailed information about the solver warm_start (str): whether try to warm start (`w0`/`benchmark`/``) (https://www.cvxpy.org/tutorial/advanced/index.html#warm-start) From d3caea60eed1caf7e8cce7ec89f9f4db938109a5 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 22 Feb 2021 17:32:03 +0800 Subject: [PATCH 024/134] Add unittest for TestStructuredCovEstimator. --- tests/test_structured_cov_estimator.py | 80 ++++++++++++++++++++++++++ 1 file changed, 80 insertions(+) create mode 100644 tests/test_structured_cov_estimator.py diff --git a/tests/test_structured_cov_estimator.py b/tests/test_structured_cov_estimator.py new file mode 100644 index 000000000..6aeae3d89 --- /dev/null +++ b/tests/test_structured_cov_estimator.py @@ -0,0 +1,80 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +import unittest +import numpy as np +from scipy.linalg import sqrtm + +from qlib.model.riskmodel import StructuredCovEstimator + + +class TestStructuredCovEstimator(unittest.TestCase): + def test_random_covariance(self): + # Try to estimate the covariance from a randomly generated matrix. + NUM_VARIABLE = 10 + NUM_OBSERVATION = 200 + EPS = 1e-6 + + estimator = StructuredCovEstimator(scale_return=False, assume_centered=True) + + X = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE) + + est_cov = estimator.predict(X, is_price=False) + np_cov = np.cov(X.T) # While numpy assume row means variable, qlib assume the other wise. + + delta = abs(est_cov - np_cov) + if_identical = (delta < EPS).all() + + self.assertTrue(if_identical) + + def test_constructed_covariance(self): + # Try to estimate the covariance from a specially crafted matrix. + # There should be some significant correlation since X is specially crafted. + NUM_VARIABLE = 7 + NUM_OBSERVATION = 500 + EPS = 0.1 + + estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, num_factors=NUM_VARIABLE - 1) + + sqrt_cov = None + while sqrt_cov is None or (np.iscomplex(sqrt_cov)).any(): + cov = np.random.rand(NUM_VARIABLE, NUM_VARIABLE) + for i in range(NUM_VARIABLE): + cov[i][i] = 1 + sqrt_cov = sqrtm(cov) + X = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE) @ sqrt_cov + + est_cov = estimator.predict(X, is_price=False) + np_cov = np.cov(X.T) # While numpy assume row means variable, qlib assume the other wise. + + delta = abs(est_cov - np_cov) + if_identical = (delta < EPS).all() + + self.assertTrue(if_identical) + + def test_decomposition(self): + # Try to estimate the covariance from a specially crafted matrix. + # The matrix is generated in the assumption that observations can be predicted by multiple factors. + NUM_VARIABLE = 30 + NUM_OBSERVATION = 100 + NUM_FACTOR = 10 + EPS = 0.1 + + estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, num_factors=NUM_FACTOR) + + F = np.random.rand(NUM_VARIABLE, NUM_FACTOR) + B = np.random.rand(NUM_FACTOR, NUM_OBSERVATION) + U = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE) + X = (F @ B).T + U + + est_cov = estimator.predict(X, is_price=False) + np_cov = np.cov(X.T) # While numpy assume row means variable, qlib assume the other wise. + + delta = abs(est_cov - np_cov) + if_identical = (delta < EPS).all() + + self.assertTrue(if_identical) + + +if __name__ == "__main__": + unittest.main() From 527718a44015a9cac3f13bd71dfcfb583f2d268f Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 22 Feb 2021 19:04:31 +0800 Subject: [PATCH 025/134] Allow enhanced indexing to generate portfolio without industry related restriction. --- qlib/portfolio/optimizer.py | 18 ++- tests/test_enhanced_indexing.py | 194 ++++++++++++++++++++++++++++++++ 2 files changed, 206 insertions(+), 6 deletions(-) create mode 100644 tests/test_enhanced_indexing.py diff --git a/qlib/portfolio/optimizer.py b/qlib/portfolio/optimizer.py index 75c6c51f5..6ee396a51 100644 --- a/qlib/portfolio/optimizer.py +++ b/qlib/portfolio/optimizer.py @@ -291,7 +291,7 @@ class EnhancedIndexingOptimizer(BaseOptimizer): lamb: float = 10, delta: float = 0.4, bench_dev: float = 0.01, - inds_dev: float = 0.01, + inds_dev: float = None, scale_alpha: bool = True, verbose: bool = False, warm_start: str = DO_NOT_START_FROM, @@ -302,7 +302,8 @@ class EnhancedIndexingOptimizer(BaseOptimizer): lamb (float): risk aversion parameter (larger `lamb` means less focus on return) delta (float): turnover rate limit bench_dev (float): benchmark deviation limit - inds_dev (float): industry deviation limit + inds_dev (float/None): industry deviation limit, set `inds_dev` to None to ignore industry specific + restriction scale_alpha (bool): if to scale alpha to match the volatility of the covariance matrix verbose (bool): if print detailed information about the solver warm_start (str): whether try to warm start (`w0`/`benchmark`/``) @@ -341,7 +342,7 @@ class EnhancedIndexingOptimizer(BaseOptimizer): varU: np.ndarray, w0: np.ndarray, w_bench: np.ndarray, - inds_onehot: np.ndarray, + inds_onehot: np.ndarray = None, ) -> Union[np.ndarray, pd.Series]: """ Args: @@ -354,6 +355,8 @@ class EnhancedIndexingOptimizer(BaseOptimizer): Returns: np.ndarray or pd.Series: optimized portfolio allocation """ + assert inds_onehot is not None or self.inds_dev is None, "Industry onehot vector is required." + # scale alpha to match volatility if self.scale_alpha: u = u / u.std() @@ -366,15 +369,18 @@ class EnhancedIndexingOptimizer(BaseOptimizer): risk = cp.quad_form(v, covB) + cp.sum(cp.multiply(varU, w ** 2)) obj = cp.Maximize(ret - self.lamb * risk) d_bench = w - w_bench - d_inds = d_bench @ inds_onehot cons = [ w >= 0, cp.sum(w) == 1, d_bench >= -self.bench_dev, d_bench <= self.bench_dev, - d_inds >= -self.inds_dev, - d_inds <= self.inds_dev, ] + + if self.inds_dev is not None: + d_inds = d_bench @ inds_onehot + cons.append(d_inds >= -self.inds_dev) + cons.append(d_inds <= self.inds_dev) + if w0 is not None: turnover = cp.sum(cp.abs(w - w0)) cons.append(turnover <= self.delta) diff --git a/tests/test_enhanced_indexing.py b/tests/test_enhanced_indexing.py new file mode 100644 index 000000000..f6e77cba4 --- /dev/null +++ b/tests/test_enhanced_indexing.py @@ -0,0 +1,194 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +import sys +import shutil +import unittest +from pathlib import Path + +import numpy as np +import pandas as pd + +import qlib +from qlib.config import REG_CN, C +from qlib.utils import drop_nan_by_y_index +from qlib.contrib.model.gbdt import LGBModel +from qlib.contrib.data.handler import Alpha158 +from qlib.contrib.strategy.strategy import TopkDropoutStrategy +from qlib.contrib.evaluate import ( + backtest as normal_backtest, + risk_analysis, +) +from qlib.utils import exists_qlib_data, init_instance_by_config, flatten_dict +from qlib.workflow import R +from qlib.workflow.record_temp import SignalRecord, SigAnaRecord, PortAnaRecord +from qlib.tests.data import GetData +from qlib.tests import TestAutoData + + +market = "csi300" +benchmark = "SH000300" + +################################### +# train model +################################### +data_handler_config = { + "start_time": "2008-01-01", + "end_time": "2020-08-01", + "fit_start_time": "2008-01-01", + "fit_end_time": "2014-12-31", + "instruments": market, +} + +task = { + "model": { + "class": "LGBModel", + "module_path": "qlib.contrib.model.gbdt", + "kwargs": { + "loss": "mse", + "colsample_bytree": 0.8879, + "learning_rate": 0.0421, + "subsample": 0.8789, + "lambda_l1": 205.6999, + "lambda_l2": 580.9768, + "max_depth": 8, + "num_leaves": 210, + "num_threads": 20, + }, + }, + "dataset": { + "class": "DatasetH", + "module_path": "qlib.data.dataset", + "kwargs": { + "handler": { + "class": "Alpha158", + "module_path": "qlib.contrib.data.handler", + "kwargs": data_handler_config, + }, + "segments": { + "train": ("2008-01-01", "2014-12-31"), + "valid": ("2015-01-01", "2016-12-31"), + "test": ("2017-01-01", "2020-08-01"), + }, + }, + }, +} + +port_analysis_config = { + "strategy": { + "class": "TopkDropoutStrategy", + "module_path": "qlib.contrib.strategy.strategy", + "kwargs": { + "topk": 50, + "n_drop": 5, + }, + }, + "backtest": { + "verbose": False, + "limit_threshold": 0.095, + "account": 100000000, + "benchmark": benchmark, + "deal_price": "close", + "open_cost": 0.0005, + "close_cost": 0.0015, + "min_cost": 5, + }, +} + + +# train +def train(): + """train model + + Returns + ------- + pred_score: pandas.DataFrame + predict scores + performance: dict + model performance + """ + + # model initiaiton + model = init_instance_by_config(task["model"]) + dataset = init_instance_by_config(task["dataset"]) + + # start exp + with R.start(experiment_name="workflow"): + R.log_params(**flatten_dict(task)) + model.fit(dataset) + + # prediction + recorder = R.get_recorder() + rid = recorder.id + sr = SignalRecord(model, dataset, recorder) + sr.generate() + pred_score = sr.load() + + # calculate ic and ric + sar = SigAnaRecord(recorder) + sar.generate() + ic = sar.load(sar.get_path("ic.pkl")) + ric = sar.load(sar.get_path("ric.pkl")) + + return pred_score, {"ic": ic, "ric": ric}, rid + + +def backtest_analysis(pred, rid): + """backtest and analysis + + Parameters + ---------- + pred : pandas.DataFrame + predict scores + rid : str + the id of the recorder to be used in this function + + Returns + ------- + analysis : pandas.DataFrame + the analysis result + + """ + recorder = R.get_recorder(experiment_name="workflow", recorder_id=rid) + # backtest + par = PortAnaRecord(recorder, port_analysis_config) + par.generate() + analysis_df = par.load(par.get_path("port_analysis.pkl")) + print(analysis_df) + return analysis_df + + +class TestAllFlow(TestAutoData): + PRED_SCORE = None + REPORT_NORMAL = None + POSITIONS = None + RID = None + + @classmethod + def tearDownClass(cls) -> None: + shutil.rmtree(str(Path(C["exp_manager"]["kwargs"]["uri"].strip("file:")).resolve())) + + def test_0_train(self): + TestAllFlow.PRED_SCORE, ic_ric, TestAllFlow.RID = train() + self.assertGreaterEqual(ic_ric["ic"].all(), 0, "train failed") + self.assertGreaterEqual(ic_ric["ric"].all(), 0, "train failed") + + def test_1_backtest(self): + analyze_df = backtest_analysis(TestAllFlow.PRED_SCORE, TestAllFlow.RID) + self.assertGreaterEqual( + analyze_df.loc(axis=0)["excess_return_with_cost", "annualized_return"].values[0], + 0.10, + "backtest failed", + ) + + +def suite(): + _suite = unittest.TestSuite() + _suite.addTest(TestAllFlow("test_0_train")) + _suite.addTest(TestAllFlow("test_1_backtest")) + return _suite + + +if __name__ == "__main__": + runner = unittest.TextTestRunner() + runner.run(suite()) From 1a990fdd25e5fd25e6b868d56122f92b7caa30f7 Mon Sep 17 00:00:00 2001 From: meng-ustc Date: Tue, 23 Feb 2021 19:08:11 +0900 Subject: [PATCH 026/134] Add Risk Prediction Demo --- examples/workflow_by_code_lgb_risk_demo.py | 179 +++++++++++++++++++++ 1 file changed, 179 insertions(+) create mode 100644 examples/workflow_by_code_lgb_risk_demo.py diff --git a/examples/workflow_by_code_lgb_risk_demo.py b/examples/workflow_by_code_lgb_risk_demo.py new file mode 100644 index 000000000..b250993d3 --- /dev/null +++ b/examples/workflow_by_code_lgb_risk_demo.py @@ -0,0 +1,179 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +import sys +from pathlib import Path + +import qlib +from qlib.config import REG_CN +from qlib.utils import exists_qlib_data, init_instance_by_config, flatten_dict +from qlib.workflow import R +from qlib.workflow.record_temp import SignalRecord, PortAnaRecord +from qlib.data.dataset.handler import DataHandlerLP + +import seaborn as sns +import matplotlib.pyplot as plt +import math +import pandas as pd +from scipy.stats.stats import pearsonr +import numpy as np + +if __name__ == "__main__": + + # use default data + provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir + if not exists_qlib_data(provider_uri): + print(f"Qlib data is not found in {provider_uri}") + sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts"))) + from get_data import GetData + + GetData().qlib_data(target_dir=provider_uri, region=REG_CN) + + qlib.init(provider_uri=provider_uri, region=REG_CN) + + market = "csi300" + benchmark = "SH000300" + + ################################### + # train model + ################################### + data_handler_config = { + "start_time": "2008-01-01", + "end_time": "2020-08-01", + "fit_start_time": "2008-01-01", + "fit_end_time": "2014-12-31", + "instruments": market, + "infer_processors": [ + {"class": "ProcessInf", "kwargs": {}}, + {"class": "ZScoreNorm", "kwargs": {"fields_group": "feature"}}, + {"class": "Fillna", "kwargs": {}}, + ], + "learn_processors": [{ + "class": "DropnaLabel", }, + ], + "label": (["Ref(Min($low, 5), -4)/$close - 1"], ["LABEL0"]) # the period for risk prediction is 5 days + } + + task = { + "model": { + "class": "LGBModel", + "module_path": "qlib.contrib.model.gbdt", + "kwargs": { + "loss": "mse", + "colsample_bytree": 0.8999, + "learning_rate": 0.02, + "subsample": 0.7, + "lambda_l1": 11.9668, + "lambda_l2": 339.1301, + "max_depth": 16, + "num_leaves": 31, + "num_threads": 20, + }, + }, + "dataset": { + "class": "DatasetH", + "module_path": "qlib.data.dataset", + "kwargs": { + "handler": { + "class": "Alpha360", + "module_path": "qlib.contrib.data.handler", + "kwargs": data_handler_config, + }, + "segments": { + "train": ("2008-01-01", "2014-12-31"), + "valid": ("2015-01-01", "2016-12-31"), + "test": ("2017-01-01", "2020-08-01"), + }, + }, + }, + } + + port_analysis_config = { + "strategy": { + "class": "TopkDropoutStrategy", + "module_path": "qlib.contrib.strategy.strategy", + "kwargs": { + "topk": 50, + "n_drop": 5, + }, + }, + "backtest": { + "verbose": False, + "limit_threshold": 0.095, + "account": 100000000, + "benchmark": benchmark, + "deal_price": "close", + "open_cost": 0.0005, + "close_cost": 0.0015, + "min_cost": 5, + "return_order": True, + }, + } + + # model initiaiton + model = init_instance_by_config(task["model"]) + dataset = init_instance_by_config(task["dataset"]) + + # NOTE: This line is optional + # It demonstrates that the dataset can be used standalone. + example_df = dataset.prepare("train") + print(example_df.head()) + + def heatmap(actual_risk, predicted_risk, step=0.02): + """ + plot the precision heatmap as a visualized evaluation for risk predition + :param actual_risk: the LABEL0 of test samples + :param predicted_risk: the predicted results of test samples + :param step: the internal size of risk values on axis + :return: + """ + num_step = math.ceil(-predicted_risk.min() / step) + matrix = np.zeros((num_step, num_step), dtype=np.float) + for pred_thresh in range(num_step): + for act_thresh in range(num_step): + actual_positive = actual_risk < -act_thresh*step + predicted_alarm = predicted_risk < -pred_thresh*step + num_alarm = predicted_alarm.sum() + num_tp = (actual_positive & predicted_alarm).sum() + matrix[pred_thresh, act_thresh] = num_tp / num_alarm + axis_labels = ['{:.3f}'.format(-x * step) for x in range(num_step)] + return matrix, axis_labels + + # start exp + with R.start(experiment_name="workflow"): + R.log_params(**flatten_dict(task)) + model.fit(dataset) + + # prediction + actual_risk = dataset.prepare("test", col_set="label", data_key=DataHandlerLP.DK_I)['LABEL0'] + pred = model.predict(dataset) + + result_df = pd.concat((actual_risk, pred), axis=1) + result_df.columns = ['Actual Risk', 'Predicted Risk'] + result_df.dropna(inplace=True) + actual_risk, predicted_risk = result_df.iloc[:, 0], result_df.iloc[:, 1] + corr = pearsonr(actual_risk, predicted_risk)[0] + print('The correlation between predicted risk and actual risk is: {:.6f}'.format(corr)) + + # visualized results + fig, axes = plt.subplots(2, 2, figsize=(15, 10)) + sns.histplot(actual_risk, ax=axes[0, 0]) + axes[0, 0].set_title('Market: {} Actual Risk'.format(market)) + axes[0, 0].grid() + + sns.histplot(predicted_risk, ax=axes[0, 1]) + axes[0, 1].set_title('Feature: {} Predicted Risk'.format(task['dataset']['kwargs']['handler']['class'])) + axes[0, 1].grid() + + sns.scatterplot(data=result_df, ax=axes[1, 0], x='Actual Risk', y='Predicted Risk', s=20) + axes[1, 0].set_title('Market: {} Feature: {} Corr: {:.5f}'.format( + market, task['dataset']['kwargs']['handler']['class'], corr)) + axes[1, 0].grid() + + matrix, ax_labels = heatmap(actual_risk, predicted_risk) + sns.heatmap(matrix, annot=True, fmt=".3f", xticklabels=ax_labels, yticklabels=ax_labels, ax=axes[1, 1], + ) + axes[1, 1].set_xlabel('Predicted Alarm Threshold') + axes[1, 1].set_ylabel('Actual Positive Threshold') + axes[1, 1].set_title('Risk Prediction Precision Heatmap') + plt.show() From ce60097722d46bb431224d1bf9e67e59b2e03224 Mon Sep 17 00:00:00 2001 From: meng-ustc Date: Wed, 24 Feb 2021 16:59:31 +0900 Subject: [PATCH 027/134] Add README and Formatted --- README.md | 1 + examples/benchmarks/DoubleEnsemble/README.md | 4 + qlib/contrib/model/double_ensemble.py | 77 ++++++++++---------- 3 files changed, 45 insertions(+), 37 deletions(-) create mode 100644 examples/benchmarks/DoubleEnsemble/README.md diff --git a/README.md b/README.md index 787075d6a..e1608c37d 100644 --- a/README.md +++ b/README.md @@ -232,6 +232,7 @@ Here is a list of models built on `Qlib`. - [SFM based on pytorch (Liheng Zhang, et al. 2017)](qlib/contrib/model/pytorch_sfm.py) - [TFT based on tensorflow (Bryan Lim, et al. 2019)](examples/benchmarks/TFT/tft.py) - [TabNet based on pytorch (Sercan O. Arik, et al. 2019)](qlib/contrib/model/pytorch_tabnet.py) +- [DoubleEnsemble based on LightGBM (Chuheng Zhang, et al. 2020)](qlib/contrib/model/double_ensemble.py) Your PR of new Quant models is highly welcomed. diff --git a/examples/benchmarks/DoubleEnsemble/README.md b/examples/benchmarks/DoubleEnsemble/README.md new file mode 100644 index 000000000..67e741050 --- /dev/null +++ b/examples/benchmarks/DoubleEnsemble/README.md @@ -0,0 +1,4 @@ +# DoubleEnsemble +* DoubleEnsemble is an ensemble framework leveraging learning trajectory based sample reweighting and shuffling based feature selection, to solve both the low signal-to-noise ratio and increasing number of features problems. They identify the key samples based on the training dynamics on each sample and elicit key features based on the ablation impact of each feature via shuffling. The model is applicable to a wide range of base models, capable of extracting complex patterns, while mitigating the overfitting and instability issues for financial market prediction. +* This code used in Qlib is implemented by ourselves. +* Paper: DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis [https://arxiv.org/pdf/2010.01265.pdf](https://arxiv.org/pdf/2010.01265.pdf). \ No newline at end of file diff --git a/qlib/contrib/model/double_ensemble.py b/qlib/contrib/model/double_ensemble.py index 786b3987c..c96b69e8b 100644 --- a/qlib/contrib/model/double_ensemble.py +++ b/qlib/contrib/model/double_ensemble.py @@ -15,21 +15,22 @@ class DEnsembleModel(Model): """Double Ensemble Model""" def __init__( - self, - base="gbm", - loss="mse", - k=6, - enable_sr=True, - enable_fs=True, - alpha1=1., - alpha2=1., - bins_sr=10, - bins_fs=5, - decay=None, - sample_ratios=None, - sub_weights=None, - epochs=100, - **kwargs): + self, + base="gbm", + loss="mse", + k=6, + enable_sr=True, + enable_fs=True, + alpha1=1.0, + alpha2=1.0, + bins_sr=10, + bins_fs=5, + decay=None, + sample_ratios=None, + sub_weights=None, + epochs=100, + **kwargs + ): self.base = base # "gbm" or "mlp", specifically, we use lgbm for "gbm" self.k = k self.enable_sr = enable_sr @@ -54,10 +55,7 @@ class DEnsembleModel(Model): self.params.update(kwargs) self.loss = loss - def fit( - self, - dataset: DatasetH - ): + def fit(self, dataset: DatasetH): df_train, df_valid = dataset.prepare( ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L ) @@ -71,7 +69,7 @@ class DEnsembleModel(Model): # train k sub-models for i_k in range(self.k): self.sub_features.append(features) - self.logger.info("Training sub-model: ({}/{})".format(i_k+1, self.k)) + self.logger.info("Training sub-model: ({}/{})".format(i_k + 1, self.k)) model_k = self.train_submodel(df_train, df_valid, weights, features) self.ensemble.append(model_k) # no further sample re-weight and feature selection needed for the last sub-model @@ -82,12 +80,12 @@ class DEnsembleModel(Model): loss_curve = self.retrieve_loss_curve(model_k, df_train, features) pred_k = self.predict_sub(model_k, df_train, features) pred_sub.iloc[:, i_k] = pred_k - pred_ensemble = pred_sub.iloc[:, :i_k+1].mean(axis=1) + pred_ensemble = pred_sub.iloc[:, : i_k + 1].mean(axis=1) loss_values = pd.Series(self.get_loss(y_train.values.squeeze(), pred_ensemble.values)) if self.enable_sr: self.logger.info("Sample re-weighting...") - weights = self.sample_reweight(loss_curve, loss_values, i_k+1) + weights = self.sample_reweight(loss_curve, loss_values, i_k + 1) if self.enable_fs: self.logger.info("Feature selection...") @@ -148,14 +146,14 @@ class DEnsembleModel(Model): # calculate h-value for each sample h1 = loss_values_norm h2 = (l_end / l_start).rank(pct=True) - h = pd.DataFrame({'h_value': self.alpha1 * h1 + self.alpha2 * h2}) + h = pd.DataFrame({"h_value": self.alpha1 * h1 + self.alpha2 * h2}) # calculate weights - h['bins'] = pd.cut(h['h_value'], self.bins_sr) - h_avg = h.groupby('bins')['h_value'].mean() + h["bins"] = pd.cut(h["h_value"], self.bins_sr) + h_avg = h.groupby("bins")["h_value"].mean() weights = pd.Series(np.zeros(N, dtype=float)) for i_b, b in enumerate(h_avg.index): - weights[h['bins'] == b] = 1. / (self.decay ** k_th * h_avg[i_b] + 0.1) + weights[h["bins"] == b] = 1.0 / (self.decay ** k_th * h_avg[i_b] + 0.1) return weights def feature_selection(self, df_train, loss_values): @@ -170,7 +168,7 @@ class DEnsembleModel(Model): x_train, y_train = df_train["feature"], df_train["label"] features = x_train.columns N, F = x_train.shape - g = pd.DataFrame({'g_value': np.zeros(F, dtype=float)}) + g = pd.DataFrame({"g_value": np.zeros(F, dtype=float)}) M = len(self.ensemble) # shuffle specific columns and calculate g-value for each feature @@ -179,23 +177,27 @@ class DEnsembleModel(Model): x_train_tmp.loc[:, feat] = np.random.permutation(x_train_tmp.loc[:, feat].values) pred = pd.Series(np.zeros(N), index=x_train_tmp.index) for i_s, submodel in enumerate(self.ensemble): - pred += pd.Series(submodel.predict(x_train_tmp.loc[:, self.sub_features[i_s]].values), - index=x_train_tmp.index) / M + pred += ( + pd.Series( + submodel.predict(x_train_tmp.loc[:, self.sub_features[i_s]].values), index=x_train_tmp.index + ) + / M + ) loss_feat = self.get_loss(y_train.values.squeeze(), pred.values) - g.loc[i_f, 'g_value'] = np.mean(loss_feat - loss_values) / np.std(loss_feat - loss_values) + g.loc[i_f, "g_value"] = np.mean(loss_feat - loss_values) / np.std(loss_feat - loss_values) x_train_tmp.loc[:, feat] = x_train.loc[:, feat].copy() # one column in train features is all-nan # if g['g_value'].isna().any() - g['g_value'].replace(np.nan, 0, inplace=True) + g["g_value"].replace(np.nan, 0, inplace=True) # divide features into bins_fs bins - g['bins'] = pd.cut(g['g_value'], self.bins_fs) + g["bins"] = pd.cut(g["g_value"], self.bins_fs) # randomly sample features from bins to construct the new features res_feat = [] - sorted_bins = sorted(g['bins'].unique(), reverse=True) + sorted_bins = sorted(g["bins"].unique(), reverse=True) for i_b, b in enumerate(sorted_bins): - b_feat = features[g['bins'] == b] + b_feat = features[g["bins"] == b] num_feat = int(np.ceil(self.sample_ratios[i_b] * len(b_feat))) res_feat = res_feat + np.random.choice(b_feat, size=num_feat).tolist() return pd.Index(res_feat) @@ -233,12 +235,13 @@ class DEnsembleModel(Model): pred = pd.Series(np.zeros(x_test.shape[0]), index=x_test.index) for i_sub, submodel in enumerate(self.ensemble): feat_sub = self.sub_features[i_sub] - pred += pd.Series(submodel.predict(x_test.loc[:, feat_sub].values), index=x_test.index) * self.sub_weights[i_sub] + pred += ( + pd.Series(submodel.predict(x_test.loc[:, feat_sub].values), index=x_test.index) + * self.sub_weights[i_sub] + ) return pred def predict_sub(self, submodel, df_data, features): x_data, y_data = df_data["feature"].loc[:, features], df_data["label"] pred_sub = pd.Series(submodel.predict(x_data.values), index=x_data.index) return pred_sub - - From 70575e8a1ce4f63cced98a55de1914009012bdcd Mon Sep 17 00:00:00 2001 From: Meng Dong Date: Wed, 24 Feb 2021 16:10:38 +0800 Subject: [PATCH 028/134] Delete workflow_by_code_lgb_risk_demo.py --- examples/workflow_by_code_lgb_risk_demo.py | 179 --------------------- 1 file changed, 179 deletions(-) delete mode 100644 examples/workflow_by_code_lgb_risk_demo.py diff --git a/examples/workflow_by_code_lgb_risk_demo.py b/examples/workflow_by_code_lgb_risk_demo.py deleted file mode 100644 index b250993d3..000000000 --- a/examples/workflow_by_code_lgb_risk_demo.py +++ /dev/null @@ -1,179 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. - -import sys -from pathlib import Path - -import qlib -from qlib.config import REG_CN -from qlib.utils import exists_qlib_data, init_instance_by_config, flatten_dict -from qlib.workflow import R -from qlib.workflow.record_temp import SignalRecord, PortAnaRecord -from qlib.data.dataset.handler import DataHandlerLP - -import seaborn as sns -import matplotlib.pyplot as plt -import math -import pandas as pd -from scipy.stats.stats import pearsonr -import numpy as np - -if __name__ == "__main__": - - # use default data - provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir - if not exists_qlib_data(provider_uri): - print(f"Qlib data is not found in {provider_uri}") - sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts"))) - from get_data import GetData - - GetData().qlib_data(target_dir=provider_uri, region=REG_CN) - - qlib.init(provider_uri=provider_uri, region=REG_CN) - - market = "csi300" - benchmark = "SH000300" - - ################################### - # train model - ################################### - data_handler_config = { - "start_time": "2008-01-01", - "end_time": "2020-08-01", - "fit_start_time": "2008-01-01", - "fit_end_time": "2014-12-31", - "instruments": market, - "infer_processors": [ - {"class": "ProcessInf", "kwargs": {}}, - {"class": "ZScoreNorm", "kwargs": {"fields_group": "feature"}}, - {"class": "Fillna", "kwargs": {}}, - ], - "learn_processors": [{ - "class": "DropnaLabel", }, - ], - "label": (["Ref(Min($low, 5), -4)/$close - 1"], ["LABEL0"]) # the period for risk prediction is 5 days - } - - task = { - "model": { - "class": "LGBModel", - "module_path": "qlib.contrib.model.gbdt", - "kwargs": { - "loss": "mse", - "colsample_bytree": 0.8999, - "learning_rate": 0.02, - "subsample": 0.7, - "lambda_l1": 11.9668, - "lambda_l2": 339.1301, - "max_depth": 16, - "num_leaves": 31, - "num_threads": 20, - }, - }, - "dataset": { - "class": "DatasetH", - "module_path": "qlib.data.dataset", - "kwargs": { - "handler": { - "class": "Alpha360", - "module_path": "qlib.contrib.data.handler", - "kwargs": data_handler_config, - }, - "segments": { - "train": ("2008-01-01", "2014-12-31"), - "valid": ("2015-01-01", "2016-12-31"), - "test": ("2017-01-01", "2020-08-01"), - }, - }, - }, - } - - port_analysis_config = { - "strategy": { - "class": "TopkDropoutStrategy", - "module_path": "qlib.contrib.strategy.strategy", - "kwargs": { - "topk": 50, - "n_drop": 5, - }, - }, - "backtest": { - "verbose": False, - "limit_threshold": 0.095, - "account": 100000000, - "benchmark": benchmark, - "deal_price": "close", - "open_cost": 0.0005, - "close_cost": 0.0015, - "min_cost": 5, - "return_order": True, - }, - } - - # model initiaiton - model = init_instance_by_config(task["model"]) - dataset = init_instance_by_config(task["dataset"]) - - # NOTE: This line is optional - # It demonstrates that the dataset can be used standalone. - example_df = dataset.prepare("train") - print(example_df.head()) - - def heatmap(actual_risk, predicted_risk, step=0.02): - """ - plot the precision heatmap as a visualized evaluation for risk predition - :param actual_risk: the LABEL0 of test samples - :param predicted_risk: the predicted results of test samples - :param step: the internal size of risk values on axis - :return: - """ - num_step = math.ceil(-predicted_risk.min() / step) - matrix = np.zeros((num_step, num_step), dtype=np.float) - for pred_thresh in range(num_step): - for act_thresh in range(num_step): - actual_positive = actual_risk < -act_thresh*step - predicted_alarm = predicted_risk < -pred_thresh*step - num_alarm = predicted_alarm.sum() - num_tp = (actual_positive & predicted_alarm).sum() - matrix[pred_thresh, act_thresh] = num_tp / num_alarm - axis_labels = ['{:.3f}'.format(-x * step) for x in range(num_step)] - return matrix, axis_labels - - # start exp - with R.start(experiment_name="workflow"): - R.log_params(**flatten_dict(task)) - model.fit(dataset) - - # prediction - actual_risk = dataset.prepare("test", col_set="label", data_key=DataHandlerLP.DK_I)['LABEL0'] - pred = model.predict(dataset) - - result_df = pd.concat((actual_risk, pred), axis=1) - result_df.columns = ['Actual Risk', 'Predicted Risk'] - result_df.dropna(inplace=True) - actual_risk, predicted_risk = result_df.iloc[:, 0], result_df.iloc[:, 1] - corr = pearsonr(actual_risk, predicted_risk)[0] - print('The correlation between predicted risk and actual risk is: {:.6f}'.format(corr)) - - # visualized results - fig, axes = plt.subplots(2, 2, figsize=(15, 10)) - sns.histplot(actual_risk, ax=axes[0, 0]) - axes[0, 0].set_title('Market: {} Actual Risk'.format(market)) - axes[0, 0].grid() - - sns.histplot(predicted_risk, ax=axes[0, 1]) - axes[0, 1].set_title('Feature: {} Predicted Risk'.format(task['dataset']['kwargs']['handler']['class'])) - axes[0, 1].grid() - - sns.scatterplot(data=result_df, ax=axes[1, 0], x='Actual Risk', y='Predicted Risk', s=20) - axes[1, 0].set_title('Market: {} Feature: {} Corr: {:.5f}'.format( - market, task['dataset']['kwargs']['handler']['class'], corr)) - axes[1, 0].grid() - - matrix, ax_labels = heatmap(actual_risk, predicted_risk) - sns.heatmap(matrix, annot=True, fmt=".3f", xticklabels=ax_labels, yticklabels=ax_labels, ax=axes[1, 1], - ) - axes[1, 1].set_xlabel('Predicted Alarm Threshold') - axes[1, 1].set_ylabel('Actual Positive Threshold') - axes[1, 1].set_title('Risk Prediction Precision Heatmap') - plt.show() From 719074d30673d9ba256bf69df396854c3e498c47 Mon Sep 17 00:00:00 2001 From: wangershi Date: Thu, 25 Feb 2021 19:20:14 +0800 Subject: [PATCH 029/134] touch file --- scripts/data_collector/fund/README.md | 49 ++++++++++++++++++++ scripts/data_collector/fund/collector.py | 0 scripts/data_collector/fund/requirements.txt | 0 3 files changed, 49 insertions(+) create mode 100644 scripts/data_collector/fund/README.md create mode 100644 scripts/data_collector/fund/collector.py create mode 100644 scripts/data_collector/fund/requirements.txt diff --git a/scripts/data_collector/fund/README.md b/scripts/data_collector/fund/README.md new file mode 100644 index 000000000..c7b91a3f5 --- /dev/null +++ b/scripts/data_collector/fund/README.md @@ -0,0 +1,49 @@ +# Collect Fund Data + +> *Please pay **ATTENTION** that the data is collected from [天天基金网](https://fund.eastmoney.com/) and the data might not be perfect. We recommend users to prepare their own data if they have high-quality dataset. For more information, users can refer to the [related document](https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format)* + +## Requirements + +```bash +pip install -r requirements.txt +``` + +## Collector Data + + +### CN Data + +#### 1d from East Money + +```bash + +# download from yahoo finance +python collector.py download_data --source_dir ~/.qlib/stock_data/source/cn_1d --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d + + +# dump data +cd qlib/scripts +python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/cn_1d_nor --qlib_dir ~/.qlib/qlib_data/qlib_cn_1d --freq day --exclude_fields date,adjclose,dividends,splits,symbol + +``` + +### using data + +```python +import qlib +from qlib.data import D + +qlib.init(provider_uri="~/.qlib/qlib_data/qlib_cn_1d", region="CN") +df = D.features(D.instruments("all"), ["$close"], freq="day") +``` + + +### Help +```bash +pythono collector.py collector_data --help +``` + +## Parameters + +- interval: 1min or 1d +- region: CN or US diff --git a/scripts/data_collector/fund/collector.py b/scripts/data_collector/fund/collector.py new file mode 100644 index 000000000..e69de29bb diff --git a/scripts/data_collector/fund/requirements.txt b/scripts/data_collector/fund/requirements.txt new file mode 100644 index 000000000..e69de29bb From a96f0c2e5fa427ce33660c8791fa2057c6f423fc Mon Sep 17 00:00:00 2001 From: Wendi Li Date: Fri, 26 Feb 2021 23:12:59 +0800 Subject: [PATCH 030/134] Update README.md Typos fixed. --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 6f416d420..a13a2b820 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ Qlib is an AI-oriented quantitative investment platform, which aims to realize t It contains the full ML pipeline of data processing, model training, back-testing; and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution. -With Qlib, user can easily try ideas to create better Quant investment strategies. +With Qlib, users can easily try ideas to create better Quant investment strategies. For more details, please refer to our paper ["Qlib: An AI-oriented Quantitative Investment Platform"](https://arxiv.org/abs/2009.11189). @@ -246,10 +246,10 @@ The performance of each model on the `Alpha158` and `Alpha360` dataset can be fo All the models listed above are runnable with ``Qlib``. Users can find the config files we provide and some details about the model through the [benchmarks](examples/benchmarks) folder. More information can be retrieved at the model files listed above. `Qlib` provides three different ways to run a single model, users can pick the one that fits their cases best: -- User can use the tool `qrun` mentioned above to run a model's workflow based from a config file. -- User can create a `workflow_by_code` python script based on the [one](examples/workflow_by_code.py) listed in the `examples` folder. +- Users can use the tool `qrun` mentioned above to run a model's workflow based from a config file. +- Users can create a `workflow_by_code` python script based on the [one](examples/workflow_by_code.py) listed in the `examples` folder. -- User can use the script [`run_all_model.py`](examples/run_all_model.py) listed in the `examples` folder to run a model. Here is an example of the specific shell command to be used: `python run_all_model.py --models=lightgbm`, where the `--models` arguments can take any number of models listed above(the available models can be found in [benchmarks](examples/benchmarks/)). For more use cases, please refer to the file's [docstrings](examples/run_all_model.py). +- Users can use the script [`run_all_model.py`](examples/run_all_model.py) listed in the `examples` folder to run a model. Here is an example of the specific shell command to be used: `python run_all_model.py --models=lightgbm`, where the `--models` arguments can take any number of models listed above(the available models can be found in [benchmarks](examples/benchmarks/)). For more use cases, please refer to the file's [docstrings](examples/run_all_model.py). ## Run multiple models `Qlib` also provides a script [`run_all_model.py`](examples/run_all_model.py) which can run multiple models for several iterations. (**Note**: the script only support *Linux* for now. Other OS will be supported in the future. Besides, it doesn't support parrallel running the same model for multiple times as well, and this will be fixed in the future development too.) From 6e5639621710bae40d508357d77b9d60a104aff8 Mon Sep 17 00:00:00 2001 From: wangershi Date: Sun, 28 Feb 2021 12:24:26 +0800 Subject: [PATCH 031/134] add crawler --- scripts/data_collector/fund/README.md | 4 +- scripts/data_collector/fund/collector.py | 408 +++++++++++++++++++++++ scripts/data_collector/utils.py | 37 ++ 3 files changed, 447 insertions(+), 2 deletions(-) diff --git a/scripts/data_collector/fund/README.md b/scripts/data_collector/fund/README.md index c7b91a3f5..b14938a3d 100644 --- a/scripts/data_collector/fund/README.md +++ b/scripts/data_collector/fund/README.md @@ -17,8 +17,8 @@ pip install -r requirements.txt ```bash -# download from yahoo finance -python collector.py download_data --source_dir ~/.qlib/stock_data/source/cn_1d --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d +# download from eastmoney.com +python collector.py download_data --source_dir ~/.qlib/fund_data/source/cn_1d --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d # dump data diff --git a/scripts/data_collector/fund/collector.py b/scripts/data_collector/fund/collector.py index e69de29bb..404b6af0e 100644 --- a/scripts/data_collector/fund/collector.py +++ b/scripts/data_collector/fund/collector.py @@ -0,0 +1,408 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +import abc +import sys +import copy +import time +import datetime +import importlib +import json +from abc import ABC +from pathlib import Path +from typing import Iterable, Type +from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor + +import fire +import requests +import numpy as np +import pandas as pd +from tqdm import tqdm +from loguru import logger +from dateutil.tz import tzlocal +from qlib.utils import code_to_fname, fname_to_code + +CUR_DIR = Path(__file__).resolve().parent +sys.path.append(str(CUR_DIR.parent.parent)) +from data_collector.utils import get_calendar_list, get_en_fund_symbols + +INDEX_BENCH_URL = "http://api.fund.eastmoney.com/f10/lsjz?callback=jQuery_&fundCode={index_code}&pageIndex=1&pageSize={numberOfHistoricalDaysToCrawl}&startDate={startDate}&endDate={endDate}" +REGION_CN = "CN" +REGION_US = "US" + + +class FundData: + START_DATETIME = pd.Timestamp("2000-01-01") + HIGH_FREQ_START_DATETIME = pd.Timestamp(datetime.datetime.now() - pd.Timedelta(days=5 * 6)) + END_DATETIME = pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1)) + INTERVAL_1d = "1d" + + def __init__( + self, + timezone: str = None, + start=None, + end=None, + interval="1d", + delay=0, + show_1min_logging: bool = False, + ): + """ + + Parameters + ---------- + timezone: str + The timezone where the data is located + delay: float + time.sleep(delay), default 0 + interval: str + freq, value from [1d], default 1d + start: str + start datetime, default None + end: str + end datetime, default None + show_1min_logging: bool + show 1min logging, by default False; if True, there may be many warning logs + """ + self._timezone = tzlocal() if timezone is None else timezone + self._delay = delay + self._interval = interval + self._show_1min_logging = show_1min_logging + self.start_datetime = pd.Timestamp(str(start)) if start else self.START_DATETIME + self.end_datetime = min(pd.Timestamp(str(end)) if end else self.END_DATETIME, self.END_DATETIME) + if self._interval != self.INTERVAL_1d: + raise ValueError(f"interval error: {self._interval}") + + # using for 1min + self._next_datetime = self.convert_datetime(self.start_datetime.date() + pd.Timedelta(days=1), self._timezone) + self._latest_datetime = self.convert_datetime(self.end_datetime.date(), self._timezone) + + self.start_datetime = self.convert_datetime(self.start_datetime, self._timezone) + self.end_datetime = self.convert_datetime(self.end_datetime, self._timezone) + + @staticmethod + def convert_datetime(dt: [pd.Timestamp, datetime.date, str], timezone): + try: + dt = pd.Timestamp(dt, tz=timezone).timestamp() + dt = pd.Timestamp(dt, tz=tzlocal(), unit="s") + except ValueError as e: + pass + return dt + + def _sleep(self): + time.sleep(self._delay) + + @staticmethod + def get_data_from_remote(symbol, interval, start, end, show_1min_logging: bool = False): + error_msg = f"{symbol}-{interval}-{start}-{end}" + + try: + _resp = None + + # TODO: numberOfHistoricalDaysToCrawl should be bigger enouhg + url = INDEX_BENCH_URL.format(index_code=symbol, numberOfHistoricalDaysToCrawl=100, startDate=start, endDate=end) + resp = requests.get(url, headers={"referer": "http://fund.eastmoney.com/110022.html"}) + + if resp.status_code != 200: + raise ValueError("request error") + try: + data = json.loads(resp.text.split("(")[-1].split(")")[0]) + + # Some funds don't show the net value, example: http://fundf10.eastmoney.com/jjjz_010288.html + SYType = data["Data"]["SYType"] + if (SYType == "每万份收益") or (SYType == "每百份收益") or (SYType == "每百万份收益"): + raise Exception("The fund contains 每*份收益") + + _resp = pd.DataFrame( + data["Data"]["LSJZList"] + ) + + except Exception as e: + logger.warning(f"request error: {e}") + raise + + if isinstance(_resp, pd.DataFrame): + return _resp.reset_index() + except Exception as e: + logger.warning(f"{error_msg}:{e}") + + def get_data(self, symbol: str) -> [pd.DataFrame]: + def _get_simple(start_, end_): + self._sleep() + _remote_interval = self._interval + return self.get_data_from_remote( + symbol, + interval=_remote_interval, + start=start_, + end=end_, + show_1min_logging=self._show_1min_logging, + ) + + if self._interval == self.INTERVAL_1d: + _result = _get_simple(self.start_datetime, self.end_datetime) + else: + raise ValueError(f"cannot support {self._interval}") + return _result + + +class FundCollector: + def __init__( + self, + save_dir: [str, Path], + start=None, + end=None, + interval="1d", + max_workers=4, + max_collector_count=2, + delay=0, + check_data_length: bool = False, + limit_nums: int = None, + show_1min_logging: bool = False, + ): + """ + + Parameters + ---------- + save_dir: str + stock save dir + max_workers: int + workers, default 4 + max_collector_count: int + default 2 + delay: float + time.sleep(delay), default 0 + interval: str + freq, value from [1min, 1d], default 1min + start: str + start datetime, default None + end: str + end datetime, default None + check_data_length: bool + check data length, by default False + limit_nums: int + using for debug, by default None + show_1min_logging: bool + show 1m logging, by default False; if True, there may be many warning logs + """ + self.save_dir = Path(save_dir).expanduser().resolve() + self.save_dir.mkdir(parents=True, exist_ok=True) + + self._delay = delay + self.max_workers = max_workers + self._max_collector_count = max_collector_count + self._mini_symbol_map = {} + self._interval = interval + self._check_small_data = check_data_length + + self.fund_list = sorted(set(self.get_fund_list())) + if limit_nums is not None: + try: + self.fund_list = self.fund_list[: int(limit_nums)] + except Exception as e: + logger.warning(f"Cannot use limit_nums={limit_nums}, the parameter will be ignored") + + self.fund_data = FundData( + timezone=self._timezone, + start=start, + end=end, + interval=interval, + delay=delay, + show_1min_logging=show_1min_logging, + ) + + @property + @abc.abstractmethod + def min_numbers_trading(self): + # daily, one year: 252 / 4 + # us 1min, a week: 6.5 * 60 * 5 + # cn 1min, a week: 4 * 60 * 5 + raise NotImplementedError("rewrite min_numbers_trading") + + @abc.abstractmethod + def get_fund_list(self): + raise NotImplementedError("rewrite get_fund_list") + + @property + @abc.abstractmethod + def _timezone(self): + raise NotImplementedError("rewrite get_timezone") + + def save_fund(self, symbol, df: pd.DataFrame): + """save fund data to file + + Parameters + ---------- + symbol: str + fund code + df : pd.DataFrame + df.columns must contain "symbol" and "datetime" + """ + if df.empty: + logger.warning(f"{symbol} is empty") + return + + symbol = code_to_fname(symbol) + stock_path = self.save_dir.joinpath(f"{symbol}.csv") + df["symbol"] = symbol + if stock_path.exists(): + _old_df = pd.read_csv(stock_path) + df = _old_df.append(df, sort=False) + df.to_csv(stock_path, index=False) + + def _save_small_data(self, symbol, df): + if len(df) <= self.min_numbers_trading: + logger.warning(f"the number of trading days of {symbol} is less than {self.min_numbers_trading}!") + _temp = self._mini_symbol_map.setdefault(symbol, []) + _temp.append(df.copy()) + return None + else: + if symbol in self._mini_symbol_map: + self._mini_symbol_map.pop(symbol) + return symbol + + def _get_data(self, symbol): + _result = None + df = self.fund_data.get_data(symbol) + if isinstance(df, pd.DataFrame): + if not df.empty: + if self._check_small_data: + if self._save_small_data(symbol, df) is not None: + _result = symbol + self.save_fund(symbol, df) + else: + _result = symbol + self.save_fund(symbol, df) + return _result + + def _collector(self, fund_list): + + error_symbol = [] + with ThreadPoolExecutor(max_workers=self.max_workers) as executor: + with tqdm(total=len(fund_list)) as p_bar: + for _symbol, _result in zip(fund_list, executor.map(self._get_data, fund_list)): + if _result is None: + error_symbol.append(_symbol) + p_bar.update() + print(error_symbol) + logger.info(f"error symbol nums: {len(error_symbol)}") + logger.info(f"current get symbol nums: {len(fund_list)}") + error_symbol.extend(self._mini_symbol_map.keys()) + return sorted(set(error_symbol)) + + def collector_data(self): + """collector data""" + logger.info("start collector fund data......") + fund_list = self.fund_list + for i in range(self._max_collector_count): + if not fund_list: + break + logger.info(f"getting data: {i+1}") + fund_list = self._collector(fund_list) + logger.info(f"{i+1} finish.") + for _symbol, _df_list in self._mini_symbol_map.items(): + self.save_fund(_symbol, pd.concat(_df_list, sort=False).drop_duplicates(["date"]).sort_values(["date"])) + if self._mini_symbol_map: + logger.warning(f"less than {self.min_numbers_trading} stock list: {list(self._mini_symbol_map.keys())}") + logger.info(f"total {len(self.fund_list)}, error: {len(set(fund_list))}") + +class FundollectorCN(FundCollector, ABC): + def get_fund_list(self): + logger.info("get cn fund symbols......") + symbols = get_en_fund_symbols() + logger.info(f"get {len(symbols)} symbols.") + return symbols + + @property + def _timezone(self): + return "Asia/Shanghai" + + +class FundCollectorCN1d(FundollectorCN): + @property + def min_numbers_trading(self): + return 252 / 4 + +class Run: + def __init__(self, source_dir=None, normalize_dir=None, max_workers=4, region=REGION_CN): + """ + + Parameters + ---------- + source_dir: str + The directory where the raw data collected from the Internet is saved, default "Path(__file__).parent/source" + normalize_dir: str + Directory for normalize data, default "Path(__file__).parent/normalize" + max_workers: int + Concurrent number, default is 4 + region: str + region, value from ["CN", "US"], default "CN" + """ + if source_dir is None: + source_dir = CUR_DIR.joinpath("source") + self.source_dir = Path(source_dir).expanduser().resolve() + self.source_dir.mkdir(parents=True, exist_ok=True) + + if normalize_dir is None: + normalize_dir = CUR_DIR.joinpath("normalize") + self.normalize_dir = Path(normalize_dir).expanduser().resolve() + self.normalize_dir.mkdir(parents=True, exist_ok=True) + + self._cur_module = importlib.import_module("collector") + self.max_workers = max_workers + self.region = region + + def download_data( + self, + max_collector_count=2, + delay=0, + start=None, + end=None, + interval="1d", + check_data_length=False, + limit_nums=None, + show_1min_logging=False, + ): + """download data from Internet + + Parameters + ---------- + max_collector_count: int + default 2 + delay: float + time.sleep(delay), default 0 + interval: str + freq, value from [1min, 1d], default 1d + start: str + start datetime, default "2000-01-01" + end: str + end datetime, default ``pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1))`` + check_data_length: bool + check data length, by default False + limit_nums: int + using for debug, by default None + show_1min_logging: bool + show 1m logging, by default False; if True, there may be many warning logs + + Examples + --------- + # get daily data + $ python collector.py download_data --source_dir ~/.qlib/fund_data/source/cn_1d --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d + """ + + _class = getattr( + self._cur_module, f"FundCollector{self.region.upper()}{interval}" + ) # type: Type[FundCollector] + _class( + self.source_dir, + max_workers=self.max_workers, + max_collector_count=max_collector_count, + delay=delay, + start=start, + end=end, + interval=interval, + check_data_length=check_data_length, + limit_nums=limit_nums, + show_1min_logging=show_1min_logging, + ).collector_data() + +if __name__ == "__main__": + fire.Fire(Run) diff --git a/scripts/data_collector/utils.py b/scripts/data_collector/utils.py index 5f34aae7d..3319025fc 100644 --- a/scripts/data_collector/utils.py +++ b/scripts/data_collector/utils.py @@ -34,6 +34,7 @@ _BENCH_CALENDAR_LIST = None _ALL_CALENDAR_LIST = None _HS_SYMBOLS = None _US_SYMBOLS = None +_EN_FUND_SYMBOLS = None _CALENDAR_MAP = {} # NOTE: Until 2020-10-20 20:00:00 @@ -220,6 +221,42 @@ def get_us_stock_symbols(qlib_data_path: [str, Path] = None) -> list: return _US_SYMBOLS +def get_en_fund_symbols(qlib_data_path: [str, Path] = None) -> list: + """get en fund symbols + + Returns + ------- + fund symbols in China + """ + global _EN_FUND_SYMBOLS + + @deco_retry + def _get_eastmoney(): + url = "http://fund.eastmoney.com/js/fundcode_search.js" + resp = requests.get(url) + if resp.status_code != 200: + raise ValueError("request error") + try: + _symbols = [] + for sub_data in re.findall(r"[\[](.*?)[\]]", resp.content.decode().split("= [")[-1].replace("];", "")): + data = sub_data.replace("\"","").replace("'","") + # TODO: do we need other informations, like fund_name from ['000001', 'HXCZHH', '华夏成长混合', '混合型', 'HUAXIACHENGZHANGHUNHE'] + _symbols.append(data.split(",")[0]) + except Exception as e: + logger.warning(f"request error: {e}") + raise + if len(_symbols) < 8000: + raise ValueError("request error") + return _symbols + + if _EN_FUND_SYMBOLS is None: + _all_symbols = _get_eastmoney() + + _EN_FUND_SYMBOLS = sorted(set(_all_symbols)) + + return _EN_FUND_SYMBOLS + + def symbol_suffix_to_prefix(symbol: str, capital: bool = True) -> str: """symbol suffix to prefix From db80b620d8408998cab30e4a6d51b9d038264c7e Mon Sep 17 00:00:00 2001 From: wangershi Date: Sun, 28 Feb 2021 17:03:14 +0800 Subject: [PATCH 032/134] ready for collector --- scripts/data_collector/fund/collector.py | 79 ++++++-------------- scripts/data_collector/fund/requirements.txt | 10 +++ 2 files changed, 32 insertions(+), 57 deletions(-) diff --git a/scripts/data_collector/fund/collector.py b/scripts/data_collector/fund/collector.py index 404b6af0e..f9b2a6775 100644 --- a/scripts/data_collector/fund/collector.py +++ b/scripts/data_collector/fund/collector.py @@ -20,20 +20,16 @@ import pandas as pd from tqdm import tqdm from loguru import logger from dateutil.tz import tzlocal -from qlib.utils import code_to_fname, fname_to_code CUR_DIR = Path(__file__).resolve().parent sys.path.append(str(CUR_DIR.parent.parent)) -from data_collector.utils import get_calendar_list, get_en_fund_symbols +from data_collector.utils import get_en_fund_symbols INDEX_BENCH_URL = "http://api.fund.eastmoney.com/f10/lsjz?callback=jQuery_&fundCode={index_code}&pageIndex=1&pageSize={numberOfHistoricalDaysToCrawl}&startDate={startDate}&endDate={endDate}" REGION_CN = "CN" -REGION_US = "US" - class FundData: START_DATETIME = pd.Timestamp("2000-01-01") - HIGH_FREQ_START_DATETIME = pd.Timestamp(datetime.datetime.now() - pd.Timedelta(days=5 * 6)) END_DATETIME = pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1)) INTERVAL_1d = "1d" @@ -44,7 +40,6 @@ class FundData: end=None, interval="1d", delay=0, - show_1min_logging: bool = False, ): """ @@ -60,22 +55,15 @@ class FundData: start datetime, default None end: str end datetime, default None - show_1min_logging: bool - show 1min logging, by default False; if True, there may be many warning logs """ self._timezone = tzlocal() if timezone is None else timezone self._delay = delay self._interval = interval - self._show_1min_logging = show_1min_logging self.start_datetime = pd.Timestamp(str(start)) if start else self.START_DATETIME self.end_datetime = min(pd.Timestamp(str(end)) if end else self.END_DATETIME, self.END_DATETIME) if self._interval != self.INTERVAL_1d: raise ValueError(f"interval error: {self._interval}") - # using for 1min - self._next_datetime = self.convert_datetime(self.start_datetime.date() + pd.Timedelta(days=1), self._timezone) - self._latest_datetime = self.convert_datetime(self.end_datetime.date(), self._timezone) - self.start_datetime = self.convert_datetime(self.start_datetime, self._timezone) self.end_datetime = self.convert_datetime(self.end_datetime, self._timezone) @@ -92,33 +80,26 @@ class FundData: time.sleep(self._delay) @staticmethod - def get_data_from_remote(symbol, interval, start, end, show_1min_logging: bool = False): + def get_data_from_remote(symbol, interval, start, end): error_msg = f"{symbol}-{interval}-{start}-{end}" try: - _resp = None - # TODO: numberOfHistoricalDaysToCrawl should be bigger enouhg - url = INDEX_BENCH_URL.format(index_code=symbol, numberOfHistoricalDaysToCrawl=100, startDate=start, endDate=end) + url = INDEX_BENCH_URL.format(index_code=symbol, numberOfHistoricalDaysToCrawl=10000, startDate=start, endDate=end) resp = requests.get(url, headers={"referer": "http://fund.eastmoney.com/110022.html"}) if resp.status_code != 200: raise ValueError("request error") - try: - data = json.loads(resp.text.split("(")[-1].split(")")[0]) + + data = json.loads(resp.text.split("(")[-1].split(")")[0]) - # Some funds don't show the net value, example: http://fundf10.eastmoney.com/jjjz_010288.html - SYType = data["Data"]["SYType"] - if (SYType == "每万份收益") or (SYType == "每百份收益") or (SYType == "每百万份收益"): - raise Exception("The fund contains 每*份收益") + # Some funds don't show the net value, example: http://fundf10.eastmoney.com/jjjz_010288.html + SYType = data["Data"]["SYType"] + if (SYType == "每万份收益") or (SYType == "每百份收益") or (SYType == "每百万份收益"): + raise Exception("The fund contains 每*份收益") - _resp = pd.DataFrame( - data["Data"]["LSJZList"] - ) - - except Exception as e: - logger.warning(f"request error: {e}") - raise + # TODO: should we sort the value by datetime? + _resp = pd.DataFrame(data["Data"]["LSJZList"]) if isinstance(_resp, pd.DataFrame): return _resp.reset_index() @@ -134,7 +115,6 @@ class FundData: interval=_remote_interval, start=start_, end=end_, - show_1min_logging=self._show_1min_logging, ) if self._interval == self.INTERVAL_1d: @@ -156,14 +136,13 @@ class FundCollector: delay=0, check_data_length: bool = False, limit_nums: int = None, - show_1min_logging: bool = False, ): """ Parameters ---------- save_dir: str - stock save dir + fund save dir max_workers: int workers, default 4 max_collector_count: int @@ -180,8 +159,6 @@ class FundCollector: check data length, by default False limit_nums: int using for debug, by default None - show_1min_logging: bool - show 1m logging, by default False; if True, there may be many warning logs """ self.save_dir = Path(save_dir).expanduser().resolve() self.save_dir.mkdir(parents=True, exist_ok=True) @@ -206,7 +183,6 @@ class FundCollector: end=end, interval=interval, delay=delay, - show_1min_logging=show_1min_logging, ) @property @@ -240,13 +216,14 @@ class FundCollector: logger.warning(f"{symbol} is empty") return - symbol = code_to_fname(symbol) - stock_path = self.save_dir.joinpath(f"{symbol}.csv") + fund_path = self.save_dir.joinpath(f"{symbol}.csv") df["symbol"] = symbol - if stock_path.exists(): - _old_df = pd.read_csv(stock_path) + if fund_path.exists(): + # TODO: read the fund code as str, not int, like "000001" shouldn't be "1" + _old_df = pd.read_csv(fund_path) + # TODO: remove the duplicate date df = _old_df.append(df, sort=False) - df.to_csv(stock_path, index=False) + df.to_csv(fund_path, index=False) def _save_small_data(self, symbol, df): if len(df) <= self.min_numbers_trading: @@ -274,7 +251,6 @@ class FundCollector: return _result def _collector(self, fund_list): - error_symbol = [] with ThreadPoolExecutor(max_workers=self.max_workers) as executor: with tqdm(total=len(fund_list)) as p_bar: @@ -301,7 +277,7 @@ class FundCollector: for _symbol, _df_list in self._mini_symbol_map.items(): self.save_fund(_symbol, pd.concat(_df_list, sort=False).drop_duplicates(["date"]).sort_values(["date"])) if self._mini_symbol_map: - logger.warning(f"less than {self.min_numbers_trading} stock list: {list(self._mini_symbol_map.keys())}") + logger.warning(f"less than {self.min_numbers_trading} fund list: {list(self._mini_symbol_map.keys())}") logger.info(f"total {len(self.fund_list)}, error: {len(set(fund_list))}") class FundollectorCN(FundCollector, ABC): @@ -322,30 +298,23 @@ class FundCollectorCN1d(FundollectorCN): return 252 / 4 class Run: - def __init__(self, source_dir=None, normalize_dir=None, max_workers=4, region=REGION_CN): + def __init__(self, source_dir=None, max_workers=4, region=REGION_CN): """ Parameters ---------- source_dir: str The directory where the raw data collected from the Internet is saved, default "Path(__file__).parent/source" - normalize_dir: str - Directory for normalize data, default "Path(__file__).parent/normalize" max_workers: int Concurrent number, default is 4 region: str - region, value from ["CN", "US"], default "CN" + region, value from ["CN"], default "CN" """ if source_dir is None: source_dir = CUR_DIR.joinpath("source") self.source_dir = Path(source_dir).expanduser().resolve() self.source_dir.mkdir(parents=True, exist_ok=True) - if normalize_dir is None: - normalize_dir = CUR_DIR.joinpath("normalize") - self.normalize_dir = Path(normalize_dir).expanduser().resolve() - self.normalize_dir.mkdir(parents=True, exist_ok=True) - self._cur_module = importlib.import_module("collector") self.max_workers = max_workers self.region = region @@ -359,7 +328,6 @@ class Run: interval="1d", check_data_length=False, limit_nums=None, - show_1min_logging=False, ): """download data from Internet @@ -375,12 +343,10 @@ class Run: start datetime, default "2000-01-01" end: str end datetime, default ``pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1))`` - check_data_length: bool + check_data_length: bool # if this param useful? check data length, by default False limit_nums: int using for debug, by default None - show_1min_logging: bool - show 1m logging, by default False; if True, there may be many warning logs Examples --------- @@ -401,7 +367,6 @@ class Run: interval=interval, check_data_length=check_data_length, limit_nums=limit_nums, - show_1min_logging=show_1min_logging, ).collector_data() if __name__ == "__main__": diff --git a/scripts/data_collector/fund/requirements.txt b/scripts/data_collector/fund/requirements.txt index e69de29bb..11c6730c0 100644 --- a/scripts/data_collector/fund/requirements.txt +++ b/scripts/data_collector/fund/requirements.txt @@ -0,0 +1,10 @@ +loguru +fire +requests +numpy +pandas +tqdm +lxml +loguru +yahooquery +json \ No newline at end of file From 3082f6ac1ba201ebcdeb115f27e97f75d2017439 Mon Sep 17 00:00:00 2001 From: wangershi Date: Sun, 28 Feb 2021 19:06:40 +0800 Subject: [PATCH 033/134] ready for dump_bin --- scripts/data_collector/fund/README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/scripts/data_collector/fund/README.md b/scripts/data_collector/fund/README.md index b14938a3d..bcbbbcba7 100644 --- a/scripts/data_collector/fund/README.md +++ b/scripts/data_collector/fund/README.md @@ -23,7 +23,7 @@ python collector.py download_data --source_dir ~/.qlib/fund_data/source/cn_1d -- # dump data cd qlib/scripts -python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/cn_1d_nor --qlib_dir ~/.qlib/qlib_data/qlib_cn_1d --freq day --exclude_fields date,adjclose,dividends,splits,symbol +python dump_bin.py dump_all --csv_path ~/.qlib/fund_data/source/cn_1d --qlib_dir ~/.qlib/qlib_data/cn_fund_data --freq day --date_field_name FSRQ --include_fields DWJZ,LJJZ ``` @@ -33,8 +33,8 @@ python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/cn_1d_nor --qli import qlib from qlib.data import D -qlib.init(provider_uri="~/.qlib/qlib_data/qlib_cn_1d", region="CN") -df = D.features(D.instruments("all"), ["$close"], freq="day") +qlib.init(provider_uri="~/.qlib/qlib_data/cn_fund_data") +df = D.features(D.instruments(market="all"), ["$DWJZ", "$LJJZ"], freq="day") ``` @@ -45,5 +45,5 @@ pythono collector.py collector_data --help ## Parameters -- interval: 1min or 1d -- region: CN or US +- interval: 1d +- region: CN From 82353b20e18462d8b1a67742b2b6210dc80461ee Mon Sep 17 00:00:00 2001 From: wangershi Date: Mon, 1 Mar 2021 21:10:46 +0800 Subject: [PATCH 034/134] black format --- scripts/data_collector/fund/collector.py | 14 +++++++++----- scripts/data_collector/utils.py | 2 +- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/scripts/data_collector/fund/collector.py b/scripts/data_collector/fund/collector.py index f9b2a6775..a2b7089a1 100644 --- a/scripts/data_collector/fund/collector.py +++ b/scripts/data_collector/fund/collector.py @@ -28,6 +28,7 @@ from data_collector.utils import get_en_fund_symbols INDEX_BENCH_URL = "http://api.fund.eastmoney.com/f10/lsjz?callback=jQuery_&fundCode={index_code}&pageIndex=1&pageSize={numberOfHistoricalDaysToCrawl}&startDate={startDate}&endDate={endDate}" REGION_CN = "CN" + class FundData: START_DATETIME = pd.Timestamp("2000-01-01") END_DATETIME = pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1)) @@ -85,12 +86,14 @@ class FundData: try: # TODO: numberOfHistoricalDaysToCrawl should be bigger enouhg - url = INDEX_BENCH_URL.format(index_code=symbol, numberOfHistoricalDaysToCrawl=10000, startDate=start, endDate=end) + url = INDEX_BENCH_URL.format( + index_code=symbol, numberOfHistoricalDaysToCrawl=10000, startDate=start, endDate=end + ) resp = requests.get(url, headers={"referer": "http://fund.eastmoney.com/110022.html"}) if resp.status_code != 200: raise ValueError("request error") - + data = json.loads(resp.text.split("(")[-1].split(")")[0]) # Some funds don't show the net value, example: http://fundf10.eastmoney.com/jjjz_010288.html @@ -280,6 +283,7 @@ class FundCollector: logger.warning(f"less than {self.min_numbers_trading} fund list: {list(self._mini_symbol_map.keys())}") logger.info(f"total {len(self.fund_list)}, error: {len(set(fund_list))}") + class FundollectorCN(FundCollector, ABC): def get_fund_list(self): logger.info("get cn fund symbols......") @@ -297,6 +301,7 @@ class FundCollectorCN1d(FundollectorCN): def min_numbers_trading(self): return 252 / 4 + class Run: def __init__(self, source_dir=None, max_workers=4, region=REGION_CN): """ @@ -354,9 +359,7 @@ class Run: $ python collector.py download_data --source_dir ~/.qlib/fund_data/source/cn_1d --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d """ - _class = getattr( - self._cur_module, f"FundCollector{self.region.upper()}{interval}" - ) # type: Type[FundCollector] + _class = getattr(self._cur_module, f"FundCollector{self.region.upper()}{interval}") # type: Type[FundCollector] _class( self.source_dir, max_workers=self.max_workers, @@ -369,5 +372,6 @@ class Run: limit_nums=limit_nums, ).collector_data() + if __name__ == "__main__": fire.Fire(Run) diff --git a/scripts/data_collector/utils.py b/scripts/data_collector/utils.py index 3319025fc..5d5822f91 100644 --- a/scripts/data_collector/utils.py +++ b/scripts/data_collector/utils.py @@ -239,7 +239,7 @@ def get_en_fund_symbols(qlib_data_path: [str, Path] = None) -> list: try: _symbols = [] for sub_data in re.findall(r"[\[](.*?)[\]]", resp.content.decode().split("= [")[-1].replace("];", "")): - data = sub_data.replace("\"","").replace("'","") + data = sub_data.replace('"', "").replace("'", "") # TODO: do we need other informations, like fund_name from ['000001', 'HXCZHH', '华夏成长混合', '混合型', 'HUAXIACHENGZHANGHUNHE'] _symbols.append(data.split(",")[0]) except Exception as e: From 6e2ce6f1dce676f1c82bed2446e84ba43e4bc29b Mon Sep 17 00:00:00 2001 From: meng-ustc Date: Tue, 2 Mar 2021 12:17:05 +0900 Subject: [PATCH 035/134] Add the results of DoubleEnsemble --- examples/benchmarks/README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/examples/benchmarks/README.md b/examples/benchmarks/README.md index bcd2279b3..c5bfced6d 100644 --- a/examples/benchmarks/README.md +++ b/examples/benchmarks/README.md @@ -16,7 +16,7 @@ The numbers shown below demonstrate the performance of the entire `workflow` of | LSTM (Sepp Hochreiter, et al.) | Alpha360 | 0.0443±0.01 | 0.3401±0.05| 0.0536±0.01 | 0.4248±0.05 | 0.0627±0.03 | 0.8441±0.48| -0.0882±0.03 | | ALSTM (Yao Qin, et al.) | Alpha360 | 0.0493±0.01 | 0.3778±0.06| 0.0585±0.00 | 0.4606±0.04 | 0.0513±0.03 | 0.6727±0.38| -0.1085±0.02 | | GATs (Petar Velickovic, et al.) | Alpha360 | 0.0475±0.00 | 0.3515±0.02| 0.0592±0.00 | 0.4585±0.01 | 0.0876±0.02 | 1.1513±0.27| -0.0795±0.02 | - +| DoubleEnsemble (Chuheng Zhang, et al.) | Alpha360 | 0.0407±0.00| 0.3053±0.00 | 0.0490±0.00 | 0.3840±0.00 | 0.0380±0.02 | 0.5000±0.21 | -0.0984±0.02 | ## Alpha158 dataset | Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown | |---|---|---|---|---|---|---|---|---| @@ -31,5 +31,7 @@ The numbers shown below demonstrate the performance of the entire `workflow` of | LSTM (Sepp Hochreiter, et al.) | Alpha158 (with selected 20 features) | 0.0312±0.00 | 0.2394±0.04| 0.0418±0.00 | 0.3324±0.03 | 0.0298±0.02 | 0.4198±0.33| -0.1348±0.03 | | ALSTM (Yao Qin, et al.) | Alpha158 (with selected 20 features) | 0.0385±0.01 | 0.3022±0.06| 0.0478±0.00 | 0.3874±0.04 | 0.0486±0.03 | 0.7141±0.45| -0.1088±0.03 | | GATs (Petar Velickovic, et al.) | Alpha158 (with selected 20 features) | 0.0349±0.00 | 0.2511±0.01| 0.0457±0.00 | 0.3537±0.01 | 0.0578±0.02 | 0.8221±0.25| -0.0824±0.02 | +| DoubleEnsemble (Chuheng Zhang, et al.) | Alpha158 | 0.0544±0.00 | 0.4338±0.01 | 0.0523±0.00 | 0.4257±0.01 | 0.1253±0.01 | 1.4105±0.14 | -0.0902±0.01 | - The selected 20 features are based on the feature importance of a lightgbm-based model. +- The base model of DoubleEnsemble is LGBM. From 1de4def444a44247aeb80db1adadb8fcad5beb39 Mon Sep 17 00:00:00 2001 From: meng-ustc Date: Tue, 2 Mar 2021 16:14:56 +0900 Subject: [PATCH 036/134] Update parameter names: 'k' and 'base' --- ...rkflow_config_doubleensemble_Alpha158.yaml | 4 +-- ...rkflow_config_doubleensemble_Alpha360.yaml | 4 +-- qlib/contrib/model/double_ensemble.py | 30 +++++++++---------- 3 files changed, 19 insertions(+), 19 deletions(-) diff --git a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml index 74923c0e5..a12df802d 100644 --- a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml +++ b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml @@ -30,9 +30,9 @@ task: class: DEnsembleModel module_path: qlib.contrib.model.double_ensemble kwargs: - base: "gbm" + base_model: "gbm" loss: mse - k: 6 + num_models: 6 enable_sr: True enable_fs: True alpha1: 1 diff --git a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml index 3351cefc5..415448f0b 100644 --- a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml +++ b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml @@ -37,9 +37,9 @@ task: class: DEnsembleModel module_path: qlib.contrib.model.double_ensemble kwargs: - base: "gbm" + base_model: "gbm" loss: mse - k: 6 + num_models: 6 enable_sr: True enable_fs: True alpha1: 1 diff --git a/qlib/contrib/model/double_ensemble.py b/qlib/contrib/model/double_ensemble.py index c96b69e8b..a340489c2 100644 --- a/qlib/contrib/model/double_ensemble.py +++ b/qlib/contrib/model/double_ensemble.py @@ -16,9 +16,9 @@ class DEnsembleModel(Model): def __init__( self, - base="gbm", + base_model="gbm", loss="mse", - k=6, + num_models=6, enable_sr=True, enable_fs=True, alpha1=1.0, @@ -31,8 +31,8 @@ class DEnsembleModel(Model): epochs=100, **kwargs ): - self.base = base # "gbm" or "mlp", specifically, we use lgbm for "gbm" - self.k = k + self.base_model = base_model # "gbm" or "mlp", specifically, we use lgbm for "gbm" + self.num_models = num_models # the number of sub-models self.enable_sr = enable_sr self.enable_fs = enable_fs self.alpha1 = alpha1 @@ -43,8 +43,8 @@ class DEnsembleModel(Model): if not len(sample_ratios) == bins_fs: raise ValueError("The length of sample_ratios should be equal to bins_fs.") self.sample_ratios = sample_ratios - if not len(sub_weights) == k: - raise ValueError("The length of sub_weights should be equal to k.") + if not len(sub_weights) == num_models: + raise ValueError("The length of sub_weights should be equal to num_models.") self.sub_weights = sub_weights self.epochs = epochs self.logger = get_module_logger("DEnsembleModel") @@ -65,27 +65,27 @@ class DEnsembleModel(Model): weights = pd.Series(np.ones(N, dtype=float)) # initialize the features features = x_train.columns - pred_sub = pd.DataFrame(np.zeros((N, self.k), dtype=float), index=x_train.index) - # train k sub-models - for i_k in range(self.k): + pred_sub = pd.DataFrame(np.zeros((N, self.num_models), dtype=float), index=x_train.index) + # train sub-models + for k in range(self.num_models): self.sub_features.append(features) - self.logger.info("Training sub-model: ({}/{})".format(i_k + 1, self.k)) + self.logger.info("Training sub-model: ({}/{})".format(k + 1, self.num_models)) model_k = self.train_submodel(df_train, df_valid, weights, features) self.ensemble.append(model_k) # no further sample re-weight and feature selection needed for the last sub-model - if i_k + 1 == self.k: + if k + 1 == self.num_models: break self.logger.info("Retrieving loss curve and loss values...") loss_curve = self.retrieve_loss_curve(model_k, df_train, features) pred_k = self.predict_sub(model_k, df_train, features) - pred_sub.iloc[:, i_k] = pred_k - pred_ensemble = pred_sub.iloc[:, : i_k + 1].mean(axis=1) + pred_sub.iloc[:, k] = pred_k + pred_ensemble = pred_sub.iloc[:, : k + 1].mean(axis=1) loss_values = pd.Series(self.get_loss(y_train.values.squeeze(), pred_ensemble.values)) if self.enable_sr: self.logger.info("Sample re-weighting...") - weights = self.sample_reweight(loss_curve, loss_values, i_k + 1) + weights = self.sample_reweight(loss_curve, loss_values, k + 1) if self.enable_fs: self.logger.info("Feature selection...") @@ -209,7 +209,7 @@ class DEnsembleModel(Model): raise ValueError("not implemented yet") def retrieve_loss_curve(self, model, df_train, features): - if self.base == "gbm": + if self.base_model == "gbm": num_trees = model.num_trees() x_train, y_train = df_train["feature"].loc[:, features], df_train["label"] # Lightgbm need 1D array as its label From b592669d1fc1556bfee6b0a5ac9ad85e50dbc30a Mon Sep 17 00:00:00 2001 From: you-n-g Date: Tue, 2 Mar 2021 20:23:27 +0800 Subject: [PATCH 037/134] Update Index Report --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 368e2385f..5a035662b 100644 --- a/README.md +++ b/README.md @@ -319,6 +319,7 @@ Qlib data are stored in a compact format, which is efficient to be combined into # Related Reports +- [【华泰金工林晓明团队】图神经网络选股与Qlib实践——华泰人工智能系列之四十二](https://mp.weixin.qq.com/s/w5fDB6oAv9dO6vlhf1kmhA) - [Guide To Qlib: Microsoft’s AI Investment Platform](https://analyticsindiamag.com/qlib/) - [【华泰金工林晓明团队】微软AI量化投资平台Qlib体验——华泰人工智能系列之四十](https://mp.weixin.qq.com/s/Brcd7im4NibJOJzZfMn6tQ) - [微软也搞AI量化平台？还是开源的！](https://mp.weixin.qq.com/s/47bP5YwxfTp2uTHjUBzJQQ) From c48b4c99712fbacd6d477e43d8209796d877d682 Mon Sep 17 00:00:00 2001 From: D-X-Y Date: Tue, 2 Mar 2021 21:06:32 +0800 Subject: [PATCH 038/134] Make Wrapper with a informative str repr. --- qlib/utils/__init__.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/qlib/utils/__init__.py b/qlib/utils/__init__.py index be7969b65..484c0bbef 100644 --- a/qlib/utils/__init__.py +++ b/qlib/utils/__init__.py @@ -722,6 +722,10 @@ class Wrapper: def register(self, provider): self._provider = provider + def __repr__(self): + return ('{name}(provider={provider})'.format(name=self.__class__.__name__, + provider=self._provider)) + def __getattr__(self, key): if self._provider is None: raise AttributeError("Please run qlib.init() first using qlib") From dbc8ca7379ba771228ea253f174ab0132745db01 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Tue, 2 Mar 2021 22:15:30 +0800 Subject: [PATCH 039/134] Fix pylint by black -l 120 --- qlib/utils/__init__.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/qlib/utils/__init__.py b/qlib/utils/__init__.py index 484c0bbef..6640dae2c 100644 --- a/qlib/utils/__init__.py +++ b/qlib/utils/__init__.py @@ -723,8 +723,7 @@ class Wrapper: self._provider = provider def __repr__(self): - return ('{name}(provider={provider})'.format(name=self.__class__.__name__, - provider=self._provider)) + return "{name}(provider={provider})".format(name=self.__class__.__name__, provider=self._provider) def __getattr__(self, key): if self._provider is None: From a1fb10f7cff4495d5c63a188d534a922e210e77d Mon Sep 17 00:00:00 2001 From: bxdd Date: Wed, 3 Mar 2021 00:44:52 +0900 Subject: [PATCH 040/134] update FAQ doc --- docs/FAQ/FAQ.rst | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/docs/FAQ/FAQ.rst b/docs/FAQ/FAQ.rst index ba6f77b47..df910e564 100644 --- a/docs/FAQ/FAQ.rst +++ b/docs/FAQ/FAQ.rst @@ -70,3 +70,31 @@ If the issue is not resolved, use ``keys *`` to find if multiple keys exist. If Also, feel free to post a new issue in our GitHub repository. We always check each issue carefully and try our best to solve them. + +3. ModuleNotFoundError: No module named 'qlib.data._libs.rolling' +------------------------------------------------------------------------------------------------------------------------------------ + +.. code-block:: python + + #### Do not import qlib package in the repository directory in case of importing qlib from . without compiling ##### + Traceback (most recent call last): + File "", line 1, in + File "qlib/qlib/__init__.py", line 19, in init + from .data.cache import H + File "qlib/qlib/data/__init__.py", line 8, in + from .data import ( + File "qlib/qlib/data/data.py", line 20, in + from .cache import H + File "qlib/qlib/data/cache.py", line 36, in + from .ops import Operators + File "qlib/qlib/data/ops.py", line 19, in + from ._libs.rolling import rolling_slope, rolling_rsquare, rolling_resi + ModuleNotFoundError: No module named 'qlib.data._libs.rolling' + +- If the error occurs when importing ``qlib`` package with ``PyCharm`` IDE, users can execute the following command in the project root folder to compile Cython files and generate executable files: + + .. code-block:: bash + + python setup.py build_ext --inplace + +- If the error occurs when importing ``qlib`` package with command ``python`` , users need to change the running directory to ensure that the script does not run in the project directory. \ No newline at end of file From 5378d261b41c1b104cb8a391ac29a059abee8f3f Mon Sep 17 00:00:00 2001 From: bxdd Date: Wed, 3 Mar 2021 00:47:50 +0900 Subject: [PATCH 041/134] update ops.py docstring --- qlib/data/ops.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/qlib/data/ops.py b/qlib/data/ops.py index 940c24002..94f3ff5b8 100644 --- a/qlib/data/ops.py +++ b/qlib/data/ops.py @@ -1182,7 +1182,7 @@ class Slope(Rolling): Returns ---------- Expression - a feature instance with regression slope of given window + a feature instance with linear regression slope of given window """ def __init__(self, feature, N): @@ -1210,7 +1210,7 @@ class Rsquare(Rolling): Returns ---------- Expression - a feature instance with regression r-value square of given window + a feature instance with linear regression r-value square of given window """ def __init__(self, feature, N): From a9a70dfddf131a278ba1a3d2c7395bf94d5fab08 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Wed, 3 Mar 2021 06:47:52 +0000 Subject: [PATCH 042/134] Update repr for DatasetH and ExpManager --- .gitignore | 2 ++ qlib/data/dataset/__init__.py | 17 +++++++++++------ qlib/workflow/__init__.py | 10 ++++++++-- qlib/workflow/expm.py | 3 +++ 4 files changed, 24 insertions(+), 8 deletions(-) diff --git a/.gitignore b/.gitignore index 5b3745a02..0ddd5d21f 100644 --- a/.gitignore +++ b/.gitignore @@ -34,3 +34,5 @@ tags .pytest_cache/ .vscode/ + +*.swp diff --git a/qlib/data/dataset/__init__.py b/qlib/data/dataset/__init__.py index 8ff8c1210..ecbeebc95 100644 --- a/qlib/data/dataset/__init__.py +++ b/qlib/data/dataset/__init__.py @@ -1,5 +1,5 @@ from ...utils.serial import Serializable -from typing import Union, List, Tuple +from typing import Union, List, Tuple, Dict, Text, Optional from ...utils import init_instance_by_config, np_ffill from ...log import get_module_logger from .handler import DataHandler, DataHandlerLP @@ -76,7 +76,7 @@ class DatasetH(Dataset): - The processing is related to data split. """ - def __init__(self, handler: Union[dict, DataHandler], segments: dict): + def __init__(self, handler: Union[Dict, DataHandler], segments: Dict): """ Parameters ---------- @@ -87,7 +87,7 @@ class DatasetH(Dataset): """ super().__init__(handler, segments) - def init(self, handler_kwargs: dict = None, segment_kwargs: dict = None): + def init(self, handler_kwargs: Optional[Dict] = None, segment_kwargs: Optional[Dict] = None): """ Initialize the DatasetH @@ -124,7 +124,7 @@ class DatasetH(Dataset): raise TypeError(f"param handler_kwargs must be type dict, not {type(segment_kwargs)}") self.segments = segment_kwargs.copy() - def setup_data(self, handler: Union[dict, DataHandler], segments: dict): + def setup_data(self, handler: Union[Dict, DataHandler], segments: Dict[Text, Tuple]): """ Setup the underlying data. @@ -156,6 +156,11 @@ class DatasetH(Dataset): self.handler = init_instance_by_config(handler, accept_types=DataHandler) self.segments = segments.copy() + def __repr__(self): + return "{name}(handler={handler}, segments={segments})".format( + name=self.__class__.__name__, handler=self.handler, segments=self.segments + ) + def _prepare_seg(self, slc: slice, **kwargs): """ Give a slice, retrieve the according data @@ -168,7 +173,7 @@ class DatasetH(Dataset): def prepare( self, - segments: Union[List[str], Tuple[str], str, slice], + segments: Union[List[Text], Tuple[Text], Text, slice], col_set=DataHandler.CS_ALL, data_key=DataHandlerLP.DK_I, **kwargs, @@ -178,7 +183,7 @@ class DatasetH(Dataset): Parameters ---------- - segments : Union[List[str], Tuple[str], str, slice] + segments : Union[List[Text], Tuple[Text], Text, slice] Describe the scope of the data to be prepared Here are some examples: diff --git a/qlib/workflow/__init__.py b/qlib/workflow/__init__.py index aedf73a9c..9602963d8 100644 --- a/qlib/workflow/__init__.py +++ b/qlib/workflow/__init__.py @@ -16,8 +16,11 @@ class QlibRecorder: def __init__(self, exp_manager): self.exp_manager = exp_manager + def __repr__(self): + return "{name}(manager={manager})".format(name=self.__class__.__name__, manager=self.exp_manager) + @contextmanager - def start(self, experiment_name=None, recorder_name=None): + def start(self, experiment_name=None, recorder_name=None, uri=None): """ Method to start an experiment. This method can only be called within a Python's `with` statement. Here is the example code: @@ -34,8 +37,11 @@ class QlibRecorder: name of the experiment one wants to start. recorder_name : str name of the recorder under the experiment one wants to start. + uri : str + the tracking uri of the experiment, where all the artifacts/metrics etc. will be stored. + The default uri are set in the qlib.config. """ - run = self.start_exp(experiment_name, recorder_name) + run = self.start_exp(experiment_name, recorder_name, uri) try: yield run except Exception as e: diff --git a/qlib/workflow/expm.py b/qlib/workflow/expm.py index a50dce7c9..e905434cb 100644 --- a/qlib/workflow/expm.py +++ b/qlib/workflow/expm.py @@ -25,6 +25,9 @@ class ExpManager: self.default_exp_name = default_exp_name self.active_experiment = None # only one experiment can active each time + def __repr__(self): + return "{name}(uri={uri})".format(name=self.__class__.__name__, uri=self.uri) + def start_exp(self, experiment_name=None, recorder_name=None, uri=None, **kwargs): """ Start an experiment. This method includes first get_or_create an experiment, and then From 229a39d0d353788f649d414c01e229ec1a3ff92e Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Wed, 3 Mar 2021 07:30:55 +0000 Subject: [PATCH 043/134] Fix typos in DataHandler's doc --- qlib/data/dataset/handler.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/qlib/data/dataset/handler.py b/qlib/data/dataset/handler.py index 2889c4465..050043ba6 100644 --- a/qlib/data/dataset/handler.py +++ b/qlib/data/dataset/handler.py @@ -35,7 +35,7 @@ class DataHandler(Serializable): The data handler try to maintain a handler with 2 level. `datetime` & `instruments`. - Any order of the index level can be suported(The order will implied in the data). + Any order of the index level can be suported (The order will be implied in the data). The order <`datetime`, `instruments`> will be used when the dataframe index name is missed. Example of the data: @@ -47,8 +47,8 @@ class DataHandler(Serializable): $close $volume Ref($close, 1) Mean($close, 3) $high-$low LABEL0 datetime instrument 2010-01-04 SH600000 81.807068 17145150.0 83.737389 83.016739 2.741058 0.0032 - SH600004 13.313329 11800983.0 13.313329 13.317701 0.183632 0.0042 - SH600005 37.796539 12231662.0 38.258602 37.919757 0.970325 0.0289 + SH600004 13.313329 11800983.0 13.313329 13.317701 0.183632 0.0042 + SH600005 37.796539 12231662.0 38.258602 37.919757 0.970325 0.0289 """ From 34b7da1dd88ad6886d36f44ddf7d8ccf44a66eda Mon Sep 17 00:00:00 2001 From: wangershi Date: Wed, 3 Mar 2021 22:49:48 +0800 Subject: [PATCH 044/134] add calendar list by threshold --- scripts/data_collector/utils.py | 55 +++++++++++++++++++++++++++++++++ 1 file changed, 55 insertions(+) diff --git a/scripts/data_collector/utils.py b/scripts/data_collector/utils.py index 5d5822f91..1a08c514f 100644 --- a/scripts/data_collector/utils.py +++ b/scripts/data_collector/utils.py @@ -2,6 +2,7 @@ # Licensed under the MIT License. import re +import os import time import bisect import pickle @@ -14,6 +15,9 @@ import pandas as pd from lxml import etree from loguru import logger from yahooquery import Ticker +from tqdm import tqdm +from functools import partial +from concurrent.futures import ProcessPoolExecutor HS_SYMBOLS_URL = "http://app.finance.ifeng.com/hq/list.php?type=stock_a&class={s_type}" @@ -94,6 +98,57 @@ def get_calendar_list(bench_code="CSI300") -> list: return calendar +def return_date_list(source_dir, date_field_name, file_path): + df = pd.read_csv(Path(source_dir).joinpath(file_path), sep=",", index_col=0) + + return df[date_field_name].to_list() + + +def get_calendar_list_by_ratio( + source_dir: [str, Path], date_field_name: str = "date", threshold: float = 0.5, max_workers: int = 16 +) -> list: + """get calendar list by selecting the date when few funds trade in this day + + Parameters + ---------- + source_dir: str or Path + The directory where the raw data collected from the Internet is saved + date_field_name: str + date field name, default is date + threshold: float + threshold to exclude some days when few funds trade in this day, default 0.5 + max_workers: int + Concurrent number, default is 16 + + Returns + ------- + history calendar list + """ + logger.info(f"get calendar list from {source_dir} by threshold = {threshold}......") + + _number_all_funds = len(os.listdir(source_dir)) + + _list_all_date = dict() + + _fun = partial(return_date_list, source_dir, date_field_name) + + with tqdm(total=_number_all_funds) as p_bar: + with ProcessPoolExecutor(max_workers=max_workers) as executor: + for date_list in executor.map(_fun, os.listdir(source_dir)): + for date in date_list: + if date in _list_all_date.keys(): + _list_all_date[date] += 1 + else: + _list_all_date[date] = 0 + + p_bar.update() + + _threshold_number = int(_number_all_funds * threshold) + calendar = [date for date in _list_all_date if _list_all_date[date] >= _threshold_number] + + return calendar + + def get_hs_stock_symbols() -> list: """get SH/SZ stock symbols From 592db903b3afbfd1628fd7e6ad4f150a5c5d13cc Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Thu, 4 Mar 2021 05:02:56 +0000 Subject: [PATCH 045/134] Update repr for Experiment & Recorder --- qlib/workflow/__init__.py | 6 ++++-- qlib/workflow/exp.py | 4 ++-- qlib/workflow/expm.py | 2 +- qlib/workflow/recorder.py | 4 ++-- tests/test_all_pipeline.py | 4 ++++ 5 files changed, 13 insertions(+), 7 deletions(-) diff --git a/qlib/workflow/__init__.py b/qlib/workflow/__init__.py index 9602963d8..c6bf0c86c 100644 --- a/qlib/workflow/__init__.py +++ b/qlib/workflow/__init__.py @@ -38,8 +38,10 @@ class QlibRecorder: recorder_name : str name of the recorder under the experiment one wants to start. uri : str - the tracking uri of the experiment, where all the artifacts/metrics etc. will be stored. - The default uri are set in the qlib.config. + The tracking uri of the experiment, where all the artifacts/metrics etc. will be stored. + The default uri are set in the qlib.config. Note that this uri argument will not change the one defined in the config file. + Therefore, the next time when user call this function in the same experiment, + they have to also specify this argument with the same value. Otherwise, inconsistent uri may occur. """ run = self.start_exp(experiment_name, recorder_name, uri) try: diff --git a/qlib/workflow/exp.py b/qlib/workflow/exp.py index c2548971a..7c98fd68f 100644 --- a/qlib/workflow/exp.py +++ b/qlib/workflow/exp.py @@ -11,7 +11,7 @@ from ..log import get_module_logger logger = get_module_logger("workflow", "INFO") -class Experiment: +class Experiment(object): """ This is the `Experiment` class for each experiment being run. The API is designed similar to mlflow. (The link: https://mlflow.org/docs/latest/python_api/mlflow.html) @@ -23,7 +23,7 @@ class Experiment: self.active_recorder = None # only one recorder can running each time def __repr__(self): - return str(self.info) + return "{name}(info={info})".format(name=self.__class__.__name__, info=self.info) def __str__(self): return str(self.info) diff --git a/qlib/workflow/expm.py b/qlib/workflow/expm.py index e905434cb..541507a73 100644 --- a/qlib/workflow/expm.py +++ b/qlib/workflow/expm.py @@ -14,7 +14,7 @@ from ..log import get_module_logger logger = get_module_logger("workflow", "INFO") -class ExpManager: +class ExpManager(object): """ This is the `ExpManager` class for managing experiments. The API is designed similar to mlflow. (The link: https://mlflow.org/docs/latest/python_api/mlflow.html) diff --git a/qlib/workflow/recorder.py b/qlib/workflow/recorder.py index ceb57150c..31077176d 100644 --- a/qlib/workflow/recorder.py +++ b/qlib/workflow/recorder.py @@ -11,7 +11,7 @@ from ..log import get_module_logger logger = get_module_logger("workflow", "INFO") -class Recorder: +class Recorder(object): """ This is the `Recorder` class for logging the experiments. The API is designed similar to mlflow. (The link: https://mlflow.org/docs/latest/python_api/mlflow.html) @@ -34,7 +34,7 @@ class Recorder: self.status = Recorder.STATUS_S def __repr__(self): - return str(self.info) + return "{name}(info={info})".format(name=self.__class__.__name__, info=self.info) def __str__(self): return str(self.info) diff --git a/tests/test_all_pipeline.py b/tests/test_all_pipeline.py index f6e77cba4..97f3f986a 100644 --- a/tests/test_all_pipeline.py +++ b/tests/test_all_pipeline.py @@ -110,7 +110,10 @@ def train(): # model initiaiton model = init_instance_by_config(task["model"]) + print(model) dataset = init_instance_by_config(task["dataset"]) + print(dataset) + print(R) # start exp with R.start(experiment_name="workflow"): @@ -119,6 +122,7 @@ def train(): # prediction recorder = R.get_recorder() + print(recorder) rid = recorder.id sr = SignalRecord(model, dataset, recorder) sr.generate() From ee7eb79277896ad3d1729dcf028cb0a027b9d538 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Thu, 4 Mar 2021 06:15:24 +0000 Subject: [PATCH 046/134] Fix unexpected mlruns folder error --- qlib/workflow/exp.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/qlib/workflow/exp.py b/qlib/workflow/exp.py index 7c98fd68f..15bb7604c 100644 --- a/qlib/workflow/exp.py +++ b/qlib/workflow/exp.py @@ -176,8 +176,6 @@ class MLflowExperiment(Experiment): self.client = mlflow.tracking.MlflowClient(tracking_uri=self._uri) def start(self, recorder_name=None): - # set the active experiment - mlflow.set_experiment(self.name) logger.info(f"Experiment {self.id} starts running ...") # set up recorder recorder = self.create_recorder(recorder_name) From 2bff6eb78120b8f3fc7aac12267d1e37c847ae0f Mon Sep 17 00:00:00 2001 From: Charles Young Date: Thu, 4 Mar 2021 22:08:11 +0800 Subject: [PATCH 047/134] Split classes in riskmodel.py & optimizer.py into seperate files. --- qlib/model/riskmodel_poet.py | 0 qlib/model/riskmodel_shrink.py | 0 qlib/model/riskmodel_structured.py | 0 qlib/portfolio/enhanced_indexing.py | 0 tests/test_enhanced_indexing.py | 212 ++++++++++++++++++++-------- 5 files changed, 150 insertions(+), 62 deletions(-) create mode 100644 qlib/model/riskmodel_poet.py create mode 100644 qlib/model/riskmodel_shrink.py create mode 100644 qlib/model/riskmodel_structured.py create mode 100644 qlib/portfolio/enhanced_indexing.py diff --git a/qlib/model/riskmodel_poet.py b/qlib/model/riskmodel_poet.py new file mode 100644 index 000000000..e69de29bb diff --git a/qlib/model/riskmodel_shrink.py b/qlib/model/riskmodel_shrink.py new file mode 100644 index 000000000..e69de29bb diff --git a/qlib/model/riskmodel_structured.py b/qlib/model/riskmodel_structured.py new file mode 100644 index 000000000..e69de29bb diff --git a/qlib/portfolio/enhanced_indexing.py b/qlib/portfolio/enhanced_indexing.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/test_enhanced_indexing.py b/tests/test_enhanced_indexing.py index f6e77cba4..f21d51984 100644 --- a/tests/test_enhanced_indexing.py +++ b/tests/test_enhanced_indexing.py @@ -2,32 +2,39 @@ # Licensed under the MIT License. import sys +import math import shutil import unittest -from pathlib import Path - import numpy as np import pandas as pd +from tqdm import tqdm +from pathlib import Path import qlib -from qlib.config import REG_CN, C -from qlib.utils import drop_nan_by_y_index -from qlib.contrib.model.gbdt import LGBModel -from qlib.contrib.data.handler import Alpha158 -from qlib.contrib.strategy.strategy import TopkDropoutStrategy -from qlib.contrib.evaluate import ( - backtest as normal_backtest, - risk_analysis, -) -from qlib.utils import exists_qlib_data, init_instance_by_config, flatten_dict +from qlib.config import C +from qlib.utils import init_instance_by_config, flatten_dict from qlib.workflow import R -from qlib.workflow.record_temp import SignalRecord, SigAnaRecord, PortAnaRecord -from qlib.tests.data import GetData +from qlib.config import REG_CN +from qlib.workflow.record_temp import SignalRecord, SigAnaRecord from qlib.tests import TestAutoData +from qlib.portfolio.optimizer import EnhancedIndexingOptimizer +from qlib.model.riskmodel import StructuredCovEstimator +from qlib.data.dataset.loader import QlibDataLoader +from qlib.data.dataset.handler import DataHandler +from qlib.data import D +from qlib.utils import exists_qlib_data, init_instance_by_config +market = "all" +trade_gap = 21 +label_config = "Ref($close, -{}) / Ref($close, -1) - 1".format(trade_gap) # reconstruct portfolio once a month -market = "csi300" -benchmark = "SH000300" +provider_uri = "~/.qlib_ei/qlib_data/cn_data" # target_dir +if not exists_qlib_data(provider_uri): + print(f"Qlib data is not found in {provider_uri}") + sys.path.append(str(Path.cwd().parent.joinpath("scripts"))) + from get_data import GetData + GetData().qlib_data(target_dir=provider_uri, region=REG_CN) +qlib.init(provider_uri=provider_uri, region=REG_CN) ################################### # train model @@ -36,8 +43,9 @@ data_handler_config = { "start_time": "2008-01-01", "end_time": "2020-08-01", "fit_start_time": "2008-01-01", - "fit_end_time": "2014-12-31", + "fit_end_time": "2014-11-30", "instruments": market, + "label": [label_config] } task = { @@ -53,7 +61,7 @@ task = { "lambda_l2": 580.9768, "max_depth": 8, "num_leaves": 210, - "num_threads": 20, + "num_threads": 32, }, }, "dataset": { @@ -66,37 +74,104 @@ task = { "kwargs": data_handler_config, }, "segments": { - "train": ("2008-01-01", "2014-12-31"), - "valid": ("2015-01-01", "2016-12-31"), - "test": ("2017-01-01", "2020-08-01"), + "train": ("2008-01-01", "2014-11-30"), + "valid": ("2015-01-01", "2016-11-30"), + "test": ("2017-01-01", "2018-01-01"), }, }, }, } -port_analysis_config = { - "strategy": { - "class": "TopkDropoutStrategy", - "module_path": "qlib.contrib.strategy.strategy", - "kwargs": { - "topk": 50, - "n_drop": 5, - }, - }, - "backtest": { - "verbose": False, - "limit_threshold": 0.095, - "account": 100000000, - "benchmark": benchmark, - "deal_price": "close", - "open_cost": 0.0005, - "close_cost": 0.0015, - "min_cost": 5, - }, -} + +class CSI300: + """Simulate CSI300 as the Benchmark for Enhanced Indexing to Track""" + + def __init__(self): + # provider_uri = '/nfs_data/qlib_data/ycz_daily/qlib' + # qlib.init(provider_uri=provider_uri, region=REG_CN, dataset_cache=None, expression_cache=None) + self.csi_weight = D.features(D.instruments('csi300'), ['$csi300_weight']) + + def __call__(self, pd_index, trade_date): + weights = np.zeros(len(pd_index)) + + for idx, instrument in enumerate(pd_index): + if (instrument, trade_date) in self.csi_weight.index: + weight = self.csi_weight.loc[(instrument, trade_date)].values[0] + if not math.isnan(weight): + weights[idx] = weight + + assert weights.sum() > 0, ' Fetch CSI Weights Error!' + weights = weights / weights.sum() + + return weights + + +class EnhancedIndexingStrategy: + """Enhanced Indexing Strategy""" + + def __init__(self): + self.benchmark = CSI300() + + provider_uri = "~/.qlib_ei/qlib_data/cn_data" + qlib.init(provider_uri=provider_uri, region=REG_CN) + + self.data_handler = DataHandler(market, "2015-01-01", "2019-01-01", QlibDataLoader(["$close"])) + self.label_handler = DataHandler(market, "2015-01-01", "2019-01-01", QlibDataLoader([label_config])) + self.cov_estimator = StructuredCovEstimator() + self.optimizer = EnhancedIndexingOptimizer(lamb=0.1, delta=0.4, bench_dev=0.03, max_iters=50000) + + def update(self, score_series, current, pred_date): + """ + Parameters + ----------- + score_series : pd.Series + stock_id , score. + current : Position() + current of account. + trade_exchange : Exchange() + exchange. + trade_date : pd.Timestamp + date. + """ + print(score_series) + score_series = score_series.dropna() + + # portfolio init weight + init_weight = current.reindex(score_series.index, fill_value=0).values.squeeze() + init_weight_sum = init_weight.sum() + if init_weight_sum > 0: + init_weight /= init_weight_sum + + # covariance estimation + selector = (self.data_handler.get_range_selector(pred_date, 252), score_series.index) + price = self.data_handler.fetch(selector, level=None, squeeze=True) + F, cov_b, var_u = self.cov_estimator.predict(price, return_decomposed_components=True) + + # optimize target portfolio + w_bench = self.benchmark(score_series.index, pred_date) + passed_init_weight = init_weight if init_weight_sum > 0 else None + # print(F) + # print(cov_b) + # print(var_u) + # print(passed_init_weight) + # print(w_bench) + target_weight = self.optimizer(score_series.values, F, cov_b, var_u, passed_init_weight, w_bench) + # print(target_weight) + target = pd.DataFrame(data=target_weight, index=score_series.index) + + active_weights = target_weight - w_bench + selector = (self.label_handler.get_range_selector(pred_date, 1), score_series.index) + label = self.label_handler.fetch(selector, level=None, squeeze=True) + alpha = 0 + for instrument, weight in zip(score_series.index, active_weights): + delta = label.loc[(pred_date, instrument)] + alpha += weight * (0 if math.isnan(delta) else delta) + + print(alpha) + + return alpha, target -# train def train(): """train model @@ -108,7 +183,7 @@ def train(): model performance """ - # model initiaiton + # model initiation model = init_instance_by_config(task["model"]) dataset = init_instance_by_config(task["dataset"]) @@ -133,29 +208,42 @@ def train(): return pred_score, {"ic": ic, "ric": ric}, rid -def backtest_analysis(pred, rid): - """backtest and analysis +def backtest_analysis(scores): + """backtest enhanced indexing Parameters ---------- - pred : pandas.DataFrame - predict scores - rid : str - the id of the recorder to be used in this function + scores: pandas.DataFrame + predict scores Returns ------- - analysis : pandas.DataFrame - the analysis result - + sharpe_ratio: floating-point + sharpe ratio of the enhanced indexing portfolio """ - recorder = R.get_recorder(experiment_name="workflow", recorder_id=rid) - # backtest - par = PortAnaRecord(recorder, port_analysis_config) - par.generate() - analysis_df = par.load(par.get_path("port_analysis.pkl")) - print(analysis_df) - return analysis_df + + # backtest and analysis + with R.start(experiment_name="backtest_analysis"): + strategy = EnhancedIndexingStrategy() + dates = scores.index.get_level_values(0).unique() + + alphas = [] + current = pd.DataFrame() + gap_between_next_trade = 0 + for date in tqdm(dates): + if gap_between_next_trade == 0: + score_series = scores.loc[date] + alpha, current = strategy.update(score_series, current, date) + alphas.append(alpha) + gap_between_next_trade = trade_gap + else: + gap_between_next_trade -= 1 + + alphas = np.array(alphas) + sharpe_ratio = alphas.mean() / np.std(alphas) + print('Sharpe:', sharpe_ratio) + + return sharpe_ratio class TestAllFlow(TestAutoData): @@ -174,10 +262,10 @@ class TestAllFlow(TestAutoData): self.assertGreaterEqual(ic_ric["ric"].all(), 0, "train failed") def test_1_backtest(self): - analyze_df = backtest_analysis(TestAllFlow.PRED_SCORE, TestAllFlow.RID) + sharpe_ratio = backtest_analysis(TestAllFlow.PRED_SCORE) self.assertGreaterEqual( - analyze_df.loc(axis=0)["excess_return_with_cost", "annualized_return"].values[0], - 0.10, + sharpe_ratio, + 0.90, "backtest failed", ) From 83c6e747835656d0c5d5f90fb3c903a239689158 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Thu, 4 Mar 2021 22:30:38 +0800 Subject: [PATCH 048/134] Reindex files. --- qlib/model/riskmodel.py | 611 ------------------ .../__init__.py} | 0 qlib/model/riskmodel/base.py | 141 ++++ qlib/model/riskmodel/poet.py | 84 +++ qlib/model/riskmodel/shrink.py | 262 ++++++++ qlib/model/riskmodel/structured.py | 152 +++++ qlib/portfolio/enhanced_indexing.py | 0 .../optimizer/__init__.py} | 0 .../optimizer/base.py} | 0 qlib/portfolio/optimizer/enhanced_indexing.py | 140 ++++ qlib/portfolio/{ => optimizer}/optimizer.py | 159 +---- tests/test_enhanced_indexing.py | 282 -------- 12 files changed, 793 insertions(+), 1038 deletions(-) delete mode 100644 qlib/model/riskmodel.py rename qlib/model/{riskmodel_poet.py => riskmodel/__init__.py} (100%) create mode 100644 qlib/model/riskmodel/base.py create mode 100644 qlib/model/riskmodel/poet.py create mode 100644 qlib/model/riskmodel/shrink.py create mode 100644 qlib/model/riskmodel/structured.py delete mode 100644 qlib/portfolio/enhanced_indexing.py rename qlib/{model/riskmodel_shrink.py => portfolio/optimizer/__init__.py} (100%) rename qlib/{model/riskmodel_structured.py => portfolio/optimizer/base.py} (100%) create mode 100644 qlib/portfolio/optimizer/enhanced_indexing.py rename qlib/portfolio/{ => optimizer}/optimizer.py (62%) delete mode 100644 tests/test_enhanced_indexing.py diff --git a/qlib/model/riskmodel.py b/qlib/model/riskmodel.py deleted file mode 100644 index f19c60fc9..000000000 --- a/qlib/model/riskmodel.py +++ /dev/null @@ -1,611 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. - -import numpy as np -import pandas as pd -from typing import Union -from sklearn.decomposition import PCA, FactorAnalysis - -from qlib.model.base import BaseModel - - -class RiskModel(BaseModel): - """Risk Model - - A risk model is used to estimate the covariance matrix of stock returns. - """ - - MASK_NAN = "mask" - FILL_NAN = "fill" - IGNORE_NAN = "ignore" - - def __init__(self, nan_option: str = "ignore", assume_centered: bool = False, scale_return: bool = True): - """ - Args: - nan_option (str): nan handling option (`ignore`/`mask`/`fill`). - assume_centered (bool): whether the data is assumed to be centered. - scale_return (bool): whether scale returns as percentage. - """ - # nan - assert nan_option in [ - self.MASK_NAN, - self.FILL_NAN, - self.IGNORE_NAN, - ], f"`nan_option={nan_option}` is not supported" - self.nan_option = nan_option - - self.assume_centered = assume_centered - self.scale_return = scale_return - - def predict( - self, X: Union[pd.Series, pd.DataFrame, np.ndarray], return_corr: bool = False, is_price: bool = True - ) -> Union[pd.DataFrame, np.ndarray]: - """ - Args: - X (pd.Series, pd.DataFrame or np.ndarray): data from which to estimate the covariance, - with variables as columns and observations as rows. - return_corr (bool): whether return the correlation matrix. - is_price (bool): whether `X` contains price (if not assume stock returns). - - Returns: - pd.DataFrame or np.ndarray: estimated covariance (or correlation). - """ - # transform input into 2D array - if not isinstance(X, (pd.Series, pd.DataFrame)): - columns = None - else: - if isinstance(X.index, pd.MultiIndex): - if isinstance(X, pd.DataFrame): - X = X.iloc[:, 0].unstack(level="instrument") # always use the first column - else: - X = X.unstack(level="instrument") - else: - # X is 2D DataFrame - pass - columns = X.columns # will be used to restore dataframe - X = X.values - - # calculate pct_change - if is_price: - X = X[1:] / X[:-1] - 1 # NOTE: resulting `n - 1` rows - - # scale return - if self.scale_return: - X *= 100 - - # handle nan and centered - X = self._preprocess(X) - - # estimate covariance - S = self._predict(X) - - # return correlation if needed - if return_corr: - vola = np.sqrt(np.diag(S)) - corr = S / np.outer(vola, vola) - if columns is None: - return corr - return pd.DataFrame(corr, index=columns, columns=columns) - - # return covariance - if columns is None: - return S - return pd.DataFrame(S, index=columns, columns=columns) - - def _predict(self, X: np.ndarray) -> np.ndarray: - """covariance estimation implementation - - This method should be overridden by child classes. - - By default, this method implements the empirical covariance estimation. - - Args: - X (np.ndarray): data matrix containing multiple variables (columns) and observations (rows). - - Returns: - np.ndarray: covariance matrix. - """ - xTx = np.asarray(X.T.dot(X)) - N = len(X) - if isinstance(X, np.ma.MaskedArray): - M = 1 - X.mask - N = M.T.dot(M) # each pair has distinct number of samples - return xTx / N - - def _preprocess(self, X: np.ndarray) -> Union[np.ndarray, np.ma.MaskedArray]: - """handle nan and centerize data - - Note: - if `nan_option='mask'` then the returned array will be `np.ma.MaskedArray`. - """ - # handle nan - if self.nan_option == self.FILL_NAN: - X = np.nan_to_num(X) - elif self.nan_option == self.MASK_NAN: - X = np.ma.masked_invalid(X) - # centralize - if not self.assume_centered: - X = X - np.nanmean(X, axis=0) - return X - - -class ShrinkCovEstimator(RiskModel): - """Shrinkage Covariance Estimator - - This estimator will shrink the sample covariance matrix towards - an identify matrix: - S_hat = (1 - alpha) * S + alpha * F - where `alpha` is the shrink parameter and `F` is the shrinking target. - - The following shrinking parameters (`alpha`) are supported: - - `lw` [1][2][3]: use Ledoit-Wolf shrinking parameter. - - `oas` [4]: use Oracle Approximating Shrinkage shrinking parameter. - - float: directly specify the shrink parameter, should be between [0, 1]. - - The following shrinking targets (`F`) are supported: - - `const_var` [1][4][5]: assume stocks have the same constant variance and zero correlation. - - `const_corr` [2][6]: assume stocks have different variance but equal correlation. - - `single_factor` [3][7]: assume single factor model as the shrinking target. - - np.ndarray: provide the shrinking targets directly. - - Note: - - The optimal shrinking parameter depends on the selection of the shrinking target. - Currently, `oas` is not supported for `const_corr` and `single_factor`. - - Remember to set `nan_option` to `fill` or `mask` if your data has missing values. - - References: - [1] Ledoit, O., & Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. - Journal of Multivariate Analysis, 88(2), 365–411. https://doi.org/10.1016/S0047-259X(03)00096-4 - [2] Ledoit, O., & Wolf, M. (2004). Honey, I shrunk the sample covariance matrix. - Journal of Portfolio Management, 30(4), 1–22. https://doi.org/10.3905/jpm.2004.110 - [3] Ledoit, O., & Wolf, M. (2003). Improved estimation of the covariance matrix of stock returns - with an application to portfolio selection. - Journal of Empirical Finance, 10(5), 603–621. https://doi.org/10.1016/S0927-5398(03)00007-0 - [4] Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O. (2010). Shrinkage algorithms for MMSE covariance - estimation. IEEE Transactions on Signal Processing, 58(10), 5016–5029. - https://doi.org/10.1109/TSP.2010.2053029 - [5] https://www.econ.uzh.ch/dam/jcr:ffffffff-935a-b0d6-0000-00007f64e5b9/cov1para.m.zip - [6] https://www.econ.uzh.ch/dam/jcr:ffffffff-935a-b0d6-ffff-ffffde5e2d4e/covCor.m.zip - [7] https://www.econ.uzh.ch/dam/jcr:ffffffff-935a-b0d6-0000-0000648dfc98/covMarket.m.zip - """ - - SHR_LW = "lw" - SHR_OAS = "oas" - - TGT_CONST_VAR = "const_var" - TGT_CONST_CORR = "const_corr" - TGT_SINGLE_FACTOR = "single_factor" - - def __init__(self, alpha: Union[str, float] = 0.0, target: Union[str, np.ndarray] = "const_var", **kwargs): - """ - Args: - alpha (str or float): shrinking parameter or estimator (`lw`/`oas`) - target (str or np.ndarray): shrinking target (`const_var`/`const_corr`/`single_factor`) - kwargs: see `RiskModel` for more information - """ - super().__init__(**kwargs) - - # alpha - if isinstance(alpha, str): - assert alpha in [self.SHR_LW, self.SHR_OAS], f"shrinking method `{alpha}` is not supported" - elif isinstance(alpha, (float, np.floating)): - assert 0 <= alpha <= 1, "alpha should be between [0, 1]" - else: - raise TypeError("invalid argument type for `alpha`") - self.alpha = alpha - - # target - if isinstance(target, str): - assert target in [ - self.TGT_CONST_VAR, - self.TGT_CONST_CORR, - self.TGT_SINGLE_FACTOR, - ], f"shrinking target `{target} is not supported" - elif isinstance(target, np.ndarray): - pass - else: - raise TypeError("invalid argument type for `target`") - if alpha == self.SHR_OAS and target != self.TGT_CONST_VAR: - raise NotImplementedError("currently `oas` can only support `const_var` as target") - self.target = target - - def _predict(self, X: np.ndarray) -> np.ndarray: - # sample covariance - S = super()._predict(X) - - # shrinking target - F = self._get_shrink_target(X, S) - - # get shrinking parameter - alpha = self._get_shrink_param(X, S, F) - - # shrink covariance - if alpha > 0: - S *= 1 - alpha - F *= alpha - S += F - - return S - - def _get_shrink_target(self, X: np.ndarray, S: np.ndarray) -> np.ndarray: - """get shrinking target `F`""" - if self.target == self.TGT_CONST_VAR: - return self._get_shrink_target_const_var(X, S) - if self.target == self.TGT_CONST_CORR: - return self._get_shrink_target_const_corr(X, S) - if self.target == self.TGT_SINGLE_FACTOR: - return self._get_shrink_target_single_factor(X, S) - return self.target - - def _get_shrink_target_const_var(self, X: np.ndarray, S: np.ndarray) -> np.ndarray: - """get shrinking target with constant variance - - This target assumes zero pair-wise correlation and constant variance. - The constant variance is estimated by averaging all sample's variances. - """ - n = len(S) - F = np.eye(n) - np.fill_diagonal(F, np.mean(np.diag(S))) - return F - - def _get_shrink_target_const_corr(self, X: np.ndarray, S: np.ndarray) -> np.ndarray: - """get shrinking target with constant correlation - - This target assumes constant pair-wise correlation but keep the sample variance. - The constant correlation is estimated by averaging all pairwise correlations. - """ - n = len(S) - var = np.diag(S) - sqrt_var = np.sqrt(var) - covar = np.outer(sqrt_var, sqrt_var) - r_bar = (np.sum(S / covar) - n) / (n * (n - 1)) - F = r_bar * covar - np.fill_diagonal(F, var) - return F - - def _get_shrink_target_single_factor(self, X: np.ndarray, S: np.ndarray) -> np.ndarray: - """get shrinking target with single factor model""" - X_mkt = np.nanmean(X, axis=1) - cov_mkt = np.asarray(X.T.dot(X_mkt) / len(X)) - var_mkt = np.asarray(X_mkt.dot(X_mkt) / len(X)) - F = np.outer(cov_mkt, cov_mkt) / var_mkt - np.fill_diagonal(F, np.diag(S)) - return F - - def _get_shrink_param(self, X: np.ndarray, S: np.ndarray, F: np.ndarray) -> float: - """get shrinking parameter `alpha` - - Note: - The Ledoit-Wolf shrinking parameter estimator consists of three different methods. - """ - if self.alpha == self.SHR_OAS: - return self._get_shrink_param_oas(X, S, F) - elif self.alpha == self.SHR_LW: - if self.target == self.TGT_CONST_VAR: - return self._get_shrink_param_lw_const_var(X, S, F) - if self.target == self.TGT_CONST_CORR: - return self._get_shrink_param_lw_const_corr(X, S, F) - if self.target == self.TGT_SINGLE_FACTOR: - return self._get_shrink_param_lw_single_factor(X, S, F) - return self.alpha - - def _get_shrink_param_oas(self, X: np.ndarray, S: np.ndarray, F: np.ndarray) -> float: - """Oracle Approximating Shrinkage Estimator - - This method uses the following formula to estimate the `alpha` - parameter for the shrink covariance estimator: - A = (1 - 2 / p) * trace(S^2) + trace^2(S) - B = (n + 1 - 2 / p) * (trace(S^2) - trace^2(S) / p) - alpha = A / B - where `n`, `p` are the dim of observations and variables respectively. - """ - trS2 = np.sum(S ** 2) - tr2S = np.trace(S) ** 2 - - n, p = X.shape - - A = (1 - 2 / p) * (trS2 + tr2S) - B = (n + 1 - 2 / p) * (trS2 + tr2S / p) - alpha = A / B - - return alpha - - def _get_shrink_param_lw_const_var(self, X: np.ndarray, S: np.ndarray, F: np.ndarray) -> float: - """Ledoit-Wolf Shrinkage Estimator (Constant Variance) - - This method shrinks the covariance matrix towards the constand variance target. - """ - t, n = X.shape - - y = X ** 2 - phi = np.sum(y.T.dot(y) / t - S ** 2) - - gamma = np.linalg.norm(S - F, "fro") ** 2 - - kappa = phi / gamma - alpha = max(0, min(1, kappa / t)) - - return alpha - - def _get_shrink_param_lw_const_corr(self, X: np.ndarray, S: np.ndarray, F: np.ndarray) -> float: - """Ledoit-Wolf Shrinkage Estimator (Constant Correlation) - - This method shrinks the covariance matrix towards the constand correlation target. - """ - t, n = X.shape - - var = np.diag(S) - sqrt_var = np.sqrt(var) - r_bar = (np.sum(S / np.outer(sqrt_var, sqrt_var)) - n) / (n * (n - 1)) - - y = X ** 2 - phi_mat = y.T.dot(y) / t - S ** 2 - phi = np.sum(phi_mat) - - theta_mat = (X ** 3).T.dot(X) / t - var[:, None] * S - np.fill_diagonal(theta_mat, 0) - rho = np.sum(np.diag(phi_mat)) + r_bar * np.sum(np.outer(1 / sqrt_var, sqrt_var) * theta_mat) - - gamma = np.linalg.norm(S - F, "fro") ** 2 - - kappa = (phi - rho) / gamma - alpha = max(0, min(1, kappa / t)) - - return alpha - - def _get_shrink_param_lw_single_factor(self, X: np.ndarray, S: np.ndarray, F: np.ndarray) -> float: - """Ledoit-Wolf Shrinkage Estimator (Single Factor Model) - - This method shrinks the covariance matrix towards the single factor model target. - """ - t, n = X.shape - - X_mkt = np.nanmean(X, axis=1) - cov_mkt = np.asarray(X.T.dot(X_mkt) / len(X)) - var_mkt = np.asarray(X_mkt.dot(X_mkt) / len(X)) - - y = X ** 2 - phi = np.sum(y.T.dot(y)) / t - np.sum(S ** 2) - - rdiag = np.sum(y ** 2) / t - np.sum(np.diag(S) ** 2) - z = X * X_mkt[:, None] - v1 = y.T.dot(z) / t - cov_mkt[:, None] * S - roff1 = np.sum(v1 * cov_mkt[:, None].T) / var_mkt - np.sum(np.diag(v1) * cov_mkt) / var_mkt - v3 = z.T.dot(z) / t - var_mkt * S - roff3 = ( - np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt ** 2 - np.sum(np.diag(v3) * cov_mkt ** 2) / var_mkt ** 2 - ) - roff = 2 * roff1 - roff3 - rho = rdiag + roff - - gamma = np.linalg.norm(S - F, "fro") ** 2 - - kappa = (phi - rho) / gamma - alpha = max(0, min(1, kappa / t)) - - return alpha - - -class POETCovEstimator(RiskModel): - """Principal Orthogonal Complement Thresholding Estimator (POET) - - Reference: - [1] Fan, J., Liao, Y., & Mincheva, M. (2013). Large covariance estimation by thresholding principal orthogonal complements. - Journal of the Royal Statistical Society. Series B: Statistical Methodology, 75(4), 603–680. https://doi.org/10.1111/rssb.12016 - [2] http://econweb.rutgers.edu/yl1114/papers/poet/POET.m - """ - - THRESH_SOFT = "soft" - THRESH_HARD = "hard" - THRESH_SCAD = "scad" - - def __init__(self, num_factors: int = 0, thresh: float = 1.0, thresh_method: str = "soft", **kwargs): - """ - Args: - num_factors (int): number of factors (if set to zero, no factor model will be used). - thresh (float): the positive constant for thresholding. - thresh_method (str): thresholding method, which can be - - 'soft': soft thresholding. - - 'hard': hard thresholding. - - 'scad': scad thresholding. - kwargs: see `RiskModel` for more information. - """ - super().__init__(**kwargs) - - assert num_factors >= 0, "`num_factors` requires a positive integer" - self.num_factors = num_factors - - assert thresh >= 0, "`thresh` requires a positive float number" - self.thresh = thresh - - assert thresh_method in [ - self.THRESH_HARD, - self.THRESH_SOFT, - self.THRESH_SCAD, - ], "`thresh_method` should be `soft`/`hard`/`scad`" - self.thresh_method = thresh_method - - def _predict(self, X: np.ndarray) -> np.ndarray: - - Y = X.T # NOTE: to match POET's implementation - p, n = Y.shape - - if self.num_factors > 0: - Dd, V = np.linalg.eig(Y.T.dot(Y)) - V = V[:, np.argsort(Dd)] - F = V[:, -self.num_factors :][:, ::-1] * np.sqrt(n) - LamPCA = Y.dot(F) / n - uhat = np.asarray(Y - LamPCA.dot(F.T)) - Lowrank = np.asarray(LamPCA.dot(LamPCA.T)) - rate = 1 / np.sqrt(p) + np.sqrt(np.log(p) / n) - else: - uhat = np.asarray(Y) - rate = np.sqrt(np.log(p) / n) - Lowrank = 0 - - lamb = rate * self.thresh - SuPCA = uhat.dot(uhat.T) / n - SuDiag = np.diag(np.diag(SuPCA)) - R = np.linalg.inv(SuDiag ** 0.5).dot(SuPCA).dot(np.linalg.inv(SuDiag ** 0.5)) - - if self.thresh_method == self.THRESH_HARD: - M = R * (np.abs(R) > lamb) - elif self.thresh_method == self.THRESH_SOFT: - res = np.abs(R) - lamb - res = (res + np.abs(res)) / 2 - M = np.sign(R) * res - else: - M1 = (np.abs(R) < 2 * lamb) * np.sign(R) * (np.abs(R) - lamb) * (np.abs(R) > lamb) - M2 = (np.abs(R) < 3.7 * lamb) * (np.abs(R) >= 2 * lamb) * (2.7 * R - 3.7 * np.sign(R) * lamb) / 1.7 - M3 = (np.abs(R) >= 3.7 * lamb) * R - M = M1 + M2 + M3 - - Rthresh = M - np.diag(np.diag(M)) + np.eye(p) - SigmaU = (SuDiag ** 0.5).dot(Rthresh).dot(SuDiag ** 0.5) - SigmaY = SigmaU + Lowrank - - return SigmaY - - -class StructuredCovEstimator(RiskModel): - """Structured Covariance Estimator - - This estimator assumes observations can be predicted by multiple factors - X = FB + U - where `F` can be specified by explicit risk factors or latent factors. - - Therefore the structured covariance can be estimated by - cov(X) = F cov(B) F.T + cov(U) - - We use latent factor models to estimate the structured covariance. - Specifically, the following latent factor models are supported: - - `pca`: Principal Component Analysis - - `fa`: Factor Analysis - - Reference: [1] Fan, J., Liao, Y., & Liu, H. (2016). An overview of the estimation of large covariance and - precision matrices. Econometrics Journal, 19(1), C1–C32. https://doi.org/10.1111/ectj.12061 - """ - - FACTOR_MODEL_PCA = "pca" - FACTOR_MODEL_FA = "fa" - - def __init__( - self, - factor_model: str = "pca", - num_factors: int = 10, - nan_option: str = "ignore", - assume_centered: bool = False, - scale_return: bool = True, - ): - """ - Args: - factor_model (str): the latent factor models used to estimate the structured covariance (`pca`/`fa`). - num_factors (int): number of components to keep. - nan_option (str): nan handling option (`ignore`/`fill`). - assume_centered (bool): whether the data is assumed to be centered. - scale_return (bool): whether scale returns as percentage. - """ - super().__init__(nan_option, assume_centered, scale_return) - - assert factor_model in [ - self.FACTOR_MODEL_PCA, - self.FACTOR_MODEL_FA, - ], "factor_model={} is not supported".format(factor_model) - self.solver = PCA if factor_model == self.FACTOR_MODEL_PCA else FactorAnalysis - - self.num_factors = num_factors - - def predict( - self, - X: Union[pd.Series, pd.DataFrame, np.ndarray], - return_corr: bool = False, - is_price: bool = True, - return_decomposed_components=False, - ) -> Union[pd.DataFrame, np.ndarray, tuple]: - """ - Args: - X (pd.Series, pd.DataFrame or np.ndarray): data from which to estimate the covariance, - with variables as columns and observations as rows. - return_corr (bool): whether return the correlation matrix. - is_price (bool): whether `X` contains price (if not assume stock returns). - return_decomposed_components (bool): whether return decomposed components of the covariance matrix. - - Returns: - tuple or pd.DataFrame or np.ndarray: decomposed covariance matrix or estimated covariance or correlation. - """ - assert ( - not return_corr or not return_decomposed_components - ), "Can only return either correlation matrix or decomposed components." - - # transform input into 2D array - if not isinstance(X, (pd.Series, pd.DataFrame)): - columns = None - else: - if isinstance(X.index, pd.MultiIndex): - if isinstance(X, pd.DataFrame): - X = X.iloc[:, 0].unstack(level="instrument") # always use the first column - else: - X = X.unstack(level="instrument") - else: - # X is 2D DataFrame - pass - columns = X.columns # will be used to restore dataframe - X = X.values - - # calculate pct_change - if is_price: - X = X[1:] / X[:-1] - 1 # NOTE: resulting `n - 1` rows - - # scale return - if self.scale_return: - X *= 100 - - # handle nan and centered - X = self._preprocess(X) - - if return_decomposed_components: - F, cov_b, var_u = self._predict(X, return_structured=True) - return F, cov_b, var_u - else: - # estimate covariance - S = self._predict(X) - - # return correlation if needed - if return_corr: - vola = np.sqrt(np.diag(S)) - corr = S / np.outer(vola, vola) - if columns is None: - return corr - return pd.DataFrame(corr, index=columns, columns=columns) - - # return covariance - if columns is None: - return S - return pd.DataFrame(S, index=columns, columns=columns) - - def _predict(self, X: np.ndarray, return_structured=False) -> Union[np.ndarray, tuple]: - """ - covariance estimation implementation - - Args: - X (np.ndarray): data matrix containing multiple variables (columns) and observations (rows). - return_structured (bool): whether return decomposed components of the covariance matrix. - - Returns: - tuple or np.ndarray: decomposed covariance matrix or covariance matrix. - """ - - model = self.solver(self.num_factors, random_state=0).fit(X) - - F = model.components_.T # num_features x num_factors - B = model.transform(X) # num_samples x num_factors - U = X - B @ F.T - cov_b = np.cov(B.T) # num_factors x num_factors - var_u = np.var(U, axis=0) # diagonal - - if return_structured: - return F, cov_b, var_u - - cov_x = F @ cov_b @ F.T + np.diag(var_u) - - return cov_x diff --git a/qlib/model/riskmodel_poet.py b/qlib/model/riskmodel/__init__.py similarity index 100% rename from qlib/model/riskmodel_poet.py rename to qlib/model/riskmodel/__init__.py diff --git a/qlib/model/riskmodel/base.py b/qlib/model/riskmodel/base.py new file mode 100644 index 000000000..d5b009ccc --- /dev/null +++ b/qlib/model/riskmodel/base.py @@ -0,0 +1,141 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +import numpy as np +import pandas as pd +from typing import Union + +from qlib.model.base import BaseModel + +from qlib.model.riskmodel_poet import POETCovEstimator +from qlib.model.riskmodel_shrink import ShrinkCovEstimator +from qlib.model.riskmodel_structured import StructuredCovEstimator + + +class RiskModel(BaseModel): + """Risk Model + + A risk model is used to estimate the covariance matrix of stock returns. + """ + + MASK_NAN = "mask" + FILL_NAN = "fill" + IGNORE_NAN = "ignore" + + def __init__(self, nan_option: str = "ignore", assume_centered: bool = False, scale_return: bool = True): + """ + Args: + nan_option (str): nan handling option (`ignore`/`mask`/`fill`). + assume_centered (bool): whether the data is assumed to be centered. + scale_return (bool): whether scale returns as percentage. + """ + # nan + assert nan_option in [ + self.MASK_NAN, + self.FILL_NAN, + self.IGNORE_NAN, + ], f"`nan_option={nan_option}` is not supported" + self.nan_option = nan_option + + self.assume_centered = assume_centered + self.scale_return = scale_return + + def predict( + self, X: Union[pd.Series, pd.DataFrame, np.ndarray], return_corr: bool = False, is_price: bool = True + ) -> Union[pd.DataFrame, np.ndarray]: + """ + Args: + X (pd.Series, pd.DataFrame or np.ndarray): data from which to estimate the covariance, + with variables as columns and observations as rows. + return_corr (bool): whether return the correlation matrix. + is_price (bool): whether `X` contains price (if not assume stock returns). + + Returns: + pd.DataFrame or np.ndarray: estimated covariance (or correlation). + """ + # transform input into 2D array + if not isinstance(X, (pd.Series, pd.DataFrame)): + columns = None + else: + if isinstance(X.index, pd.MultiIndex): + if isinstance(X, pd.DataFrame): + X = X.iloc[:, 0].unstack(level="instrument") # always use the first column + else: + X = X.unstack(level="instrument") + else: + # X is 2D DataFrame + pass + columns = X.columns # will be used to restore dataframe + X = X.values + + # calculate pct_change + if is_price: + X = X[1:] / X[:-1] - 1 # NOTE: resulting `n - 1` rows + + # scale return + if self.scale_return: + X *= 100 + + # handle nan and centered + X = self._preprocess(X) + + # estimate covariance + S = self._predict(X) + + # return correlation if needed + if return_corr: + vola = np.sqrt(np.diag(S)) + corr = S / np.outer(vola, vola) + if columns is None: + return corr + return pd.DataFrame(corr, index=columns, columns=columns) + + # return covariance + if columns is None: + return S + return pd.DataFrame(S, index=columns, columns=columns) + + def _predict(self, X: np.ndarray) -> np.ndarray: + """covariance estimation implementation + + This method should be overridden by child classes. + + By default, this method implements the empirical covariance estimation. + + Args: + X (np.ndarray): data matrix containing multiple variables (columns) and observations (rows). + + Returns: + np.ndarray: covariance matrix. + """ + xTx = np.asarray(X.T.dot(X)) + N = len(X) + if isinstance(X, np.ma.MaskedArray): + M = 1 - X.mask + N = M.T.dot(M) # each pair has distinct number of samples + return xTx / N + + def _preprocess(self, X: np.ndarray) -> Union[np.ndarray, np.ma.MaskedArray]: + """handle nan and centerize data + + Note: + if `nan_option='mask'` then the returned array will be `np.ma.MaskedArray`. + """ + # handle nan + if self.nan_option == self.FILL_NAN: + X = np.nan_to_num(X) + elif self.nan_option == self.MASK_NAN: + X = np.ma.masked_invalid(X) + # centralize + if not self.assume_centered: + X = X - np.nanmean(X, axis=0) + return X + + + + + + + + + diff --git a/qlib/model/riskmodel/poet.py b/qlib/model/riskmodel/poet.py new file mode 100644 index 000000000..8dbe89036 --- /dev/null +++ b/qlib/model/riskmodel/poet.py @@ -0,0 +1,84 @@ +import numpy as np + +from qlib.model.riskmodel import RiskModel + + +class POETCovEstimator(RiskModel): + """Principal Orthogonal Complement Thresholding Estimator (POET) + + Reference: + [1] Fan, J., Liao, Y., & Mincheva, M. (2013). Large covariance estimation by thresholding principal orthogonal complements. + Journal of the Royal Statistical Society. Series B: Statistical Methodology, 75(4), 603–680. https://doi.org/10.1111/rssb.12016 + [2] http://econweb.rutgers.edu/yl1114/papers/poet/POET.m + """ + + THRESH_SOFT = "soft" + THRESH_HARD = "hard" + THRESH_SCAD = "scad" + + def __init__(self, num_factors: int = 0, thresh: float = 1.0, thresh_method: str = "soft", **kwargs): + """ + Args: + num_factors (int): number of factors (if set to zero, no factor model will be used). + thresh (float): the positive constant for thresholding. + thresh_method (str): thresholding method, which can be + - 'soft': soft thresholding. + - 'hard': hard thresholding. + - 'scad': scad thresholding. + kwargs: see `RiskModel` for more information. + """ + super().__init__(**kwargs) + + assert num_factors >= 0, "`num_factors` requires a positive integer" + self.num_factors = num_factors + + assert thresh >= 0, "`thresh` requires a positive float number" + self.thresh = thresh + + assert thresh_method in [ + self.THRESH_HARD, + self.THRESH_SOFT, + self.THRESH_SCAD, + ], "`thresh_method` should be `soft`/`hard`/`scad`" + self.thresh_method = thresh_method + + def _predict(self, X: np.ndarray) -> np.ndarray: + + Y = X.T # NOTE: to match POET's implementation + p, n = Y.shape + + if self.num_factors > 0: + Dd, V = np.linalg.eig(Y.T.dot(Y)) + V = V[:, np.argsort(Dd)] + F = V[:, -self.num_factors:][:, ::-1] * np.sqrt(n) + LamPCA = Y.dot(F) / n + uhat = np.asarray(Y - LamPCA.dot(F.T)) + Lowrank = np.asarray(LamPCA.dot(LamPCA.T)) + rate = 1 / np.sqrt(p) + np.sqrt(np.log(p) / n) + else: + uhat = np.asarray(Y) + rate = np.sqrt(np.log(p) / n) + Lowrank = 0 + + lamb = rate * self.thresh + SuPCA = uhat.dot(uhat.T) / n + SuDiag = np.diag(np.diag(SuPCA)) + R = np.linalg.inv(SuDiag ** 0.5).dot(SuPCA).dot(np.linalg.inv(SuDiag ** 0.5)) + + if self.thresh_method == self.THRESH_HARD: + M = R * (np.abs(R) > lamb) + elif self.thresh_method == self.THRESH_SOFT: + res = np.abs(R) - lamb + res = (res + np.abs(res)) / 2 + M = np.sign(R) * res + else: + M1 = (np.abs(R) < 2 * lamb) * np.sign(R) * (np.abs(R) - lamb) * (np.abs(R) > lamb) + M2 = (np.abs(R) < 3.7 * lamb) * (np.abs(R) >= 2 * lamb) * (2.7 * R - 3.7 * np.sign(R) * lamb) / 1.7 + M3 = (np.abs(R) >= 3.7 * lamb) * R + M = M1 + M2 + M3 + + Rthresh = M - np.diag(np.diag(M)) + np.eye(p) + SigmaU = (SuDiag ** 0.5).dot(Rthresh).dot(SuDiag ** 0.5) + SigmaY = SigmaU + Lowrank + + return SigmaY diff --git a/qlib/model/riskmodel/shrink.py b/qlib/model/riskmodel/shrink.py new file mode 100644 index 000000000..1298891fb --- /dev/null +++ b/qlib/model/riskmodel/shrink.py @@ -0,0 +1,262 @@ +import numpy as np +from typing import Union + +from qlib.model.riskmodel import RiskModel + + +class ShrinkCovEstimator(RiskModel): + """Shrinkage Covariance Estimator + + This estimator will shrink the sample covariance matrix towards + an identify matrix: + S_hat = (1 - alpha) * S + alpha * F + where `alpha` is the shrink parameter and `F` is the shrinking target. + + The following shrinking parameters (`alpha`) are supported: + - `lw` [1][2][3]: use Ledoit-Wolf shrinking parameter. + - `oas` [4]: use Oracle Approximating Shrinkage shrinking parameter. + - float: directly specify the shrink parameter, should be between [0, 1]. + + The following shrinking targets (`F`) are supported: + - `const_var` [1][4][5]: assume stocks have the same constant variance and zero correlation. + - `const_corr` [2][6]: assume stocks have different variance but equal correlation. + - `single_factor` [3][7]: assume single factor model as the shrinking target. + - np.ndarray: provide the shrinking targets directly. + + Note: + - The optimal shrinking parameter depends on the selection of the shrinking target. + Currently, `oas` is not supported for `const_corr` and `single_factor`. + - Remember to set `nan_option` to `fill` or `mask` if your data has missing values. + + References: + [1] Ledoit, O., & Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. + Journal of Multivariate Analysis, 88(2), 365–411. https://doi.org/10.1016/S0047-259X(03)00096-4 + [2] Ledoit, O., & Wolf, M. (2004). Honey, I shrunk the sample covariance matrix. + Journal of Portfolio Management, 30(4), 1–22. https://doi.org/10.3905/jpm.2004.110 + [3] Ledoit, O., & Wolf, M. (2003). Improved estimation of the covariance matrix of stock returns + with an application to portfolio selection. + Journal of Empirical Finance, 10(5), 603–621. https://doi.org/10.1016/S0927-5398(03)00007-0 + [4] Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O. (2010). Shrinkage algorithms for MMSE covariance + estimation. IEEE Transactions on Signal Processing, 58(10), 5016–5029. + https://doi.org/10.1109/TSP.2010.2053029 + [5] https://www.econ.uzh.ch/dam/jcr:ffffffff-935a-b0d6-0000-00007f64e5b9/cov1para.m.zip + [6] https://www.econ.uzh.ch/dam/jcr:ffffffff-935a-b0d6-ffff-ffffde5e2d4e/covCor.m.zip + [7] https://www.econ.uzh.ch/dam/jcr:ffffffff-935a-b0d6-0000-0000648dfc98/covMarket.m.zip + """ + + SHR_LW = "lw" + SHR_OAS = "oas" + + TGT_CONST_VAR = "const_var" + TGT_CONST_CORR = "const_corr" + TGT_SINGLE_FACTOR = "single_factor" + + def __init__(self, alpha: Union[str, float] = 0.0, target: Union[str, np.ndarray] = "const_var", **kwargs): + """ + Args: + alpha (str or float): shrinking parameter or estimator (`lw`/`oas`) + target (str or np.ndarray): shrinking target (`const_var`/`const_corr`/`single_factor`) + kwargs: see `RiskModel` for more information + """ + super().__init__(**kwargs) + + # alpha + if isinstance(alpha, str): + assert alpha in [self.SHR_LW, self.SHR_OAS], f"shrinking method `{alpha}` is not supported" + elif isinstance(alpha, (float, np.floating)): + assert 0 <= alpha <= 1, "alpha should be between [0, 1]" + else: + raise TypeError("invalid argument type for `alpha`") + self.alpha = alpha + + # target + if isinstance(target, str): + assert target in [ + self.TGT_CONST_VAR, + self.TGT_CONST_CORR, + self.TGT_SINGLE_FACTOR, + ], f"shrinking target `{target} is not supported" + elif isinstance(target, np.ndarray): + pass + else: + raise TypeError("invalid argument type for `target`") + if alpha == self.SHR_OAS and target != self.TGT_CONST_VAR: + raise NotImplementedError("currently `oas` can only support `const_var` as target") + self.target = target + + def _predict(self, X: np.ndarray) -> np.ndarray: + # sample covariance + S = super()._predict(X) + + # shrinking target + F = self._get_shrink_target(X, S) + + # get shrinking parameter + alpha = self._get_shrink_param(X, S, F) + + # shrink covariance + if alpha > 0: + S *= 1 - alpha + F *= alpha + S += F + + return S + + def _get_shrink_target(self, X: np.ndarray, S: np.ndarray) -> np.ndarray: + """get shrinking target `F`""" + if self.target == self.TGT_CONST_VAR: + return self._get_shrink_target_const_var(X, S) + if self.target == self.TGT_CONST_CORR: + return self._get_shrink_target_const_corr(X, S) + if self.target == self.TGT_SINGLE_FACTOR: + return self._get_shrink_target_single_factor(X, S) + return self.target + + def _get_shrink_target_const_var(self, X: np.ndarray, S: np.ndarray) -> np.ndarray: + """get shrinking target with constant variance + + This target assumes zero pair-wise correlation and constant variance. + The constant variance is estimated by averaging all sample's variances. + """ + n = len(S) + F = np.eye(n) + np.fill_diagonal(F, np.mean(np.diag(S))) + return F + + def _get_shrink_target_const_corr(self, X: np.ndarray, S: np.ndarray) -> np.ndarray: + """get shrinking target with constant correlation + + This target assumes constant pair-wise correlation but keep the sample variance. + The constant correlation is estimated by averaging all pairwise correlations. + """ + n = len(S) + var = np.diag(S) + sqrt_var = np.sqrt(var) + covar = np.outer(sqrt_var, sqrt_var) + r_bar = (np.sum(S / covar) - n) / (n * (n - 1)) + F = r_bar * covar + np.fill_diagonal(F, var) + return F + + def _get_shrink_target_single_factor(self, X: np.ndarray, S: np.ndarray) -> np.ndarray: + """get shrinking target with single factor model""" + X_mkt = np.nanmean(X, axis=1) + cov_mkt = np.asarray(X.T.dot(X_mkt) / len(X)) + var_mkt = np.asarray(X_mkt.dot(X_mkt) / len(X)) + F = np.outer(cov_mkt, cov_mkt) / var_mkt + np.fill_diagonal(F, np.diag(S)) + return F + + def _get_shrink_param(self, X: np.ndarray, S: np.ndarray, F: np.ndarray) -> float: + """get shrinking parameter `alpha` + + Note: + The Ledoit-Wolf shrinking parameter estimator consists of three different methods. + """ + if self.alpha == self.SHR_OAS: + return self._get_shrink_param_oas(X, S, F) + elif self.alpha == self.SHR_LW: + if self.target == self.TGT_CONST_VAR: + return self._get_shrink_param_lw_const_var(X, S, F) + if self.target == self.TGT_CONST_CORR: + return self._get_shrink_param_lw_const_corr(X, S, F) + if self.target == self.TGT_SINGLE_FACTOR: + return self._get_shrink_param_lw_single_factor(X, S, F) + return self.alpha + + def _get_shrink_param_oas(self, X: np.ndarray, S: np.ndarray, F: np.ndarray) -> float: + """Oracle Approximating Shrinkage Estimator + + This method uses the following formula to estimate the `alpha` + parameter for the shrink covariance estimator: + A = (1 - 2 / p) * trace(S^2) + trace^2(S) + B = (n + 1 - 2 / p) * (trace(S^2) - trace^2(S) / p) + alpha = A / B + where `n`, `p` are the dim of observations and variables respectively. + """ + trS2 = np.sum(S ** 2) + tr2S = np.trace(S) ** 2 + + n, p = X.shape + + A = (1 - 2 / p) * (trS2 + tr2S) + B = (n + 1 - 2 / p) * (trS2 + tr2S / p) + alpha = A / B + + return alpha + + def _get_shrink_param_lw_const_var(self, X: np.ndarray, S: np.ndarray, F: np.ndarray) -> float: + """Ledoit-Wolf Shrinkage Estimator (Constant Variance) + + This method shrinks the covariance matrix towards the constand variance target. + """ + t, n = X.shape + + y = X ** 2 + phi = np.sum(y.T.dot(y) / t - S ** 2) + + gamma = np.linalg.norm(S - F, "fro") ** 2 + + kappa = phi / gamma + alpha = max(0, min(1, kappa / t)) + + return alpha + + def _get_shrink_param_lw_const_corr(self, X: np.ndarray, S: np.ndarray, F: np.ndarray) -> float: + """Ledoit-Wolf Shrinkage Estimator (Constant Correlation) + + This method shrinks the covariance matrix towards the constand correlation target. + """ + t, n = X.shape + + var = np.diag(S) + sqrt_var = np.sqrt(var) + r_bar = (np.sum(S / np.outer(sqrt_var, sqrt_var)) - n) / (n * (n - 1)) + + y = X ** 2 + phi_mat = y.T.dot(y) / t - S ** 2 + phi = np.sum(phi_mat) + + theta_mat = (X ** 3).T.dot(X) / t - var[:, None] * S + np.fill_diagonal(theta_mat, 0) + rho = np.sum(np.diag(phi_mat)) + r_bar * np.sum(np.outer(1 / sqrt_var, sqrt_var) * theta_mat) + + gamma = np.linalg.norm(S - F, "fro") ** 2 + + kappa = (phi - rho) / gamma + alpha = max(0, min(1, kappa / t)) + + return alpha + + def _get_shrink_param_lw_single_factor(self, X: np.ndarray, S: np.ndarray, F: np.ndarray) -> float: + """Ledoit-Wolf Shrinkage Estimator (Single Factor Model) + + This method shrinks the covariance matrix towards the single factor model target. + """ + t, n = X.shape + + X_mkt = np.nanmean(X, axis=1) + cov_mkt = np.asarray(X.T.dot(X_mkt) / len(X)) + var_mkt = np.asarray(X_mkt.dot(X_mkt) / len(X)) + + y = X ** 2 + phi = np.sum(y.T.dot(y)) / t - np.sum(S ** 2) + + rdiag = np.sum(y ** 2) / t - np.sum(np.diag(S) ** 2) + z = X * X_mkt[:, None] + v1 = y.T.dot(z) / t - cov_mkt[:, None] * S + roff1 = np.sum(v1 * cov_mkt[:, None].T) / var_mkt - np.sum(np.diag(v1) * cov_mkt) / var_mkt + v3 = z.T.dot(z) / t - var_mkt * S + roff3 = ( + np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt ** 2 - np.sum( + np.diag(v3) * cov_mkt ** 2) / var_mkt ** 2 + ) + roff = 2 * roff1 - roff3 + rho = rdiag + roff + + gamma = np.linalg.norm(S - F, "fro") ** 2 + + kappa = (phi - rho) / gamma + alpha = max(0, min(1, kappa / t)) + + return alpha diff --git a/qlib/model/riskmodel/structured.py b/qlib/model/riskmodel/structured.py new file mode 100644 index 000000000..e778c2faa --- /dev/null +++ b/qlib/model/riskmodel/structured.py @@ -0,0 +1,152 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +import numpy as np +import pandas as pd +from typing import Union +from sklearn.decomposition import PCA, FactorAnalysis + +from qlib.model.riskmodel import RiskModel + + +class StructuredCovEstimator(RiskModel): + """Structured Covariance Estimator + + This estimator assumes observations can be predicted by multiple factors + X = FB + U + where `F` can be specified by explicit risk factors or latent factors. + + Therefore the structured covariance can be estimated by + cov(X) = F cov(B) F.T + cov(U) + + We use latent factor models to estimate the structured covariance. + Specifically, the following latent factor models are supported: + - `pca`: Principal Component Analysis + - `fa`: Factor Analysis + + Reference: [1] Fan, J., Liao, Y., & Liu, H. (2016). An overview of the estimation of large covariance and + precision matrices. Econometrics Journal, 19(1), C1–C32. https://doi.org/10.1111/ectj.12061 + """ + + FACTOR_MODEL_PCA = "pca" + FACTOR_MODEL_FA = "fa" + NAN_OPTION = "fill" + + def __init__( + self, + factor_model: str = "pca", + num_factors: int = 10, + assume_centered: bool = False, + scale_return: bool = True, + ): + """ + Args: + factor_model (str): the latent factor models used to estimate the structured covariance (`pca`/`fa`). + num_factors (int): number of components to keep. + assume_centered (bool): whether the data is assumed to be centered. + scale_return (bool): whether scale returns as percentage. + """ + super().__init__(self.NAN_OPTION, assume_centered, scale_return) + + assert factor_model in [ + self.FACTOR_MODEL_PCA, + self.FACTOR_MODEL_FA, + ], "factor_model={} is not supported".format(factor_model) + self.solver = PCA if factor_model == self.FACTOR_MODEL_PCA else FactorAnalysis + + self.num_factors = num_factors + + def predict( + self, + X: Union[pd.Series, pd.DataFrame, np.ndarray], + return_corr: bool = False, + is_price: bool = True, + return_decomposed_components=False, + ) -> Union[pd.DataFrame, np.ndarray, tuple]: + """ + Args: + X (pd.Series, pd.DataFrame or np.ndarray): data from which to estimate the covariance, + with variables as columns and observations as rows. + return_corr (bool): whether return the correlation matrix. + is_price (bool): whether `X` contains price (if not assume stock returns). + return_decomposed_components (bool): whether return decomposed components of the covariance matrix. + + Returns: + tuple or pd.DataFrame or np.ndarray: decomposed covariance matrix or estimated covariance or correlation. + """ + assert ( + not return_corr or not return_decomposed_components + ), "Can only return either correlation matrix or decomposed components." + + # transform input into 2D array + if not isinstance(X, (pd.Series, pd.DataFrame)): + columns = None + else: + if isinstance(X.index, pd.MultiIndex): + if isinstance(X, pd.DataFrame): + X = X.iloc[:, 0].unstack(level="instrument") # always use the first column + else: + X = X.unstack(level="instrument") + else: + # X is 2D DataFrame + pass + columns = X.columns # will be used to restore dataframe + X = X.values + + # calculate pct_change + if is_price: + X = X[1:] / X[:-1] - 1 # NOTE: resulting `n - 1` rows + + # scale return + if self.scale_return: + X *= 100 + + # handle nan and centered + X = self._preprocess(X) + + if return_decomposed_components: + F, cov_b, var_u = self._predict(X, return_structured=True) + return F, cov_b, var_u + else: + # estimate covariance + S = self._predict(X) + + # return correlation if needed + if return_corr: + vola = np.sqrt(np.diag(S)) + corr = S / np.outer(vola, vola) + if columns is None: + return corr + return pd.DataFrame(corr, index=columns, columns=columns) + + # return covariance + if columns is None: + return S + return pd.DataFrame(S, index=columns, columns=columns) + + def _predict(self, X: np.ndarray, return_structured=False) -> Union[np.ndarray, tuple]: + """ + covariance estimation implementation + + Args: + X (np.ndarray): data matrix containing multiple variables (columns) and observations (rows). + return_structured (bool): whether return decomposed components of the covariance matrix. + + Returns: + tuple or np.ndarray: decomposed covariance matrix or covariance matrix. + """ + + model = self.solver(self.num_factors, random_state=0).fit(X) + + F = model.components_.T # num_features x num_factors + B = model.transform(X) # num_samples x num_factors + U = X - B @ F.T + cov_b = np.cov(B.T) # num_factors x num_factors + var_u = np.var(U, axis=0) # diagonal + + if return_structured: + return F, cov_b, var_u + + cov_x = F @ cov_b @ F.T + np.diag(var_u) + + return cov_x diff --git a/qlib/portfolio/enhanced_indexing.py b/qlib/portfolio/enhanced_indexing.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/qlib/model/riskmodel_shrink.py b/qlib/portfolio/optimizer/__init__.py similarity index 100% rename from qlib/model/riskmodel_shrink.py rename to qlib/portfolio/optimizer/__init__.py diff --git a/qlib/model/riskmodel_structured.py b/qlib/portfolio/optimizer/base.py similarity index 100% rename from qlib/model/riskmodel_structured.py rename to qlib/portfolio/optimizer/base.py diff --git a/qlib/portfolio/optimizer/enhanced_indexing.py b/qlib/portfolio/optimizer/enhanced_indexing.py new file mode 100644 index 000000000..d988c776b --- /dev/null +++ b/qlib/portfolio/optimizer/enhanced_indexing.py @@ -0,0 +1,140 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +import numpy as np +import cvxpy as cp +import pandas as pd +from typing import Union + +from qlib.portfolio.optimizer import BaseOptimizer + + +class EnhancedIndexingOptimizer(BaseOptimizer): + """ + Portfolio Optimizer with Enhanced Indexing + + Note: + This optimizer always assumes full investment and no-shorting. + """ + + START_FROM_W0 = "w0" + START_FROM_BENCH = "benchmark" + DO_NOT_START_FROM = "no_warm_start" + + def __init__( + self, + lamb: float = 10, + delta: float = 0.4, + bench_dev: float = 0.01, + inds_dev: float = None, + scale_alpha: bool = True, + verbose: bool = False, + warm_start: str = DO_NOT_START_FROM, + max_iters: int = 10000, + ): + """ + Args: + lamb (float): risk aversion parameter (larger `lamb` means less focus on return) + delta (float): turnover rate limit + bench_dev (float): benchmark deviation limit + inds_dev (float/None): industry deviation limit, set `inds_dev` to None to ignore industry specific + restriction + scale_alpha (bool): if to scale alpha to match the volatility of the covariance matrix + verbose (bool): if print detailed information about the solver + warm_start (str): whether try to warm start (`w0`/`benchmark`/``) + (https://www.cvxpy.org/tutorial/advanced/index.html#warm-start) + """ + + assert lamb >= 0, "risk aversion parameter `lamb` should be positive" + self.lamb = lamb + + assert delta >= 0, "turnover limit `delta` should be positive" + self.delta = delta + + assert bench_dev >= 0, "benchmark deviation limit `bench_dev` should be positive" + self.bench_dev = bench_dev + + assert inds_dev is None or inds_dev >= 0, "industry deviation limit `inds_dev` should be positive or None." + self.inds_dev = inds_dev + + assert warm_start in [ + self.DO_NOT_START_FROM, + self.START_FROM_W0, + self.START_FROM_BENCH, + ], "illegal warm start option" + self.start_from_w0 = warm_start == self.START_FROM_W0 + self.start_from_bench = warm_start == self.START_FROM_BENCH + + self.scale_alpha = scale_alpha + self.verbose = verbose + self.max_iters = max_iters + + def __call__( + self, + u: np.ndarray, + F: np.ndarray, + covB: np.ndarray, + varU: np.ndarray, + w0: np.ndarray, + w_bench: np.ndarray, + inds_onehot: np.ndarray = None, + ) -> Union[np.ndarray, pd.Series]: + """ + Args: + u (np.ndarray): expected returns (a.k.a., alpha) + F, covB, varU (np.ndarray): see StructuredCovEstimator + w0 (np.ndarray): initial weights (for turnover control) + w_bench (np.ndarray): benchmark weights + inds_onehot (np.ndarray): industry (onehot) + + Returns: + np.ndarray or pd.Series: optimized portfolio allocation + """ + assert inds_onehot is not None or self.inds_dev is None, "Industry onehot vector is required." + + # scale alpha to match volatility + if self.scale_alpha: + u = u / u.std() + x_variance = np.mean(np.diag(F @ covB @ F.T) + varU) + u *= x_variance ** 0.5 + + w = cp.Variable(len(u)) # num_assets + v = w @ F # num_factors + ret = w @ u + risk = cp.quad_form(v, covB) + cp.sum(cp.multiply(varU, w ** 2)) + obj = cp.Maximize(ret - self.lamb * risk) + d_bench = w - w_bench + cons = [ + w >= 0, + cp.sum(w) == 1, + d_bench >= -self.bench_dev, + d_bench <= self.bench_dev, + ] + + if self.inds_dev is not None: + d_inds = d_bench @ inds_onehot + cons.append(d_inds >= -self.inds_dev) + cons.append(d_inds <= self.inds_dev) + + if w0 is not None: + turnover = cp.sum(cp.abs(w - w0)) + cons.append(turnover <= self.delta) + + warm_start = False + if self.start_from_w0: + if w0 is None: + print("Warning: try warm start with w0, but w0 is `None`.") + else: + w.value = w0 + warm_start = True + elif self.start_from_bench: + w.value = w_bench + warm_start = True + + prob = cp.Problem(obj, cons) + prob.solve(solver=cp.SCS, verbose=self.verbose, warm_start=warm_start, max_iters=self.max_iters) + + if prob.status != "optimal": + print("Warning: solve failed.", prob.status) + + return np.asarray(w.value) diff --git a/qlib/portfolio/optimizer.py b/qlib/portfolio/optimizer/optimizer.py similarity index 62% rename from qlib/portfolio/optimizer.py rename to qlib/portfolio/optimizer/optimizer.py index 6ee396a51..17a7fc30a 100644 --- a/qlib/portfolio/optimizer.py +++ b/qlib/portfolio/optimizer/optimizer.py @@ -4,11 +4,12 @@ import abc import warnings import numpy as np -import cvxpy as cp import pandas as pd import scipy.optimize as so from typing import Optional, Union, Callable, List +from qlib.portfolio.enhanced_indexing import EnhancedIndexingOptimizer + class BaseOptimizer(abc.ABC): """ Construct portfolio with a optimization related method """ @@ -38,13 +39,13 @@ class PortfolioOptimizer(BaseOptimizer): OPT_INV = "inv" def __init__( - self, - method: str = "inv", - lamb: float = 0, - delta: float = 0, - alpha: float = 0.0, - scale_alpha: bool = True, - tol: float = 1e-8, + self, + method: str = "inv", + lamb: float = 0, + delta: float = 0, + alpha: float = 0.0, + scale_alpha: bool = True, + tol: float = 1e-8, ): """ Args: @@ -71,10 +72,10 @@ class PortfolioOptimizer(BaseOptimizer): self.scale_alpha = scale_alpha def __call__( - self, - S: Union[np.ndarray, pd.DataFrame], - u: Optional[Union[np.ndarray, pd.Series]] = None, - w0: Optional[Union[np.ndarray, pd.Series]] = None, + self, + S: Union[np.ndarray, pd.DataFrame], + u: Optional[Union[np.ndarray, pd.Series]] = None, + w0: Optional[Union[np.ndarray, pd.Series]] = None, ) -> Union[np.ndarray, pd.Series]: """ Args: @@ -163,7 +164,7 @@ class PortfolioOptimizer(BaseOptimizer): return self._solve(len(S), self._get_objective_gmv(S), *self._get_constrains(w0)) def _optimize_mvo( - self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None + self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None ) -> np.ndarray: """optimize mean-variance portfolio @@ -259,7 +260,6 @@ class PortfolioOptimizer(BaseOptimizer): # add l2 regularization wrapped_obj = obj if self.alpha > 0: - def opt_obj(x): return obj(x) + self.alpha * np.sum(np.square(x)) @@ -272,134 +272,3 @@ class PortfolioOptimizer(BaseOptimizer): warnings.warn(f"optimization not success ({sol.status})") return sol.x - - -class EnhancedIndexingOptimizer(BaseOptimizer): - """ - Portfolio Optimizer with Enhanced Indexing - - Note: - This optimizer always assumes full investment and no-shorting. - """ - - START_FROM_W0 = "w0" - START_FROM_BENCH = "benchmark" - DO_NOT_START_FROM = "no_warm_start" - - def __init__( - self, - lamb: float = 10, - delta: float = 0.4, - bench_dev: float = 0.01, - inds_dev: float = None, - scale_alpha: bool = True, - verbose: bool = False, - warm_start: str = DO_NOT_START_FROM, - max_iters: int = 10000, - ): - """ - Args: - lamb (float): risk aversion parameter (larger `lamb` means less focus on return) - delta (float): turnover rate limit - bench_dev (float): benchmark deviation limit - inds_dev (float/None): industry deviation limit, set `inds_dev` to None to ignore industry specific - restriction - scale_alpha (bool): if to scale alpha to match the volatility of the covariance matrix - verbose (bool): if print detailed information about the solver - warm_start (str): whether try to warm start (`w0`/`benchmark`/``) - (https://www.cvxpy.org/tutorial/advanced/index.html#warm-start) - """ - - assert lamb >= 0, "risk aversion parameter `lamb` should be positive" - self.lamb = lamb - - assert delta >= 0, "turnover limit `delta` should be positive" - self.delta = delta - - assert bench_dev >= 0, "benchmark deviation limit `bench_dev` should be positive" - self.bench_dev = bench_dev - - assert inds_dev >= 0, "industry deviation limit `inds_dev` should be positive" - self.inds_dev = inds_dev - - assert warm_start in [ - self.DO_NOT_START_FROM, - self.START_FROM_W0, - self.START_FROM_BENCH, - ], "illegal warm start option" - self.start_from_w0 = warm_start == self.START_FROM_W0 - self.start_from_bench = warm_start == self.START_FROM_BENCH - - self.scale_alpha = scale_alpha - self.verbose = verbose - self.max_iters = max_iters - - def __call__( - self, - u: np.ndarray, - F: np.ndarray, - covB: np.ndarray, - varU: np.ndarray, - w0: np.ndarray, - w_bench: np.ndarray, - inds_onehot: np.ndarray = None, - ) -> Union[np.ndarray, pd.Series]: - """ - Args: - u (np.ndarray): expected returns (a.k.a., alpha) - F, covB, varU (np.ndarray): see StructuredCovEstimator - w0 (np.ndarray): initial weights (for turnover control) - w_bench (np.ndarray): benchmark weights - inds_onehot (np.ndarray): industry (onehot) - - Returns: - np.ndarray or pd.Series: optimized portfolio allocation - """ - assert inds_onehot is not None or self.inds_dev is None, "Industry onehot vector is required." - - # scale alpha to match volatility - if self.scale_alpha: - u = u / u.std() - x_variance = np.mean(np.diag(F @ covB @ F.T) + varU) - u *= x_variance ** 0.5 - - w = cp.Variable(len(u)) # num_assets - v = w @ F # num_factors - ret = w @ u - risk = cp.quad_form(v, covB) + cp.sum(cp.multiply(varU, w ** 2)) - obj = cp.Maximize(ret - self.lamb * risk) - d_bench = w - w_bench - cons = [ - w >= 0, - cp.sum(w) == 1, - d_bench >= -self.bench_dev, - d_bench <= self.bench_dev, - ] - - if self.inds_dev is not None: - d_inds = d_bench @ inds_onehot - cons.append(d_inds >= -self.inds_dev) - cons.append(d_inds <= self.inds_dev) - - if w0 is not None: - turnover = cp.sum(cp.abs(w - w0)) - cons.append(turnover <= self.delta) - - warm_start = False - if self.start_from_w0: - if w0 is None: - print("Warning: try warm start with w0, but w0 is `None`.") - else: - w.value = w0 - warm_start = True - elif self.start_from_bench: - w.value = w_bench - warm_start = True - - prob = cp.Problem(obj, cons) - prob.solve(solver=cp.SCS, verbose=self.verbose, warm_start=warm_start, max_iters=self.max_iters) - - if prob.status != "optimal": - print("Warning: solve failed.", prob.status) - - return np.asarray(w.value) diff --git a/tests/test_enhanced_indexing.py b/tests/test_enhanced_indexing.py deleted file mode 100644 index f21d51984..000000000 --- a/tests/test_enhanced_indexing.py +++ /dev/null @@ -1,282 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. - -import sys -import math -import shutil -import unittest -import numpy as np -import pandas as pd -from tqdm import tqdm -from pathlib import Path - -import qlib -from qlib.config import C -from qlib.utils import init_instance_by_config, flatten_dict -from qlib.workflow import R -from qlib.config import REG_CN -from qlib.workflow.record_temp import SignalRecord, SigAnaRecord -from qlib.tests import TestAutoData -from qlib.portfolio.optimizer import EnhancedIndexingOptimizer -from qlib.model.riskmodel import StructuredCovEstimator -from qlib.data.dataset.loader import QlibDataLoader -from qlib.data.dataset.handler import DataHandler -from qlib.data import D -from qlib.utils import exists_qlib_data, init_instance_by_config - -market = "all" -trade_gap = 21 -label_config = "Ref($close, -{}) / Ref($close, -1) - 1".format(trade_gap) # reconstruct portfolio once a month - -provider_uri = "~/.qlib_ei/qlib_data/cn_data" # target_dir -if not exists_qlib_data(provider_uri): - print(f"Qlib data is not found in {provider_uri}") - sys.path.append(str(Path.cwd().parent.joinpath("scripts"))) - from get_data import GetData - GetData().qlib_data(target_dir=provider_uri, region=REG_CN) -qlib.init(provider_uri=provider_uri, region=REG_CN) - -################################### -# train model -################################### -data_handler_config = { - "start_time": "2008-01-01", - "end_time": "2020-08-01", - "fit_start_time": "2008-01-01", - "fit_end_time": "2014-11-30", - "instruments": market, - "label": [label_config] -} - -task = { - "model": { - "class": "LGBModel", - "module_path": "qlib.contrib.model.gbdt", - "kwargs": { - "loss": "mse", - "colsample_bytree": 0.8879, - "learning_rate": 0.0421, - "subsample": 0.8789, - "lambda_l1": 205.6999, - "lambda_l2": 580.9768, - "max_depth": 8, - "num_leaves": 210, - "num_threads": 32, - }, - }, - "dataset": { - "class": "DatasetH", - "module_path": "qlib.data.dataset", - "kwargs": { - "handler": { - "class": "Alpha158", - "module_path": "qlib.contrib.data.handler", - "kwargs": data_handler_config, - }, - "segments": { - "train": ("2008-01-01", "2014-11-30"), - "valid": ("2015-01-01", "2016-11-30"), - "test": ("2017-01-01", "2018-01-01"), - }, - }, - }, -} - - -class CSI300: - """Simulate CSI300 as the Benchmark for Enhanced Indexing to Track""" - - def __init__(self): - # provider_uri = '/nfs_data/qlib_data/ycz_daily/qlib' - # qlib.init(provider_uri=provider_uri, region=REG_CN, dataset_cache=None, expression_cache=None) - self.csi_weight = D.features(D.instruments('csi300'), ['$csi300_weight']) - - def __call__(self, pd_index, trade_date): - weights = np.zeros(len(pd_index)) - - for idx, instrument in enumerate(pd_index): - if (instrument, trade_date) in self.csi_weight.index: - weight = self.csi_weight.loc[(instrument, trade_date)].values[0] - if not math.isnan(weight): - weights[idx] = weight - - assert weights.sum() > 0, ' Fetch CSI Weights Error!' - weights = weights / weights.sum() - - return weights - - -class EnhancedIndexingStrategy: - """Enhanced Indexing Strategy""" - - def __init__(self): - self.benchmark = CSI300() - - provider_uri = "~/.qlib_ei/qlib_data/cn_data" - qlib.init(provider_uri=provider_uri, region=REG_CN) - - self.data_handler = DataHandler(market, "2015-01-01", "2019-01-01", QlibDataLoader(["$close"])) - self.label_handler = DataHandler(market, "2015-01-01", "2019-01-01", QlibDataLoader([label_config])) - self.cov_estimator = StructuredCovEstimator() - self.optimizer = EnhancedIndexingOptimizer(lamb=0.1, delta=0.4, bench_dev=0.03, max_iters=50000) - - def update(self, score_series, current, pred_date): - """ - Parameters - ----------- - score_series : pd.Series - stock_id , score. - current : Position() - current of account. - trade_exchange : Exchange() - exchange. - trade_date : pd.Timestamp - date. - """ - print(score_series) - score_series = score_series.dropna() - - # portfolio init weight - init_weight = current.reindex(score_series.index, fill_value=0).values.squeeze() - init_weight_sum = init_weight.sum() - if init_weight_sum > 0: - init_weight /= init_weight_sum - - # covariance estimation - selector = (self.data_handler.get_range_selector(pred_date, 252), score_series.index) - price = self.data_handler.fetch(selector, level=None, squeeze=True) - F, cov_b, var_u = self.cov_estimator.predict(price, return_decomposed_components=True) - - # optimize target portfolio - w_bench = self.benchmark(score_series.index, pred_date) - passed_init_weight = init_weight if init_weight_sum > 0 else None - # print(F) - # print(cov_b) - # print(var_u) - # print(passed_init_weight) - # print(w_bench) - target_weight = self.optimizer(score_series.values, F, cov_b, var_u, passed_init_weight, w_bench) - # print(target_weight) - target = pd.DataFrame(data=target_weight, index=score_series.index) - - active_weights = target_weight - w_bench - selector = (self.label_handler.get_range_selector(pred_date, 1), score_series.index) - label = self.label_handler.fetch(selector, level=None, squeeze=True) - alpha = 0 - for instrument, weight in zip(score_series.index, active_weights): - delta = label.loc[(pred_date, instrument)] - alpha += weight * (0 if math.isnan(delta) else delta) - - print(alpha) - - return alpha, target - - -def train(): - """train model - - Returns - ------- - pred_score: pandas.DataFrame - predict scores - performance: dict - model performance - """ - - # model initiation - model = init_instance_by_config(task["model"]) - dataset = init_instance_by_config(task["dataset"]) - - # start exp - with R.start(experiment_name="workflow"): - R.log_params(**flatten_dict(task)) - model.fit(dataset) - - # prediction - recorder = R.get_recorder() - rid = recorder.id - sr = SignalRecord(model, dataset, recorder) - sr.generate() - pred_score = sr.load() - - # calculate ic and ric - sar = SigAnaRecord(recorder) - sar.generate() - ic = sar.load(sar.get_path("ic.pkl")) - ric = sar.load(sar.get_path("ric.pkl")) - - return pred_score, {"ic": ic, "ric": ric}, rid - - -def backtest_analysis(scores): - """backtest enhanced indexing - - Parameters - ---------- - scores: pandas.DataFrame - predict scores - - Returns - ------- - sharpe_ratio: floating-point - sharpe ratio of the enhanced indexing portfolio - """ - - # backtest and analysis - with R.start(experiment_name="backtest_analysis"): - strategy = EnhancedIndexingStrategy() - dates = scores.index.get_level_values(0).unique() - - alphas = [] - current = pd.DataFrame() - gap_between_next_trade = 0 - for date in tqdm(dates): - if gap_between_next_trade == 0: - score_series = scores.loc[date] - alpha, current = strategy.update(score_series, current, date) - alphas.append(alpha) - gap_between_next_trade = trade_gap - else: - gap_between_next_trade -= 1 - - alphas = np.array(alphas) - sharpe_ratio = alphas.mean() / np.std(alphas) - print('Sharpe:', sharpe_ratio) - - return sharpe_ratio - - -class TestAllFlow(TestAutoData): - PRED_SCORE = None - REPORT_NORMAL = None - POSITIONS = None - RID = None - - @classmethod - def tearDownClass(cls) -> None: - shutil.rmtree(str(Path(C["exp_manager"]["kwargs"]["uri"].strip("file:")).resolve())) - - def test_0_train(self): - TestAllFlow.PRED_SCORE, ic_ric, TestAllFlow.RID = train() - self.assertGreaterEqual(ic_ric["ic"].all(), 0, "train failed") - self.assertGreaterEqual(ic_ric["ric"].all(), 0, "train failed") - - def test_1_backtest(self): - sharpe_ratio = backtest_analysis(TestAllFlow.PRED_SCORE) - self.assertGreaterEqual( - sharpe_ratio, - 0.90, - "backtest failed", - ) - - -def suite(): - _suite = unittest.TestSuite() - _suite.addTest(TestAllFlow("test_0_train")) - _suite.addTest(TestAllFlow("test_1_backtest")) - return _suite - - -if __name__ == "__main__": - runner = unittest.TextTestRunner() - runner.run(suite()) From 0f3e3d206b51300b953e06674104af2ae23dc786 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Thu, 4 Mar 2021 22:47:42 +0800 Subject: [PATCH 049/134] Update __init__.py. --- qlib/model/riskmodel/__init__.py | 7 +++++++ qlib/model/riskmodel/base.py | 4 ---- qlib/portfolio/__init__.py | 2 ++ qlib/portfolio/optimizer/__init__.py | 6 ++++++ qlib/portfolio/optimizer/base.py | 13 +++++++++++++ qlib/portfolio/optimizer/optimizer.py | 13 ++----------- 6 files changed, 30 insertions(+), 15 deletions(-) diff --git a/qlib/model/riskmodel/__init__.py b/qlib/model/riskmodel/__init__.py index e69de29bb..05af6b7d3 100644 --- a/qlib/model/riskmodel/__init__.py +++ b/qlib/model/riskmodel/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +from .base import RiskModel +from .poet import POETCovEstimator +from .shrink import ShrinkCovEstimator +from .structured import StructuredCovEstimator diff --git a/qlib/model/riskmodel/base.py b/qlib/model/riskmodel/base.py index d5b009ccc..02ab8c2fb 100644 --- a/qlib/model/riskmodel/base.py +++ b/qlib/model/riskmodel/base.py @@ -7,10 +7,6 @@ from typing import Union from qlib.model.base import BaseModel -from qlib.model.riskmodel_poet import POETCovEstimator -from qlib.model.riskmodel_shrink import ShrinkCovEstimator -from qlib.model.riskmodel_structured import StructuredCovEstimator - class RiskModel(BaseModel): """Risk Model diff --git a/qlib/portfolio/__init__.py b/qlib/portfolio/__init__.py index e69de29bb..139597f9c 100644 --- a/qlib/portfolio/__init__.py +++ b/qlib/portfolio/__init__.py @@ -0,0 +1,2 @@ + + diff --git a/qlib/portfolio/optimizer/__init__.py b/qlib/portfolio/optimizer/__init__.py index e69de29bb..5080b9a46 100644 --- a/qlib/portfolio/optimizer/__init__.py +++ b/qlib/portfolio/optimizer/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +from .base import BaseOptimizer +from .optimizer import PortfolioOptimizer +from .enhanced_indexing import EnhancedIndexingOptimizer diff --git a/qlib/portfolio/optimizer/base.py b/qlib/portfolio/optimizer/base.py index e69de29bb..502443869 100644 --- a/qlib/portfolio/optimizer/base.py +++ b/qlib/portfolio/optimizer/base.py @@ -0,0 +1,13 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +import abc + + +class BaseOptimizer(abc.ABC): + """ Construct portfolio with a optimization related method """ + + @abc.abstractmethod + def __call__(self, *args, **kwargs) -> object: + """ Generate a optimized portfolio allocation """ + pass diff --git a/qlib/portfolio/optimizer/optimizer.py b/qlib/portfolio/optimizer/optimizer.py index 17a7fc30a..3daa98af3 100644 --- a/qlib/portfolio/optimizer/optimizer.py +++ b/qlib/portfolio/optimizer/optimizer.py @@ -1,23 +1,14 @@ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. -import abc + import warnings import numpy as np import pandas as pd import scipy.optimize as so from typing import Optional, Union, Callable, List -from qlib.portfolio.enhanced_indexing import EnhancedIndexingOptimizer - - -class BaseOptimizer(abc.ABC): - """ Construct portfolio with a optimization related method """ - - @abc.abstractmethod - def __call__(self, *args, **kwargs) -> object: - """ Generate a optimized portfolio allocation """ - pass +from qlib.portfolio.optimizer import BaseOptimizer class PortfolioOptimizer(BaseOptimizer): From c4d6e00470e7cb1e962c56e971a6bfe5874ffadc Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Thu, 4 Mar 2021 21:04:01 -0800 Subject: [PATCH 050/134] Fix logic of uri in ExpM and add test --- qlib/data/ops.py | 2 +- qlib/workflow/__init__.py | 6 +++--- qlib/workflow/exp.py | 15 +++++++-------- qlib/workflow/expm.py | 28 +++++++++++++++++----------- qlib/workflow/recorder.py | 2 +- tests/test_all_pipeline.py | 31 +++++++++++++++++++++++++++++-- 6 files changed, 58 insertions(+), 26 deletions(-) diff --git a/qlib/data/ops.py b/qlib/data/ops.py index 940c24002..9307fdafa 100644 --- a/qlib/data/ops.py +++ b/qlib/data/ops.py @@ -1489,7 +1489,7 @@ OpsList = [ ] -class OpsWrapper(object): +class OpsWrapper: """Ops Wrapper""" def __init__(self): diff --git a/qlib/workflow/__init__.py b/qlib/workflow/__init__.py index c6bf0c86c..7bff505ce 100644 --- a/qlib/workflow/__init__.py +++ b/qlib/workflow/__init__.py @@ -39,8 +39,8 @@ class QlibRecorder: name of the recorder under the experiment one wants to start. uri : str The tracking uri of the experiment, where all the artifacts/metrics etc. will be stored. - The default uri are set in the qlib.config. Note that this uri argument will not change the one defined in the config file. - Therefore, the next time when user call this function in the same experiment, + The default uri is set in the qlib.config. Note that this uri argument will not change the one defined in the config file. + Therefore, the next time when users call this function in the same experiment, they have to also specify this argument with the same value. Otherwise, inconsistent uri may occur. """ run = self.start_exp(experiment_name, recorder_name, uri) @@ -280,7 +280,7 @@ class QlibRecorder: ------- The uri of current experiment manager. """ - return self.exp_manager.get_uri() + return self.exp_manager.uri def get_recorder(self, recorder_id=None, recorder_name=None, experiment_name=None): """ diff --git a/qlib/workflow/exp.py b/qlib/workflow/exp.py index 15bb7604c..18b0a143d 100644 --- a/qlib/workflow/exp.py +++ b/qlib/workflow/exp.py @@ -11,7 +11,7 @@ from ..log import get_module_logger logger = get_module_logger("workflow", "INFO") -class Experiment(object): +class Experiment: """ This is the `Experiment` class for each experiment being run. The API is designed similar to mlflow. (The link: https://mlflow.org/docs/latest/python_api/mlflow.html) @@ -173,7 +173,7 @@ class MLflowExperiment(Experiment): self._uri = uri self._default_name = None self._default_rec_name = "mlflow_recorder" - self.client = mlflow.tracking.MlflowClient(tracking_uri=self._uri) + self._client = mlflow.tracking.MlflowClient(tracking_uri=self._uri) def start(self, recorder_name=None): logger.info(f"Experiment {self.id} starts running ...") @@ -208,7 +208,6 @@ class MLflowExperiment(Experiment): else: recorder, is_new = self._get_recorder(recorder_id=recorder_id, recorder_name=recorder_name), False if is_new: - mlflow.set_experiment(self.name) self.active_recorder = recorder # start the recorder self.active_recorder.start_run() @@ -237,7 +236,7 @@ class MLflowExperiment(Experiment): ), "Please input at least one of recorder id or name before retrieving recorder." if recorder_id is not None: try: - run = self.client.get_run(recorder_id) + run = self._client.get_run(recorder_id) recorder = MLflowRecorder(self.id, self._uri, mlflow_run=run) return recorder except MlflowException: @@ -258,7 +257,7 @@ class MLflowExperiment(Experiment): max_results = 100000 if kwargs.get("max_results") is None else kwargs.get("max_results") order_by = kwargs.get("order_by") - return self.client.search_runs([self.id], filter_string, run_view_type, max_results, order_by) + return self._client.search_runs([self.id], filter_string, run_view_type, max_results, order_by) def delete_recorder(self, recorder_id=None, recorder_name=None): assert ( @@ -266,10 +265,10 @@ class MLflowExperiment(Experiment): ), "Please input a valid recorder id or name before deleting." try: if recorder_id is not None: - self.client.delete_run(recorder_id) + self._client.delete_run(recorder_id) else: recorder = self._get_recorder(recorder_name=recorder_name) - self.client.delete_run(recorder.id) + self._client.delete_run(recorder.id) except MlflowException as e: raise Exception( f"Error: {e}. Something went wrong when deleting recorder. Please check if the name/id of the recorder is correct." @@ -278,7 +277,7 @@ class MLflowExperiment(Experiment): UNLIMITED = 50000 # FIXME: Mlflow can only list 50000 records at most!!!!!!! def list_recorders(self, max_results=UNLIMITED): - runs = self.client.search_runs(self.id, run_view_type=ViewType.ACTIVE_ONLY, max_results=max_results)[::-1] + runs = self._client.search_runs(self.id, run_view_type=ViewType.ACTIVE_ONLY, max_results=max_results)[::-1] recorders = dict() for i in range(len(runs)): recorder = MLflowRecorder(self.id, self._uri, mlflow_run=runs[i]) diff --git a/qlib/workflow/expm.py b/qlib/workflow/expm.py index 541507a73..82265b585 100644 --- a/qlib/workflow/expm.py +++ b/qlib/workflow/expm.py @@ -14,19 +14,20 @@ from ..log import get_module_logger logger = get_module_logger("workflow", "INFO") -class ExpManager(object): +class ExpManager: """ This is the `ExpManager` class for managing experiments. The API is designed similar to mlflow. (The link: https://mlflow.org/docs/latest/python_api/mlflow.html) """ def __init__(self, uri, default_exp_name): - self.uri = uri + self._default_uri = uri + self._current_uri = None self.default_exp_name = default_exp_name self.active_experiment = None # only one experiment can active each time def __repr__(self): - return "{name}(uri={uri})".format(name=self.__class__.__name__, uri=self.uri) + return "{name}(default_uri={duri}, current_uri={curi})".format(name=self.__class__.__name__, duri=self._default_uri, curi=self._current_uri) def start_exp(self, experiment_name=None, recorder_name=None, uri=None, **kwargs): """ @@ -206,7 +207,8 @@ class ExpManager(object): """ raise NotImplementedError(f"Please implement the `delete_exp` method.") - def get_uri(self): + @property + def uri(self): """ Get the default tracking URI or current URI. @@ -214,7 +216,7 @@ class ExpManager(object): ------- The tracking URI string. """ - return self.uri + return self._current_uri or self._default_uri def list_experiments(self): """ @@ -234,25 +236,27 @@ class MLflowExpManager(ExpManager): def __init__(self, uri, default_exp_name): super(MLflowExpManager, self).__init__(uri, default_exp_name) + self._client = None @property def client(self): # Delay the creation of mlflow client in case of creating `mlruns` folder when importing qlib - if not hasattr(self, "_client"): + if self._client is None: self._client = mlflow.tracking.MlflowClient(tracking_uri=self.uri) return self._client def start_exp(self, experiment_name=None, recorder_name=None, uri=None): - # set the tracking uri + # Set the tracking uri if uri is None: logger.info("No tracking URI is provided. Use the default tracking URI.") else: - self.uri = uri - # create experiment + # Temporarily re-set the current uri as the uri argument. + self._current_uri = uri + # Create experiment experiment, _ = self._get_or_create_exp(experiment_name=experiment_name) - # set up active experiment + # Set up active experiment self.active_experiment = experiment - # start the experiment + # Start the experiment self.active_experiment.start(recorder_name) return self.active_experiment @@ -261,6 +265,8 @@ class MLflowExpManager(ExpManager): if self.active_experiment is not None: self.active_experiment.end(recorder_status) self.active_experiment = None + # When an experiment end, we will release the current uri. + self._current_uri = None def create_exp(self, experiment_name=None): assert experiment_name is not None diff --git a/qlib/workflow/recorder.py b/qlib/workflow/recorder.py index 31077176d..e75ae347b 100644 --- a/qlib/workflow/recorder.py +++ b/qlib/workflow/recorder.py @@ -11,7 +11,7 @@ from ..log import get_module_logger logger = get_module_logger("workflow", "INFO") -class Recorder(object): +class Recorder: """ This is the `Recorder` class for logging the experiments. The API is designed similar to mlflow. (The link: https://mlflow.org/docs/latest/python_api/mlflow.html) diff --git a/tests/test_all_pipeline.py b/tests/test_all_pipeline.py index 97f3f986a..a75eada75 100644 --- a/tests/test_all_pipeline.py +++ b/tests/test_all_pipeline.py @@ -96,7 +96,6 @@ port_analysis_config = { } -# train def train(): """train model @@ -110,8 +109,8 @@ def train(): # model initiaiton model = init_instance_by_config(task["model"]) - print(model) dataset = init_instance_by_config(task["dataset"]) + # To test __repr__ print(dataset) print(R) @@ -122,6 +121,7 @@ def train(): # prediction recorder = R.get_recorder() + # To test __repr__ print(recorder) rid = recorder.id sr = SignalRecord(model, dataset, recorder) @@ -137,6 +137,27 @@ def train(): return pred_score, {"ic": ic, "ric": ric}, rid +def fake_experiment(): + """A fake experiment workflow to test uri + + Returns + ------- + pass_or_not_for_default_uri: bool + pass_or_not_for_current_uri: bool + temporary_exp_dir: str + """ + + # start exp + default_uri = R.get_uri() + current_uri = 'file:./temp-test-exp-mag' + with R.start(experiment_name="fake_workflow_for_expm", uri=current_uri): + R.log_params(**flatten_dict(task)) + + current_uri_to_check = R.get_uri() + default_uri_to_check = R.get_uri() + return default_uri == default_uri_to_check, current_uri == current_uri_to_check, current_uri + + def backtest_analysis(pred, rid): """backtest and analysis @@ -185,6 +206,12 @@ class TestAllFlow(TestAutoData): "backtest failed", ) + def test_2_expmanager(self): + pass_default, pass_current, uri_path = fake_experiment() + self.assertTrue(pass_default, msg='default uri is incorrect') + self.assertTrue(pass_current, msg='current uri is incorrect') + shutil.rmtree(str(Path(uri_path.strip("file:")).resolve())) + def suite(): _suite = unittest.TestSuite() From 452fb8f013904a0f119771da7dc37b4d083201b4 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Thu, 4 Mar 2021 22:33:35 -0800 Subject: [PATCH 051/134] Make mlflow client consistant with uri --- qlib/workflow/expm.py | 66 +++++++++++++++++++++++++++++++++---------- 1 file changed, 51 insertions(+), 15 deletions(-) diff --git a/qlib/workflow/expm.py b/qlib/workflow/expm.py index 82265b585..362b1a82b 100644 --- a/qlib/workflow/expm.py +++ b/qlib/workflow/expm.py @@ -7,8 +7,10 @@ from mlflow.entities import ViewType import os from pathlib import Path from contextlib import contextmanager +from typing import Optional, Text + from .exp import MLflowExperiment, Experiment -from .recorder import Recorder, MLflowRecorder +from .recorder import Recorder from ..log import get_module_logger logger = get_module_logger("workflow", "INFO") @@ -20,16 +22,24 @@ class ExpManager: (The link: https://mlflow.org/docs/latest/python_api/mlflow.html) """ - def __init__(self, uri, default_exp_name): + def __init__(self, uri: Text, default_exp_name: Optional[Text]): self._default_uri = uri self._current_uri = None self.default_exp_name = default_exp_name self.active_experiment = None # only one experiment can active each time def __repr__(self): - return "{name}(default_uri={duri}, current_uri={curi})".format(name=self.__class__.__name__, duri=self._default_uri, curi=self._current_uri) + return "{name}(default_uri={duri}, current_uri={curi})".format( + name=self.__class__.__name__, duri=self._default_uri, curi=self._current_uri + ) - def start_exp(self, experiment_name=None, recorder_name=None, uri=None, **kwargs): + def start_exp( + self, + experiment_name: Optional[Text] = None, + recorder_name: Optional[Text] = None, + uri: Optional[Text] = None, + **kwargs, + ): """ Start an experiment. This method includes first get_or_create an experiment, and then set it to be active. @@ -49,7 +59,7 @@ class ExpManager: """ raise NotImplementedError(f"Please implement the `start_exp` method.") - def end_exp(self, recorder_status: str = Recorder.STATUS_S, **kwargs): + def end_exp(self, recorder_status: Text = Recorder.STATUS_S, **kwargs): """ End an active experiment. @@ -62,7 +72,7 @@ class ExpManager: """ raise NotImplementedError(f"Please implement the `end_exp` method.") - def create_exp(self, experiment_name=None): + def create_exp(self, experiment_name: Optional[Text] = None): """ Create an experiment. @@ -218,6 +228,30 @@ class ExpManager: """ return self._current_uri or self._default_uri + def set_uri(self, uri: Optional[Text] = None): + """ + Set the current tracking URI and the corresponding variables. + + Parameters + ---------- + uri : str + + """ + if uri is None: + logger.info("No tracking URI is provided. Use the default tracking URI.") + self._current_uri = self._default_uri + else: + # Temporarily re-set the current uri as the uri argument. + self._current_uri = uri + # Customized features for subclasses. + self._set_uri() + + def _set_uri(self): + """ + Customized features for subclasses' set_uri function. + """ + raise NotImplementedError(f"Please implement the `_set_uri` method.") + def list_experiments(self): """ List all the existing experiments. @@ -234,10 +268,14 @@ class MLflowExpManager(ExpManager): Use mlflow to implement ExpManager. """ - def __init__(self, uri, default_exp_name): + def __init__(self, uri: Text, default_exp_name: Optional[Text]): super(MLflowExpManager, self).__init__(uri, default_exp_name) self._client = None + def _set_uri(self): + self._client = mlflow.tracking.MlflowClient(tracking_uri=self.uri) + logger.info('{:}'.format(self._client)) + @property def client(self): # Delay the creation of mlflow client in case of creating `mlruns` folder when importing qlib @@ -245,13 +283,11 @@ class MLflowExpManager(ExpManager): self._client = mlflow.tracking.MlflowClient(tracking_uri=self.uri) return self._client - def start_exp(self, experiment_name=None, recorder_name=None, uri=None): + def start_exp( + self, experiment_name: Optional[Text] = None, recorder_name: Optional[Text] = None, uri: Optional[Text] = None + ): # Set the tracking uri - if uri is None: - logger.info("No tracking URI is provided. Use the default tracking URI.") - else: - # Temporarily re-set the current uri as the uri argument. - self._current_uri = uri + self.set_uri(uri) # Create experiment experiment, _ = self._get_or_create_exp(experiment_name=experiment_name) # Set up active experiment @@ -261,14 +297,14 @@ class MLflowExpManager(ExpManager): return self.active_experiment - def end_exp(self, recorder_status: str = Recorder.STATUS_S): + def end_exp(self, recorder_status: Text = Recorder.STATUS_S): if self.active_experiment is not None: self.active_experiment.end(recorder_status) self.active_experiment = None # When an experiment end, we will release the current uri. self._current_uri = None - def create_exp(self, experiment_name=None): + def create_exp(self, experiment_name: Optional[Text] = None): assert experiment_name is not None # init experiment experiment_id = self.client.create_experiment(experiment_name) From e327f404e33c7fc12f7c2fb43dd383b5d9dcaba4 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Thu, 4 Mar 2021 22:37:58 -0800 Subject: [PATCH 052/134] Fix pylint issues --- qlib/workflow/expm.py | 2 +- tests/test_all_pipeline.py | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/qlib/workflow/expm.py b/qlib/workflow/expm.py index 362b1a82b..4ba72a634 100644 --- a/qlib/workflow/expm.py +++ b/qlib/workflow/expm.py @@ -274,7 +274,7 @@ class MLflowExpManager(ExpManager): def _set_uri(self): self._client = mlflow.tracking.MlflowClient(tracking_uri=self.uri) - logger.info('{:}'.format(self._client)) + logger.info("{:}".format(self._client)) @property def client(self): diff --git a/tests/test_all_pipeline.py b/tests/test_all_pipeline.py index a75eada75..d9d684697 100644 --- a/tests/test_all_pipeline.py +++ b/tests/test_all_pipeline.py @@ -149,10 +149,10 @@ def fake_experiment(): # start exp default_uri = R.get_uri() - current_uri = 'file:./temp-test-exp-mag' + current_uri = "file:./temp-test-exp-mag" with R.start(experiment_name="fake_workflow_for_expm", uri=current_uri): R.log_params(**flatten_dict(task)) - + current_uri_to_check = R.get_uri() default_uri_to_check = R.get_uri() return default_uri == default_uri_to_check, current_uri == current_uri_to_check, current_uri @@ -208,8 +208,8 @@ class TestAllFlow(TestAutoData): def test_2_expmanager(self): pass_default, pass_current, uri_path = fake_experiment() - self.assertTrue(pass_default, msg='default uri is incorrect') - self.assertTrue(pass_current, msg='current uri is incorrect') + self.assertTrue(pass_default, msg="default uri is incorrect") + self.assertTrue(pass_current, msg="current uri is incorrect") shutil.rmtree(str(Path(uri_path.strip("file:")).resolve())) From 19d93744f3678881a29e110a3b2b4ea48b070fee Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Fri, 5 Mar 2021 07:15:25 +0000 Subject: [PATCH 053/134] Add set_log_basic_config function support re-directing log stream --- qlib/config.py | 8 +++---- qlib/log.py | 31 +++++++++++++++++++++------ qlib/workflow/recorder.py | 45 +++++++++++++++++++++++++++++++++------ 3 files changed, 66 insertions(+), 18 deletions(-) diff --git a/qlib/config.py b/qlib/config.py index 52b05568d..75ab0fa3e 100644 --- a/qlib/config.py +++ b/qlib/config.py @@ -105,7 +105,7 @@ _default_config = { "redis_port": 6379, "redis_task_db": 1, # This value can be reset via qlib.init - "logging_level": "INFO", + "logging_level": logging.INFO, # Global configuration of qlib log # logging_level can control the logging level more finely "logging_config": { @@ -124,12 +124,12 @@ _default_config = { "handlers": { "console": { "class": "logging.StreamHandler", - "level": "DEBUG", + "level": logging.DEBUG, "formatter": "logger_format", "filters": ["field_not_found"], } }, - "loggers": {"qlib": {"level": "DEBUG", "handlers": ["console"]}}, + "loggers": {"qlib": {"level": logging.DEBUG, "handlers": ["console"]}}, }, # Defatult config for experiment manager "exp_manager": { @@ -185,7 +185,7 @@ MODE_CONF = { # The nfs should be auto-mounted by qlib on other # serversS(such as PAI) [auto_mount:True] "timeout": 100, - "logging_level": "INFO", + "logging_level": logging.INFO, "region": REG_CN, ## Custom Operator "custom_ops": [], diff --git a/qlib/log.py b/qlib/log.py index 6553dcb11..78f12eb09 100644 --- a/qlib/log.py +++ b/qlib/log.py @@ -3,8 +3,7 @@ import logging -import logging.handlers -import os +from typing import Optional, Text, Dict, Any import re from logging import config as logging_config from time import time @@ -13,16 +12,13 @@ from contextlib import contextmanager from .config import C -def get_module_logger(module_name, level=None): +def get_module_logger(module_name, level: Optional[int] = None): """ Get a logger for a specific module. :param module_name: str Logic module name. :param level: int - :param sh_level: int - Stream handler log level. - :param log_format: str :return: Logger Logger object. """ @@ -103,7 +99,7 @@ class TimeInspector: cls.log_cost_time(info=f"{name} Done") -def set_log_with_config(log_config: dict): +def set_log_with_config(log_config: Dict[Text, Any]): """set log with config :param log_config: @@ -112,6 +108,27 @@ def set_log_with_config(log_config: dict): logging_config.dictConfig(log_config) +def set_log_basic_config(filename: Optional[Text] = None, format: Optional[Text] = None, level: Optional[int] = None): + """ + Set the basic configuration for the logging system. + See details at https://docs.python.org/3/library/logging.html#logging.basicConfig + + :param filename: str or None + The path to save the logs. + :param format: the logging format + :param level: int + :return: Logger + Logger object. + """ + if level is None: + level = C.logging_level + + if format is None: + format = C.logging_config["formatters"]["logger_format"]["format"] + + logging.basicConfig(filename=filename, format=format, level=level) + + class LogFilter(logging.Filter): def __init__(self, param=None): self.param = param diff --git a/qlib/workflow/recorder.py b/qlib/workflow/recorder.py index 31077176d..519b69710 100644 --- a/qlib/workflow/recorder.py +++ b/qlib/workflow/recorder.py @@ -11,7 +11,7 @@ from ..log import get_module_logger logger = get_module_logger("workflow", "INFO") -class Recorder(object): +class Recorder: """ This is the `Recorder` class for logging the experiments. The API is designed similar to mlflow. (The link: https://mlflow.org/docs/latest/python_api/mlflow.html) @@ -201,7 +201,7 @@ class MLflowRecorder(Recorder): def __init__(self, experiment_id, uri, name=None, mlflow_run=None): super(MLflowRecorder, self).__init__(experiment_id, name) self._uri = uri - self.artifact_uri = None + self._artifact_uri = None self.client = mlflow.tracking.MlflowClient(tracking_uri=self._uri) # construct from mlflow run if mlflow_run is not None: @@ -220,14 +220,45 @@ class MLflowRecorder(Recorder): else None ) + def __repr__(self): + name = self.__class__.__name__ + space_length = len(name) + 1 + return "{name}(info={info},\n{space}uri={uri},\n{space}artifact_uri={artifact_uri},\n{space}client={client})".format( + name=name, + space=" " * space_length, + info=self.info, + uri=self.uri, + artifact_uri=self.artifact_uri, + client=self.client, + ) + + @property + def uri(self): + return self._uri + + @property + def artifact_uri(self): + return self._artifact_uri + + @property + def root_uri(self): + start_str = "file:" + if self.artifact_uri is not None: + xpath = self.artifact_uri.strip(start_str) + return (Path(xpath) / "..").resolve() + else: + raise Exception( + "Please make sure the recorder has been created and started properly before getting artifact uri." + ) + def start_run(self): # set the tracking uri - mlflow.set_tracking_uri(self._uri) + mlflow.set_tracking_uri(self.uri) # start the run run = mlflow.start_run(self.id, self.experiment_id, self.name) # save the run id and artifact_uri self.id = run.info.run_id - self.artifact_uri = run.info.artifact_uri + self._artifact_uri = run.info.artifact_uri self.start_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S") self.status = Recorder.STATUS_R logger.info(f"Recorder {self.id} starts running under Experiment {self.experiment_id} ...") @@ -247,7 +278,7 @@ class MLflowRecorder(Recorder): self.status = status def save_objects(self, local_path=None, artifact_path=None, **kwargs): - assert self._uri is not None, "Please start the experiment and recorder first before using recorder directly." + assert self.uri is not None, "Please start the experiment and recorder first before using recorder directly." if local_path is not None: self.client.log_artifacts(self.id, local_path, artifact_path) else: @@ -259,7 +290,7 @@ class MLflowRecorder(Recorder): shutil.rmtree(temp_dir) def load_object(self, name): - assert self._uri is not None, "Please start the experiment and recorder first before using recorder directly." + assert self.uri is not None, "Please start the experiment and recorder first before using recorder directly." path = self.client.download_artifacts(self.id, name) with Path(path).open("rb") as f: return pickle.load(f) @@ -289,7 +320,7 @@ class MLflowRecorder(Recorder): ) def list_artifacts(self, artifact_path=None): - assert self._uri is not None, "Please start the experiment and recorder first before using recorder directly." + assert self.uri is not None, "Please start the experiment and recorder first before using recorder directly." artifacts = self.client.list_artifacts(self.id, artifact_path) return [art.path for art in artifacts] From 131f0e2e676fb6368d9e76d05be4d17a3512741b Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Fri, 5 Mar 2021 12:07:43 +0000 Subject: [PATCH 054/134] Add count_parameters for pytorch models in contrib --- qlib/contrib/model/pytorch_alstm.py | 10 +++++++--- qlib/contrib/model/pytorch_alstm_ts.py | 6 +++--- qlib/contrib/model/pytorch_gru.py | 6 +++++- qlib/contrib/model/pytorch_gru_ts.py | 8 ++++++-- qlib/contrib/model/pytorch_lstm.py | 2 +- qlib/contrib/model/pytorch_lstm_ts.py | 2 +- qlib/contrib/model/pytorch_nn.py | 6 +++--- qlib/contrib/model/pytorch_utils.py | 21 +++++++++++++++++++++ 8 files changed, 47 insertions(+), 14 deletions(-) create mode 100644 qlib/contrib/model/pytorch_utils.py diff --git a/qlib/contrib/model/pytorch_alstm.py b/qlib/contrib/model/pytorch_alstm.py index bbbb61851..6df996e11 100644 --- a/qlib/contrib/model/pytorch_alstm.py +++ b/qlib/contrib/model/pytorch_alstm.py @@ -23,6 +23,7 @@ import torch import torch.nn as nn import torch.optim as optim +from .pytorch_utils import count_parameters from ...model.base import Model from ...data.dataset import DatasetH from ...data.dataset.handler import DataHandlerLP @@ -39,8 +40,8 @@ class ALSTM(Model): the evaluate metric used in early stop optimizer : str optimizer name - GPU : str - the GPU ID(s) used for training + GPU : int + the GPU ID used for training """ def __init__( @@ -76,7 +77,7 @@ class ALSTM(Model): self.early_stop = early_stop self.optimizer = optimizer.lower() self.loss = loss - self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu") + self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu") self.use_gpu = torch.cuda.is_available() self.seed = seed @@ -123,6 +124,9 @@ class ALSTM(Model): num_layers=self.num_layers, dropout=self.dropout, ) + self.logger.info("model:\n{:}".format(self.ALSTM_model)) + self.logger.info("model size: {:.4f} MB".format(count_parameters(self.ALSTM_model))) + if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.ALSTM_model.parameters(), lr=self.lr) elif optimizer.lower() == "gd": diff --git a/qlib/contrib/model/pytorch_alstm_ts.py b/qlib/contrib/model/pytorch_alstm_ts.py index 725568de8..c8854e8d3 100644 --- a/qlib/contrib/model/pytorch_alstm_ts.py +++ b/qlib/contrib/model/pytorch_alstm_ts.py @@ -40,8 +40,8 @@ class ALSTM(Model): the evaluate metric used in early stop optimizer : str optimizer name - GPU : str - the GPU ID(s) used for training + GPU : int + the GPU ID used for training """ def __init__( @@ -78,7 +78,7 @@ class ALSTM(Model): self.early_stop = early_stop self.optimizer = optimizer.lower() self.loss = loss - self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu") + self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu") self.n_jobs = n_jobs self.use_gpu = torch.cuda.is_available() self.seed = seed diff --git a/qlib/contrib/model/pytorch_gru.py b/qlib/contrib/model/pytorch_gru.py index 84f863b9f..720e6b4f1 100755 --- a/qlib/contrib/model/pytorch_gru.py +++ b/qlib/contrib/model/pytorch_gru.py @@ -23,6 +23,7 @@ import torch import torch.nn as nn import torch.optim as optim +from .pytorch_utils import count_parameters from ...model.base import Model from ...data.dataset import DatasetH from ...data.dataset.handler import DataHandlerLP @@ -76,7 +77,7 @@ class GRU(Model): self.early_stop = early_stop self.optimizer = optimizer.lower() self.loss = loss - self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu") + self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu") self.use_gpu = torch.cuda.is_available() self.seed = seed @@ -123,6 +124,9 @@ class GRU(Model): num_layers=self.num_layers, dropout=self.dropout, ) + self.logger.info("model:\n{:}".format(self.gru_model)) + self.logger.info("model size: {:.4f} MB".format(count_parameters(self.gru_model))) + if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.gru_model.parameters(), lr=self.lr) elif optimizer.lower() == "gd": diff --git a/qlib/contrib/model/pytorch_gru_ts.py b/qlib/contrib/model/pytorch_gru_ts.py index bb6618b85..cbf1c1add 100755 --- a/qlib/contrib/model/pytorch_gru_ts.py +++ b/qlib/contrib/model/pytorch_gru_ts.py @@ -24,6 +24,7 @@ import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader +from .pytorch_utils import count_parameters from ...model.base import Model from ...data.dataset import DatasetH, TSDatasetH from ...data.dataset.handler import DataHandlerLP @@ -78,7 +79,7 @@ class GRU(Model): self.early_stop = early_stop self.optimizer = optimizer.lower() self.loss = loss - self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu") + self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu") self.n_jobs = n_jobs self.use_gpu = torch.cuda.is_available() self.seed = seed @@ -127,7 +128,10 @@ class GRU(Model): hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, - ).to(self.device) + ) + self.logger.info("model:\n{:}".format(self.gru_model)) + self.logger.info("model size: {:.4f} MB".format(count_parameters(self.gru_model))) + if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.GRU_model.parameters(), lr=self.lr) elif optimizer.lower() == "gd": diff --git a/qlib/contrib/model/pytorch_lstm.py b/qlib/contrib/model/pytorch_lstm.py index 163d500ec..61e372425 100755 --- a/qlib/contrib/model/pytorch_lstm.py +++ b/qlib/contrib/model/pytorch_lstm.py @@ -76,7 +76,7 @@ class LSTM(Model): self.early_stop = early_stop self.optimizer = optimizer.lower() self.loss = loss - self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu") + self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu") self.use_gpu = torch.cuda.is_available() self.seed = seed diff --git a/qlib/contrib/model/pytorch_lstm_ts.py b/qlib/contrib/model/pytorch_lstm_ts.py index cf4f8fb9f..44d136676 100755 --- a/qlib/contrib/model/pytorch_lstm_ts.py +++ b/qlib/contrib/model/pytorch_lstm_ts.py @@ -78,7 +78,7 @@ class LSTM(Model): self.early_stop = early_stop self.optimizer = optimizer.lower() self.loss = loss - self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu") + self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu") self.n_jobs = n_jobs self.use_gpu = torch.cuda.is_available() self.seed = seed diff --git a/qlib/contrib/model/pytorch_nn.py b/qlib/contrib/model/pytorch_nn.py index 16fcea9ff..f8b0b7748 100644 --- a/qlib/contrib/model/pytorch_nn.py +++ b/qlib/contrib/model/pytorch_nn.py @@ -42,8 +42,8 @@ class DNNModelPytorch(Model): learning rate decay steps optimizer : str optimizer name - GPU : str - the GPU ID(s) used for training + GPU : int + the GPU ID used for training """ def __init__( @@ -80,7 +80,7 @@ class DNNModelPytorch(Model): self.lr_decay_steps = lr_decay_steps self.optimizer = optimizer.lower() self.loss_type = loss - self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu") + self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu") self.use_GPU = torch.cuda.is_available() self.seed = seed self.weight_decay = weight_decay diff --git a/qlib/contrib/model/pytorch_utils.py b/qlib/contrib/model/pytorch_utils.py new file mode 100644 index 000000000..532969eb5 --- /dev/null +++ b/qlib/contrib/model/pytorch_utils.py @@ -0,0 +1,21 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +import numpy as np +import torch.nn as nn + + +def count_parameters(model_or_parameters, unit="mb"): + if isinstance(model_or_parameters, nn.Module): + counts = np.sum(np.prod(v.size()) for v in model_or_parameters.parameters()) + else: + counts = np.sum(np.prod(v.size()) for v in model_or_parameters) + if unit.lower() == "mb": + counts /= 1e6 + elif unit.lower() == "kb": + counts /= 1e3 + elif unit.lower() == "gb": + counts /= 1e9 + elif unit is not None: + raise ValueError("Unknow unit: {:}".format(unit)) + return counts From 49697b1f1568608e3077450b72fe3ed5b92ec1e5 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Fri, 5 Mar 2021 12:46:41 +0000 Subject: [PATCH 055/134] Show model size for pytorch models --- qlib/contrib/model/pytorch_alstm_ts.py | 6 +++++- qlib/contrib/model/pytorch_gats.py | 10 +++++++--- qlib/contrib/model/pytorch_gats_ts.py | 10 +++++++--- qlib/contrib/model/pytorch_nn.py | 4 ++++ qlib/contrib/model/pytorch_sfm.py | 12 ++++++++---- qlib/contrib/model/pytorch_tabnet.py | 7 +++++-- 6 files changed, 36 insertions(+), 13 deletions(-) diff --git a/qlib/contrib/model/pytorch_alstm_ts.py b/qlib/contrib/model/pytorch_alstm_ts.py index c8854e8d3..933905439 100644 --- a/qlib/contrib/model/pytorch_alstm_ts.py +++ b/qlib/contrib/model/pytorch_alstm_ts.py @@ -24,6 +24,7 @@ import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader +from .pytorch_utils import count_parameters from ...model.base import Model from ...data.dataset import DatasetH, TSDatasetH from ...data.dataset.handler import DataHandlerLP @@ -127,7 +128,10 @@ class ALSTM(Model): hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, - ).to(self.device) + ) + self.logger.info("model:\n{:}".format(self.ALSTM_model)) + self.logger.info("model size: {:.4f} MB".format(count_parameters(self.ALSTM_model))) + if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.ALSTM_model.parameters(), lr=self.lr) elif optimizer.lower() == "gd": diff --git a/qlib/contrib/model/pytorch_gats.py b/qlib/contrib/model/pytorch_gats.py index 07048e1bc..061feaa84 100644 --- a/qlib/contrib/model/pytorch_gats.py +++ b/qlib/contrib/model/pytorch_gats.py @@ -22,6 +22,7 @@ import torch import torch.nn as nn import torch.optim as optim +from .pytorch_utils import count_parameters from ...model.base import Model from ...data.dataset import DatasetH from ...data.dataset.handler import DataHandlerLP @@ -42,8 +43,8 @@ class GATs(Model): the evaluate metric used in early stop optimizer : str optimizer name - GPU : str - the GPU ID(s) used for training + GPU : int + the GPU ID used for training """ def __init__( @@ -83,7 +84,7 @@ class GATs(Model): self.base_model = base_model self.with_pretrain = with_pretrain self.model_path = model_path - self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu") + self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu") self.use_gpu = torch.cuda.is_available() self.seed = seed @@ -135,6 +136,9 @@ class GATs(Model): dropout=self.dropout, base_model=self.base_model, ) + self.logger.info("model:\n{:}".format(self.GAT_model)) + self.logger.info("model size: {:.4f} MB".format(count_parameters(self.GAT_model))) + if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.GAT_model.parameters(), lr=self.lr) elif optimizer.lower() == "gd": diff --git a/qlib/contrib/model/pytorch_gats_ts.py b/qlib/contrib/model/pytorch_gats_ts.py index 1e94f56e4..b179a6df6 100644 --- a/qlib/contrib/model/pytorch_gats_ts.py +++ b/qlib/contrib/model/pytorch_gats_ts.py @@ -24,6 +24,7 @@ import torch.optim as optim from torch.utils.data import DataLoader from torch.utils.data import Sampler +from .pytorch_utils import count_parameters from ...model.base import Model from ...data.dataset import DatasetH from ...data.dataset.handler import DataHandlerLP @@ -62,8 +63,8 @@ class GATs(Model): the evaluate metric used in early stop optimizer : str optimizer name - GPU : str - the GPU ID(s) used for training + GPU : int + the GPU ID used for training """ def __init__( @@ -104,7 +105,7 @@ class GATs(Model): self.base_model = base_model self.with_pretrain = with_pretrain self.model_path = model_path - self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu") + self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu") self.n_jobs = n_jobs self.use_gpu = torch.cuda.is_available() self.seed = seed @@ -157,6 +158,9 @@ class GATs(Model): dropout=self.dropout, base_model=self.base_model, ) + self.logger.info("model:\n{:}".format(self.GAT_model)) + self.logger.info("model size: {:.4f} MB".format(count_parameters(self.GAT_model))) + if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.GAT_model.parameters(), lr=self.lr) elif optimizer.lower() == "gd": diff --git a/qlib/contrib/model/pytorch_nn.py b/qlib/contrib/model/pytorch_nn.py index f8b0b7748..a51481c85 100644 --- a/qlib/contrib/model/pytorch_nn.py +++ b/qlib/contrib/model/pytorch_nn.py @@ -15,6 +15,7 @@ import torch import torch.nn as nn import torch.optim as optim +from .pytorch_utils import count_parameters from ...model.base import Model from ...data.dataset import DatasetH from ...data.dataset.handler import DataHandlerLP @@ -129,6 +130,9 @@ class DNNModelPytorch(Model): self._scorer = mean_squared_error if loss == "mse" else roc_auc_score self.dnn_model = Net(input_dim, output_dim, layers, loss=self.loss_type) + self.logger.info("model:\n{:}".format(self.dnn_model)) + self.logger.info("model size: {:.4f} MB".format(count_parameters(self.dnn_model))) + if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.dnn_model.parameters(), lr=self.lr, weight_decay=self.weight_decay) elif optimizer.lower() == "gd": diff --git a/qlib/contrib/model/pytorch_sfm.py b/qlib/contrib/model/pytorch_sfm.py index d5169e6c7..55058797a 100644 --- a/qlib/contrib/model/pytorch_sfm.py +++ b/qlib/contrib/model/pytorch_sfm.py @@ -23,6 +23,7 @@ import torch.nn as nn import torch.nn.init as init import torch.optim as optim +from .pytorch_utils import count_parameters from ...model.base import Model from ...data.dataset import DatasetH from ...data.dataset.handler import DataHandlerLP @@ -196,8 +197,8 @@ class SFM(Model): learning rate optimizer : str optimizer name - GPU : str - the GPU ID(s) used for training + GPU : int + the GPU ID used for training """ def __init__( @@ -216,7 +217,7 @@ class SFM(Model): eval_steps=5, loss="mse", optimizer="gd", - GPU="0", + GPU=0, seed=None, **kwargs ): @@ -239,7 +240,7 @@ class SFM(Model): self.eval_steps = eval_steps self.optimizer = optimizer.lower() self.loss = loss - self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu") + self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu") self.use_gpu = torch.cuda.is_available() self.seed = seed @@ -295,6 +296,9 @@ class SFM(Model): dropout_U=self.dropout_U, device=self.device, ) + self.logger.info("model:\n{:}".format(self.sfm_model)) + self.logger.info("model size: {:.4f} MB".format(count_parameters(self.sfm_model))) + if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.sfm_model.parameters(), lr=self.lr) elif optimizer.lower() == "gd": diff --git a/qlib/contrib/model/pytorch_tabnet.py b/qlib/contrib/model/pytorch_tabnet.py index 62e32d701..ff56b0561 100644 --- a/qlib/contrib/model/pytorch_tabnet.py +++ b/qlib/contrib/model/pytorch_tabnet.py @@ -23,6 +23,7 @@ import torch.optim as optim import torch.nn.functional as F from torch.autograd import Function +from .pytorch_utils import count_parameters from ...model.base import Model from ...data.dataset import DatasetH from ...data.dataset.handler import DataHandlerLP @@ -49,7 +50,7 @@ class TabnetModel(Model): loss="mse", metric="", early_stop=20, - GPU="1", + GPU=0, pretrain_loss="custom", ps=0.3, lr=0.01, @@ -75,7 +76,7 @@ class TabnetModel(Model): self.n_epochs = n_epochs self.logger = get_module_logger("TabNet") self.pretrain_n_epochs = pretrain_n_epochs - self.device = "cuda:%s" % (GPU) if torch.cuda.is_available() else "cpu" + self.device = "cuda:%s" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu" self.loss = loss self.metric = metric self.early_stop = early_stop @@ -98,6 +99,8 @@ class TabnetModel(Model): self.tabnet_decoder = TabNet_Decoder(self.out_dim, self.d_feat, n_shared, n_ind, vbs, n_steps, self.device).to( self.device ) + self.logger.info("model:\n{:}\n{:}".format(self.tabnet_model, self.tabnet_decoder)) + self.logger.info("model size: {:.4f} MB".format(count_parameters(self.tabnet_model) + count_parameters(self.tabnet_decoder))) if optimizer.lower() == "adam": self.pretrain_optimizer = optim.Adam( From f277a66582eb9f8e65c64380a885105e236929e0 Mon Sep 17 00:00:00 2001 From: shubhendra Date: Sat, 6 Mar 2021 13:01:00 +0530 Subject: [PATCH 056/134] Add .deepsource.toml Signed-off-by: shubhendra --- .deepsource.toml | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 .deepsource.toml diff --git a/.deepsource.toml b/.deepsource.toml new file mode 100644 index 000000000..773c9e023 --- /dev/null +++ b/.deepsource.toml @@ -0,0 +1,12 @@ +version = 1 + +test_patterns = ["tests/test_*.py"] + +exclude_patterns = ["examples/**"] + +[[analyzers]] +name = "python" +enabled = true + + [analyzers.meta] + runtime_version = "3.x.x" From 07eef183372e7dda39f7b427ff5cf22427a31bc8 Mon Sep 17 00:00:00 2001 From: shubhendra Date: Sat, 6 Mar 2021 13:01:01 +0530 Subject: [PATCH 057/134] Remove length check in favour of truthiness of the object Signed-off-by: shubhendra --- qlib/contrib/backtest/exchange.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qlib/contrib/backtest/exchange.py b/qlib/contrib/backtest/exchange.py index 178950eeb..5a8b8b2b6 100644 --- a/qlib/contrib/backtest/exchange.py +++ b/qlib/contrib/backtest/exchange.py @@ -93,7 +93,7 @@ class Exchange: self.limit_threshold = limit_threshold # TODO: the quote, trade_dates, codes are not necessray. # It is just for performance consideration. - if trade_dates is not None and len(trade_dates): + if trade_dates is not None and trade_dates: start_date, end_date = trade_dates[0], trade_dates[-1] else: self.logger.warning("trade_dates have not been assigned, all dates will be loaded") From 6f034ccb5d289845816fc8c62e0b4a04ef4857fa Mon Sep 17 00:00:00 2001 From: shubhendra Date: Sat, 6 Mar 2021 13:01:02 +0530 Subject: [PATCH 058/134] Remove unnecessary use of comprehension Signed-off-by: shubhendra --- qlib/contrib/online/manager.py | 2 +- qlib/contrib/report/graph.py | 2 +- qlib/workflow/cli.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/qlib/contrib/online/manager.py b/qlib/contrib/online/manager.py index b0be7f076..70b7bad40 100644 --- a/qlib/contrib/online/manager.py +++ b/qlib/contrib/online/manager.py @@ -63,7 +63,7 @@ class UserManager: account_path = self.data_path / user_id strategy_file = self.data_path / user_id / "strategy_{}.pickle".format(user_id) model_file = self.data_path / user_id / "model_{}.pickle".format(user_id) - cur_user_list = [user_id for user_id in self.users] + cur_user_list = list(self.users) if user_id in cur_user_list: raise ValueError("User {} has been loaded".format(user_id)) else: diff --git a/qlib/contrib/report/graph.py b/qlib/contrib/report/graph.py index 70e382fb1..677e767ee 100644 --- a/qlib/contrib/report/graph.py +++ b/qlib/contrib/report/graph.py @@ -161,7 +161,7 @@ class DistplotGraph(BaseGraph): """ _t_df = self._df.dropna() _data_list = [_t_df[_col] for _col in self._name_dict] - _label_list = [_name for _name in self._name_dict.values()] + _label_list = list(self._name_dict.values()) _fig = create_distplot(_data_list, _label_list, show_rug=False, **self._graph_kwargs) return _fig["data"] diff --git a/qlib/workflow/cli.py b/qlib/workflow/cli.py index 6eba96277..879c0aaeb 100644 --- a/qlib/workflow/cli.py +++ b/qlib/workflow/cli.py @@ -16,7 +16,7 @@ def get_path_list(path): if isinstance(path, str): return [path] else: - return [p for p in path] + return list(path) def sys_config(config, config_path): From 5015d218ff726784280dd1913ecd987990a3ea73 Mon Sep 17 00:00:00 2001 From: shubhendra Date: Sat, 6 Mar 2021 13:01:03 +0530 Subject: [PATCH 059/134] Remove methods with unnecessary super delegation. Signed-off-by: shubhendra --- qlib/data/cache.py | 3 --- qlib/data/data.py | 3 --- qlib/data/dataset/__init__.py | 11 ----------- qlib/workflow/expm.py | 3 --- 4 files changed, 20 deletions(-) diff --git a/qlib/data/cache.py b/qlib/data/cache.py index 0174dc63f..2d0b8a7cd 100644 --- a/qlib/data/cache.py +++ b/qlib/data/cache.py @@ -1045,9 +1045,6 @@ class SimpleDatasetCache(DatasetCache): class DatasetURICache(DatasetCache): """Prepared cache mechanism for server.""" - def __init__(self, provider): - super(DatasetURICache, self).__init__(provider) - def _uri(self, instruments, fields, start_time, end_time, freq, disk_cache=1, **kwargs): return hash_args(*self.normalize_uri_args(instruments, fields, freq), disk_cache) diff --git a/qlib/data/data.py b/qlib/data/data.py index 762467da3..000bd1196 100644 --- a/qlib/data/data.py +++ b/qlib/data/data.py @@ -654,9 +654,6 @@ class LocalExpressionProvider(ExpressionProvider): Provide expression data from local data source. """ - def __init__(self): - super().__init__() - def expression(self, instrument, field, start_time=None, end_time=None, freq="day"): expression = self.get_expression_instance(field) start_time = pd.Timestamp(start_time) diff --git a/qlib/data/dataset/__init__.py b/qlib/data/dataset/__init__.py index 8ff8c1210..b87beeeb4 100644 --- a/qlib/data/dataset/__init__.py +++ b/qlib/data/dataset/__init__.py @@ -76,17 +76,6 @@ class DatasetH(Dataset): - The processing is related to data split. """ - def __init__(self, handler: Union[dict, DataHandler], segments: dict): - """ - Parameters - ---------- - handler : Union[dict, DataHandler] - handler will be passed into setup_data. - segments : dict - handler will be passed into setup_data. - """ - super().__init__(handler, segments) - def init(self, handler_kwargs: dict = None, segment_kwargs: dict = None): """ Initialize the DatasetH diff --git a/qlib/workflow/expm.py b/qlib/workflow/expm.py index a50dce7c9..a75c1cf6e 100644 --- a/qlib/workflow/expm.py +++ b/qlib/workflow/expm.py @@ -229,9 +229,6 @@ class MLflowExpManager(ExpManager): Use mlflow to implement ExpManager. """ - def __init__(self, uri, default_exp_name): - super(MLflowExpManager, self).__init__(uri, default_exp_name) - @property def client(self): # Delay the creation of mlflow client in case of creating `mlruns` folder when importing qlib From a62d1a1b360355d26a9e21476a7b76a54f222be1 Mon Sep 17 00:00:00 2001 From: shubhendra Date: Sat, 6 Mar 2021 13:01:04 +0530 Subject: [PATCH 060/134] Use literal syntax to create data structure Signed-off-by: shubhendra --- qlib/contrib/evaluate_portfolio.py | 4 ++-- qlib/utils/__init__.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/qlib/contrib/evaluate_portfolio.py b/qlib/contrib/evaluate_portfolio.py index 04ddd8db0..b3a770e7c 100644 --- a/qlib/contrib/evaluate_portfolio.py +++ b/qlib/contrib/evaluate_portfolio.py @@ -61,7 +61,7 @@ def get_position_value(evaluate_date, position): # load close price for position # position should also consider cash instruments = list(position.keys()) - instruments = list(set(instruments) - set(["cash"])) # filter 'cash' + instruments = list(set(instruments) - {"cash"}) # filter 'cash' fields = ["$close"] close_data_df = D.features( instruments, @@ -80,7 +80,7 @@ def get_position_list_value(positions): instruments = set() for day, position in positions.items(): instruments.update(position.keys()) - instruments = list(set(instruments) - set(["cash"])) # filter 'cash' + instruments = list(set(instruments) - {"cash"}) # filter 'cash' instruments.sort() day_list = list(positions.keys()) day_list.sort() diff --git a/qlib/utils/__init__.py b/qlib/utils/__init__.py index 6640dae2c..3585c5281 100644 --- a/qlib/utils/__init__.py +++ b/qlib/utils/__init__.py @@ -212,7 +212,7 @@ def get_cls_kwargs(config: Union[dict, str], module) -> (type, dict): def init_instance_by_config( - config: Union[str, dict, object], module=None, accept_types: Union[type, Tuple[type]] = tuple([]), **kwargs + config: Union[str, dict, object], module=None, accept_types: Union[type, Tuple[type]] = (), **kwargs ) -> object: """ get initialized instance with config From dc86a6abc5ec9a5423dbfa4e96ad70cb04373699 Mon Sep 17 00:00:00 2001 From: shubhendra Date: Sat, 6 Mar 2021 13:01:05 +0530 Subject: [PATCH 061/134] Refactor unnecessary `else` / `elif` when `if` block has a `continue` statement Signed-off-by: shubhendra --- qlib/contrib/backtest/account.py | 7 +++---- qlib/contrib/backtest/exchange.py | 2 +- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/qlib/contrib/backtest/account.py b/qlib/contrib/backtest/account.py index d8b285019..a614f08b6 100644 --- a/qlib/contrib/backtest/account.py +++ b/qlib/contrib/backtest/account.py @@ -104,10 +104,9 @@ class Account: # if suspend, no new price to be updated, profit is 0 if trader.check_stock_suspended(code, today): continue - else: - today_close = trader.get_close(code, today) - profit += (today_close - self.current.position[code]["price"]) * self.current.position[code]["amount"] - self.current.update_stock_price(stock_id=code, price=today_close) + today_close = trader.get_close(code, today) + profit += (today_close - self.current.position[code]["price"]) * self.current.position[code]["amount"] + self.current.update_stock_price(stock_id=code, price=today_close) self.rtn += profit # update holding day count self.current.add_count_all() diff --git a/qlib/contrib/backtest/exchange.py b/qlib/contrib/backtest/exchange.py index 5a8b8b2b6..cbb3d7932 100644 --- a/qlib/contrib/backtest/exchange.py +++ b/qlib/contrib/backtest/exchange.py @@ -325,7 +325,7 @@ class Exchange: deal_amount = self.get_real_deal_amount(current_amount, target_amount, factor) if deal_amount == 0: continue - elif deal_amount > 0: + if deal_amount > 0: # buy stock buy_order_list.append( Order( From aab5c5b311b47128a62b32500cb7fab7e3ba485f Mon Sep 17 00:00:00 2001 From: shubhendra Date: Sat, 6 Mar 2021 13:01:05 +0530 Subject: [PATCH 062/134] Refactor the comparison involving `not` Signed-off-by: shubhendra --- qlib/contrib/online/operator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qlib/contrib/online/operator.py b/qlib/contrib/online/operator.py index c8b44f578..d2307dad5 100644 --- a/qlib/contrib/online/operator.py +++ b/qlib/contrib/online/operator.py @@ -148,7 +148,7 @@ class Operator: for user_id, user in um.users.items(): dates, trade_exchange = prepare(um, trade_date, user_id, exchange_config) executor = SimulatorExecutor(trade_exchange=trade_exchange) - if not str(dates[0].date()) == str(pred_date.date()): + if str(dates[0].date()) != str(pred_date.date()): raise ValueError( "The account data is not newest! last trading date {}, today {}".format( dates[0].date(), trade_date.date() From 91fd53ab4d0724df73ccf8855ed83b6e1760bb08 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Sat, 6 Mar 2021 05:33:08 -0800 Subject: [PATCH 063/134] Add reset_default_uri func for R and expm --- qlib/workflow/__init__.py | 11 ++++++++++- qlib/workflow/exp.py | 5 ++++- qlib/workflow/expm.py | 4 ++++ 3 files changed, 18 insertions(+), 2 deletions(-) diff --git a/qlib/workflow/__init__.py b/qlib/workflow/__init__.py index 7bff505ce..54297ecd7 100644 --- a/qlib/workflow/__init__.py +++ b/qlib/workflow/__init__.py @@ -2,6 +2,7 @@ # Licensed under the MIT License. from contextlib import contextmanager +from typing import Text, Optional from .expm import MLflowExpManager from .exp import Experiment from .recorder import Recorder @@ -20,7 +21,9 @@ class QlibRecorder: return "{name}(manager={manager})".format(name=self.__class__.__name__, manager=self.exp_manager) @contextmanager - def start(self, experiment_name=None, recorder_name=None, uri=None): + def start( + self, experiment_name: Optional[Text] = None, recorder_name: Optional[Text] = None, uri: Optional[Text] = None + ): """ Method to start an experiment. This method can only be called within a Python's `with` statement. Here is the example code: @@ -282,6 +285,12 @@ class QlibRecorder: """ return self.exp_manager.uri + def reset_default_uri(self, uri: Text): + """ + Method to reset the default uri of current experiment manager. + """ + self.exp_manager.reset_default_uri(uri) + def get_recorder(self, recorder_id=None, recorder_name=None, experiment_name=None): """ Method for retrieving a recorder. diff --git a/qlib/workflow/exp.py b/qlib/workflow/exp.py index 18b0a143d..9cda020c3 100644 --- a/qlib/workflow/exp.py +++ b/qlib/workflow/exp.py @@ -23,7 +23,7 @@ class Experiment: self.active_recorder = None # only one recorder can running each time def __repr__(self): - return "{name}(info={info})".format(name=self.__class__.__name__, info=self.info) + return "{name}(id={id}, info={info})".format(name=self.__class__.__name__, id=self.id, info=self.info) def __str__(self): return str(self.info) @@ -175,6 +175,9 @@ class MLflowExperiment(Experiment): self._default_rec_name = "mlflow_recorder" self._client = mlflow.tracking.MlflowClient(tracking_uri=self._uri) + def __repr__(self): + return "{name}(id={id}, info={info})".format(name=self.__class__.__name__, id=self.id, info=self.info) + def start(self, recorder_name=None): logger.info(f"Experiment {self.id} starts running ...") # set up recorder diff --git a/qlib/workflow/expm.py b/qlib/workflow/expm.py index 4ba72a634..f9a5d0252 100644 --- a/qlib/workflow/expm.py +++ b/qlib/workflow/expm.py @@ -33,6 +33,10 @@ class ExpManager: name=self.__class__.__name__, duri=self._default_uri, curi=self._current_uri ) + def reset_default_uri(self, uri: Text): + self._default_uri = uri + self.set_uri(None) + def start_exp( self, experiment_name: Optional[Text] = None, From 73b7107ee89f890456f62fc222f71b2e9f5ed23e Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Sun, 7 Mar 2021 00:52:27 -0800 Subject: [PATCH 064/134] Remove useless verbose kwarg --- qlib/contrib/model/pytorch_alstm.py | 1 - qlib/contrib/model/pytorch_alstm_ts.py | 1 - qlib/contrib/model/pytorch_gats.py | 1 - qlib/contrib/model/pytorch_gats_ts.py | 1 - qlib/contrib/model/pytorch_gru.py | 1 - qlib/contrib/model/pytorch_gru_ts.py | 1 - qlib/contrib/model/pytorch_lstm.py | 1 - qlib/contrib/model/pytorch_lstm_ts.py | 1 - qlib/contrib/model/pytorch_sfm.py | 1 - qlib/contrib/model/pytorch_tabnet.py | 1 - 10 files changed, 10 deletions(-) diff --git a/qlib/contrib/model/pytorch_alstm.py b/qlib/contrib/model/pytorch_alstm.py index 6df996e11..e8b316795 100644 --- a/qlib/contrib/model/pytorch_alstm.py +++ b/qlib/contrib/model/pytorch_alstm.py @@ -218,7 +218,6 @@ class ALSTM(Model): self, dataset: DatasetH, evals_result=dict(), - verbose=True, save_path=None, ): diff --git a/qlib/contrib/model/pytorch_alstm_ts.py b/qlib/contrib/model/pytorch_alstm_ts.py index 933905439..1907b9d76 100644 --- a/qlib/contrib/model/pytorch_alstm_ts.py +++ b/qlib/contrib/model/pytorch_alstm_ts.py @@ -205,7 +205,6 @@ class ALSTM(Model): self, dataset, evals_result=dict(), - verbose=True, save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) diff --git a/qlib/contrib/model/pytorch_gats.py b/qlib/contrib/model/pytorch_gats.py index 061feaa84..7d3b00232 100644 --- a/qlib/contrib/model/pytorch_gats.py +++ b/qlib/contrib/model/pytorch_gats.py @@ -236,7 +236,6 @@ class GATs(Model): self, dataset: DatasetH, evals_result=dict(), - verbose=True, save_path=None, ): diff --git a/qlib/contrib/model/pytorch_gats_ts.py b/qlib/contrib/model/pytorch_gats_ts.py index b179a6df6..99ae3d4da 100644 --- a/qlib/contrib/model/pytorch_gats_ts.py +++ b/qlib/contrib/model/pytorch_gats_ts.py @@ -249,7 +249,6 @@ class GATs(Model): self, dataset, evals_result=dict(), - verbose=True, save_path=None, ): diff --git a/qlib/contrib/model/pytorch_gru.py b/qlib/contrib/model/pytorch_gru.py index 720e6b4f1..d4dc88452 100755 --- a/qlib/contrib/model/pytorch_gru.py +++ b/qlib/contrib/model/pytorch_gru.py @@ -218,7 +218,6 @@ class GRU(Model): self, dataset: DatasetH, evals_result=dict(), - verbose=True, save_path=None, ): diff --git a/qlib/contrib/model/pytorch_gru_ts.py b/qlib/contrib/model/pytorch_gru_ts.py index cbf1c1add..1f2137c8f 100755 --- a/qlib/contrib/model/pytorch_gru_ts.py +++ b/qlib/contrib/model/pytorch_gru_ts.py @@ -205,7 +205,6 @@ class GRU(Model): self, dataset, evals_result=dict(), - verbose=True, save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) diff --git a/qlib/contrib/model/pytorch_lstm.py b/qlib/contrib/model/pytorch_lstm.py index 61e372425..be8d20a15 100755 --- a/qlib/contrib/model/pytorch_lstm.py +++ b/qlib/contrib/model/pytorch_lstm.py @@ -214,7 +214,6 @@ class LSTM(Model): self, dataset: DatasetH, evals_result=dict(), - verbose=True, save_path=None, ): diff --git a/qlib/contrib/model/pytorch_lstm_ts.py b/qlib/contrib/model/pytorch_lstm_ts.py index 44d136676..3142f15c5 100755 --- a/qlib/contrib/model/pytorch_lstm_ts.py +++ b/qlib/contrib/model/pytorch_lstm_ts.py @@ -201,7 +201,6 @@ class LSTM(Model): self, dataset, evals_result=dict(), - verbose=True, save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) diff --git a/qlib/contrib/model/pytorch_sfm.py b/qlib/contrib/model/pytorch_sfm.py index 55058797a..40b991c9c 100644 --- a/qlib/contrib/model/pytorch_sfm.py +++ b/qlib/contrib/model/pytorch_sfm.py @@ -369,7 +369,6 @@ class SFM(Model): self, dataset: DatasetH, evals_result=dict(), - verbose=True, save_path=None, ): diff --git a/qlib/contrib/model/pytorch_tabnet.py b/qlib/contrib/model/pytorch_tabnet.py index ff56b0561..7e47f58dc 100644 --- a/qlib/contrib/model/pytorch_tabnet.py +++ b/qlib/contrib/model/pytorch_tabnet.py @@ -162,7 +162,6 @@ class TabnetModel(Model): self, dataset: DatasetH, evals_result=dict(), - verbose=True, save_path=None, ): if self.pretrain: From 11412727ef9089863b88f4d58b332513350cb115 Mon Sep 17 00:00:00 2001 From: wangershi Date: Sun, 7 Mar 2021 18:51:38 +0800 Subject: [PATCH 065/134] add normalizer --- scripts/data_collector/fund/README.md | 4 +- scripts/data_collector/fund/collector.py | 171 ++++++++++++++++++++++- scripts/data_collector/utils.py | 43 ++++-- 3 files changed, 200 insertions(+), 18 deletions(-) diff --git a/scripts/data_collector/fund/README.md b/scripts/data_collector/fund/README.md index bcbbbcba7..c729b7eaa 100644 --- a/scripts/data_collector/fund/README.md +++ b/scripts/data_collector/fund/README.md @@ -20,10 +20,12 @@ pip install -r requirements.txt # download from eastmoney.com python collector.py download_data --source_dir ~/.qlib/fund_data/source/cn_1d --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d +# normalize +python collector.py normalize_data --source_dir ~/.qlib/fund_data/source/cn_1d --normalize_dir ~/.qlib/fund_data/source/cn_1d_nor --region CN --interval 1d --date_field_name FSRQ # dump data cd qlib/scripts -python dump_bin.py dump_all --csv_path ~/.qlib/fund_data/source/cn_1d --qlib_dir ~/.qlib/qlib_data/cn_fund_data --freq day --date_field_name FSRQ --include_fields DWJZ,LJJZ +python dump_bin.py dump_all --csv_path ~/.qlib/fund_data/source/cn_1d_nor --qlib_dir ~/.qlib/qlib_data/cn_fund_data --freq day --date_field_name FSRQ --include_fields DWJZ,LJJZ ``` diff --git a/scripts/data_collector/fund/collector.py b/scripts/data_collector/fund/collector.py index a2b7089a1..795d8848e 100644 --- a/scripts/data_collector/fund/collector.py +++ b/scripts/data_collector/fund/collector.py @@ -23,7 +23,7 @@ from dateutil.tz import tzlocal CUR_DIR = Path(__file__).resolve().parent sys.path.append(str(CUR_DIR.parent.parent)) -from data_collector.utils import get_en_fund_symbols +from data_collector.utils import get_calendar_list, get_en_fund_symbols INDEX_BENCH_URL = "http://api.fund.eastmoney.com/f10/lsjz?callback=jQuery_&fundCode={index_code}&pageIndex=1&pageSize={numberOfHistoricalDaysToCrawl}&startDate={startDate}&endDate={endDate}" REGION_CN = "CN" @@ -302,14 +302,149 @@ class FundCollectorCN1d(FundollectorCN): return 252 / 4 +class FundNormalize: + COLUMNS = ["open", "close", "high", "low", "volume"] + DAILY_FORMAT = "%Y-%m-%d" + + def __init__( + self, + date_field_name: str = "date", + symbol_field_name: str = "symbol", + ): + """ + + Parameters + ---------- + date_field_name: str + date field name, default is date + symbol_field_name: str + symbol field name, default is symbol + """ + self._date_field_name = date_field_name + self._symbol_field_name = symbol_field_name + + self._calendar_list = self._get_calendar_list() + print (self._calendar_list) + + @staticmethod + def normalize_fund( + df: pd.DataFrame, + calendar_list: list = None, + date_field_name: str = "date", + symbol_field_name: str = "symbol", + ): + if df.empty: + return df + df = df.copy() + df.set_index(date_field_name, inplace=True) + df.index = pd.to_datetime(df.index) + df = df[~df.index.duplicated(keep="first")] + if calendar_list is not None: + df = df.reindex( + pd.DataFrame(index=calendar_list) + .loc[ + pd.Timestamp(df.index.min()).date() : pd.Timestamp(df.index.max()).date() + + pd.Timedelta(hours=23, minutes=59) + ] + .index + ) + df.sort_index(inplace=True) + + df.index.names = [date_field_name] + return df.reset_index() + + def normalize(self, df: pd.DataFrame) -> pd.DataFrame: + # normalize + df = self.normalize_fund(df, self._calendar_list, self._date_field_name, self._symbol_field_name) + return df + + @abc.abstractmethod + def _get_calendar_list(self): + """Get benchmark calendar""" + raise NotImplementedError("") + + +class FundNormalize1d(FundNormalize, ABC): + DAILY_FORMAT = "%Y-%m-%d" + + def normalize(self, df: pd.DataFrame) -> pd.DataFrame: + df = super(FundNormalize, self).normalize(df) + return df + + +class FundNormalizeCN: + def _get_calendar_list(self): + return get_calendar_list("ALL") + + +class FundNormalizeCN1d(FundNormalizeCN, FundNormalize1d): + pass + + +class Normalize: + def __init__( + self, + source_dir: [str, Path], + target_dir: [str, Path], + normalize_class: Type[FundNormalize], + max_workers: int = 16, + date_field_name: str = "date", + symbol_field_name: str = "symbol", + ): + """ + + Parameters + ---------- + source_dir: str or Path + The directory where the raw data collected from the Internet is saved + target_dir: str or Path + Directory for normalize data + normalize_class: Type[FundNormalize] + normalize class + max_workers: int + Concurrent number, default is 16 + date_field_name: str + date field name, default is date + symbol_field_name: str + symbol field name, default is symbol + """ + if not (source_dir and target_dir): + raise ValueError("source_dir and target_dir cannot be None") + self._source_dir = Path(source_dir).expanduser() + self._target_dir = Path(target_dir).expanduser() + self._target_dir.mkdir(parents=True, exist_ok=True) + + self._max_workers = max_workers + + self._normalize_obj = normalize_class(date_field_name=date_field_name, symbol_field_name=symbol_field_name) + + def _executor(self, file_path: Path): + file_path = Path(file_path) + df = pd.read_csv(file_path) + df = self._normalize_obj.normalize(df) + if not df.empty: + df.to_csv(self._target_dir.joinpath(file_path.name), index=False) + + def normalize(self): + logger.info("normalize data......") + + with ProcessPoolExecutor(max_workers=self._max_workers) as worker: + file_list = list(self._source_dir.glob("*.csv")) + with tqdm(total=len(file_list)) as p_bar: + for _ in worker.map(self._executor, file_list): + p_bar.update() + + class Run: - def __init__(self, source_dir=None, max_workers=4, region=REGION_CN): + def __init__(self, source_dir=None, normalize_dir=None, max_workers=4, region=REGION_CN): """ Parameters ---------- source_dir: str The directory where the raw data collected from the Internet is saved, default "Path(__file__).parent/source" + normalize_dir: str + Directory for normalize data, default "Path(__file__).parent/normalize" max_workers: int Concurrent number, default is 4 region: str @@ -320,6 +455,11 @@ class Run: self.source_dir = Path(source_dir).expanduser().resolve() self.source_dir.mkdir(parents=True, exist_ok=True) + if normalize_dir is None: + normalize_dir = CUR_DIR.joinpath("normalize") + self.normalize_dir = Path(normalize_dir).expanduser().resolve() + self.normalize_dir.mkdir(parents=True, exist_ok=True) + self._cur_module = importlib.import_module("collector") self.max_workers = max_workers self.region = region @@ -372,6 +512,33 @@ class Run: limit_nums=limit_nums, ).collector_data() + def normalize_data(self, interval: str = "1d", date_field_name: str = "date", symbol_field_name: str = "symbol"): + """normalize data + + Parameters + ---------- + interval: str + freq, value from [1d], default 1d + date_field_name: str + date field name, default date + symbol_field_name: str + symbol field name, default symbol + + Examples + --------- + $ python collector.py normalize_data --source_dir ~/.qlib/fund_data/source/cn_1d --normalize_dir ~/.qlib/fund_data/source/cn_1d_nor --region CN --interval 1d --date_field_name FSRQ + """ + _class = getattr(self._cur_module, f"FundNormalize{self.region.upper()}{interval}") + fc = Normalize( + source_dir=self.source_dir, + target_dir=self.normalize_dir, + normalize_class=_class, + max_workers=self.max_workers, + date_field_name=date_field_name, + symbol_field_name=symbol_field_name, + ) + fc.normalize() + if __name__ == "__main__": fire.Fire(Run) diff --git a/scripts/data_collector/utils.py b/scripts/data_collector/utils.py index 1a08c514f..56d010974 100644 --- a/scripts/data_collector/utils.py +++ b/scripts/data_collector/utils.py @@ -98,14 +98,14 @@ def get_calendar_list(bench_code="CSI300") -> list: return calendar -def return_date_list(source_dir, date_field_name, file_path): - df = pd.read_csv(Path(source_dir).joinpath(file_path), sep=",", index_col=0) - - return df[date_field_name].to_list() +def return_date_list(source_dir, date_field_name: str, file_path: Path): + file_path = Path(file_path) + date_list = pd.read_csv(Path(source_dir).joinpath(file_path), sep=",", index_col=0)[date_field_name].to_list() + return sorted(map(lambda x: pd.Timestamp(x), date_list)) def get_calendar_list_by_ratio( - source_dir: [str, Path], date_field_name: str = "date", threshold: float = 0.5, max_workers: int = 16 + source_dir: [str, Path], date_field_name: str = "date", threshold: float = 0.5, minimum_count: int = 10, max_workers: int = 16 ) -> list: """get calendar list by selecting the date when few funds trade in this day @@ -117,6 +117,8 @@ def get_calendar_list_by_ratio( date field name, default is date threshold: float threshold to exclude some days when few funds trade in this day, default 0.5 + minimum_count: int + minimum count of funds should trade in one day max_workers: int Concurrent number, default is 16 @@ -126,25 +128,36 @@ def get_calendar_list_by_ratio( """ logger.info(f"get calendar list from {source_dir} by threshold = {threshold}......") - _number_all_funds = len(os.listdir(source_dir)) + source_dir = Path(source_dir).expanduser() + file_list = list(source_dir.glob("*.csv")) - _list_all_date = dict() + _number_all_funds = len(file_list) + logger.info(f"count how many funds trade in this day......") + _dict_count_trade = dict() # dict{date:count} _fun = partial(return_date_list, source_dir, date_field_name) - with tqdm(total=_number_all_funds) as p_bar: with ProcessPoolExecutor(max_workers=max_workers) as executor: - for date_list in executor.map(_fun, os.listdir(source_dir)): + for date_list in executor.map(_fun, file_list[:_number_all_funds]): for date in date_list: - if date in _list_all_date.keys(): - _list_all_date[date] += 1 - else: - _list_all_date[date] = 0 + if date not in _dict_count_trade.keys(): + _dict_count_trade[date] = 0 + + _dict_count_trade[date] += 1 p_bar.update() + + logger.info(f"count how many funds have founded in this day......") + _dict_count_founding = {date:_number_all_funds for date in _dict_count_trade.keys()} # dict{date:count} + with tqdm(total=_number_all_funds) as p_bar: + with ProcessPoolExecutor(max_workers=max_workers) as executor: + for date_list in executor.map(_fun, file_list[:_number_all_funds]): + oldest_date = sorted(date_list)[0] # this fund haven't found before this day + for date in _dict_count_founding.keys(): + if date < oldest_date: + _dict_count_founding[date] -= 1 - _threshold_number = int(_number_all_funds * threshold) - calendar = [date for date in _list_all_date if _list_all_date[date] >= _threshold_number] + calendar = [date for date in _dict_count_trade if _dict_count_trade[date] >= max(int(_dict_count_founding[date] * threshold), minimum_count)] return calendar From d13c9ae01869a31f123285a792b674694f844370 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Sun, 7 Mar 2021 11:25:53 +0000 Subject: [PATCH 066/134] Avoid dividing zero in model.double_ensemble --- qlib/contrib/model/double_ensemble.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qlib/contrib/model/double_ensemble.py b/qlib/contrib/model/double_ensemble.py index a340489c2..541f74e99 100644 --- a/qlib/contrib/model/double_ensemble.py +++ b/qlib/contrib/model/double_ensemble.py @@ -184,7 +184,7 @@ class DEnsembleModel(Model): / M ) loss_feat = self.get_loss(y_train.values.squeeze(), pred.values) - g.loc[i_f, "g_value"] = np.mean(loss_feat - loss_values) / np.std(loss_feat - loss_values) + g.loc[i_f, "g_value"] = np.mean(loss_feat - loss_values) / (np.std(loss_feat - loss_values) + 1e-7) x_train_tmp.loc[:, feat] = x_train.loc[:, feat].copy() # one column in train features is all-nan # if g['g_value'].isna().any() From 6bcd88973b3ff906ac61e174185a28ea2b1d8ea0 Mon Sep 17 00:00:00 2001 From: wangershi Date: Sun, 7 Mar 2021 19:32:37 +0800 Subject: [PATCH 067/134] resolve one bug --- scripts/data_collector/fund/collector.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/scripts/data_collector/fund/collector.py b/scripts/data_collector/fund/collector.py index 795d8848e..08773cb9f 100644 --- a/scripts/data_collector/fund/collector.py +++ b/scripts/data_collector/fund/collector.py @@ -324,7 +324,6 @@ class FundNormalize: self._symbol_field_name = symbol_field_name self._calendar_list = self._get_calendar_list() - print (self._calendar_list) @staticmethod def normalize_fund( @@ -368,7 +367,7 @@ class FundNormalize1d(FundNormalize, ABC): DAILY_FORMAT = "%Y-%m-%d" def normalize(self, df: pd.DataFrame) -> pd.DataFrame: - df = super(FundNormalize, self).normalize(df) + df = super(FundNormalize1d, self).normalize(df) return df From 9df0361262eadd065ab783bb5349ef16203d04b4 Mon Sep 17 00:00:00 2001 From: wangershi Date: Sun, 7 Mar 2021 19:35:50 +0800 Subject: [PATCH 068/134] black --- scripts/data_collector/utils.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/scripts/data_collector/utils.py b/scripts/data_collector/utils.py index 56d010974..ed14ad6e1 100644 --- a/scripts/data_collector/utils.py +++ b/scripts/data_collector/utils.py @@ -105,7 +105,11 @@ def return_date_list(source_dir, date_field_name: str, file_path: Path): def get_calendar_list_by_ratio( - source_dir: [str, Path], date_field_name: str = "date", threshold: float = 0.5, minimum_count: int = 10, max_workers: int = 16 + source_dir: [str, Path], + date_field_name: str = "date", + threshold: float = 0.5, + minimum_count: int = 10, + max_workers: int = 16, ) -> list: """get calendar list by selecting the date when few funds trade in this day @@ -134,7 +138,7 @@ def get_calendar_list_by_ratio( _number_all_funds = len(file_list) logger.info(f"count how many funds trade in this day......") - _dict_count_trade = dict() # dict{date:count} + _dict_count_trade = dict() # dict{date:count} _fun = partial(return_date_list, source_dir, date_field_name) with tqdm(total=_number_all_funds) as p_bar: with ProcessPoolExecutor(max_workers=max_workers) as executor: @@ -146,9 +150,9 @@ def get_calendar_list_by_ratio( _dict_count_trade[date] += 1 p_bar.update() - + logger.info(f"count how many funds have founded in this day......") - _dict_count_founding = {date:_number_all_funds for date in _dict_count_trade.keys()} # dict{date:count} + _dict_count_founding = {date: _number_all_funds for date in _dict_count_trade.keys()} # dict{date:count} with tqdm(total=_number_all_funds) as p_bar: with ProcessPoolExecutor(max_workers=max_workers) as executor: for date_list in executor.map(_fun, file_list[:_number_all_funds]): @@ -157,7 +161,11 @@ def get_calendar_list_by_ratio( if date < oldest_date: _dict_count_founding[date] -= 1 - calendar = [date for date in _dict_count_trade if _dict_count_trade[date] >= max(int(_dict_count_founding[date] * threshold), minimum_count)] + calendar = [ + date + for date in _dict_count_trade + if _dict_count_trade[date] >= max(int(_dict_count_founding[date] * threshold), minimum_count) + ] return calendar From 7bed3b4c2eba8ec0a04bdd2c1227be72d267f820 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Mon, 8 Mar 2021 06:39:03 +0000 Subject: [PATCH 069/134] Fix python format by black --- qlib/contrib/model/pytorch_tabnet.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/qlib/contrib/model/pytorch_tabnet.py b/qlib/contrib/model/pytorch_tabnet.py index 7e47f58dc..682e1b19f 100644 --- a/qlib/contrib/model/pytorch_tabnet.py +++ b/qlib/contrib/model/pytorch_tabnet.py @@ -100,7 +100,9 @@ class TabnetModel(Model): self.device ) self.logger.info("model:\n{:}\n{:}".format(self.tabnet_model, self.tabnet_decoder)) - self.logger.info("model size: {:.4f} MB".format(count_parameters(self.tabnet_model) + count_parameters(self.tabnet_decoder))) + self.logger.info( + "model size: {:.4f} MB".format(count_parameters(self.tabnet_model) + count_parameters(self.tabnet_decoder)) + ) if optimizer.lower() == "adam": self.pretrain_optimizer = optim.Adam( From ca48345b29dd40d6b50c87bd027eca51cb522a05 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Mon, 8 Mar 2021 08:16:17 +0000 Subject: [PATCH 070/134] Simplify count_parameters --- qlib/contrib/model/pytorch_tabnet.py | 4 +--- qlib/contrib/model/pytorch_utils.py | 13 ++++++++----- 2 files changed, 9 insertions(+), 8 deletions(-) diff --git a/qlib/contrib/model/pytorch_tabnet.py b/qlib/contrib/model/pytorch_tabnet.py index 682e1b19f..020bbaff2 100644 --- a/qlib/contrib/model/pytorch_tabnet.py +++ b/qlib/contrib/model/pytorch_tabnet.py @@ -100,9 +100,7 @@ class TabnetModel(Model): self.device ) self.logger.info("model:\n{:}\n{:}".format(self.tabnet_model, self.tabnet_decoder)) - self.logger.info( - "model size: {:.4f} MB".format(count_parameters(self.tabnet_model) + count_parameters(self.tabnet_decoder)) - ) + self.logger.info("model size: {:.4f} MB".format(count_parameters([self.tabnet_model, self.tabnet_decoder]))) if optimizer.lower() == "adam": self.pretrain_optimizer = optim.Adam( diff --git a/qlib/contrib/model/pytorch_utils.py b/qlib/contrib/model/pytorch_utils.py index 532969eb5..f2457c9a6 100644 --- a/qlib/contrib/model/pytorch_utils.py +++ b/qlib/contrib/model/pytorch_utils.py @@ -1,15 +1,18 @@ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. -import numpy as np import torch.nn as nn -def count_parameters(model_or_parameters, unit="mb"): - if isinstance(model_or_parameters, nn.Module): - counts = np.sum(np.prod(v.size()) for v in model_or_parameters.parameters()) +def count_parameters(models_or_parameters, unit="mb"): + if isinstance(models_or_parameters, nn.Module): + counts = sum(v.numel() for v in models_or_parameters.parameters()) + elif isinstance(models_or_parameters, nn.Parameter): + counts = models_or_parameters.numel() + elif isinstance(models_or_parameters, (list, tuple)): + return sum(count_parameters(x, unit) for x in model_or_parameters) else: - counts = np.sum(np.prod(v.size()) for v in model_or_parameters) + counts = sum(v.numel() for v in models_or_parameters) if unit.lower() == "mb": counts /= 1e6 elif unit.lower() == "kb": From 03ef918dd8efb22d4b0561b281420795ec028c89 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Mon, 8 Mar 2021 08:24:23 +0000 Subject: [PATCH 071/134] Fix bugs in count_parameters --- qlib/contrib/model/pytorch_utils.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/qlib/contrib/model/pytorch_utils.py b/qlib/contrib/model/pytorch_utils.py index f2457c9a6..c0d483db3 100644 --- a/qlib/contrib/model/pytorch_utils.py +++ b/qlib/contrib/model/pytorch_utils.py @@ -3,14 +3,13 @@ import torch.nn as nn - def count_parameters(models_or_parameters, unit="mb"): if isinstance(models_or_parameters, nn.Module): counts = sum(v.numel() for v in models_or_parameters.parameters()) elif isinstance(models_or_parameters, nn.Parameter): counts = models_or_parameters.numel() elif isinstance(models_or_parameters, (list, tuple)): - return sum(count_parameters(x, unit) for x in model_or_parameters) + return sum(count_parameters(x, unit) for x in models_or_parameters) else: counts = sum(v.numel() for v in models_or_parameters) if unit.lower() == "mb": From e061443560a9cdaef837760c9a485fedd0899007 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Mon, 8 Mar 2021 08:27:58 +0000 Subject: [PATCH 072/134] Fix lint error with Black --- qlib/contrib/model/pytorch_utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/qlib/contrib/model/pytorch_utils.py b/qlib/contrib/model/pytorch_utils.py index c0d483db3..e7a8e8d67 100644 --- a/qlib/contrib/model/pytorch_utils.py +++ b/qlib/contrib/model/pytorch_utils.py @@ -3,6 +3,7 @@ import torch.nn as nn + def count_parameters(models_or_parameters, unit="mb"): if isinstance(models_or_parameters, nn.Module): counts = sum(v.numel() for v in models_or_parameters.parameters()) From 79c1142d3e7f456ca66fe238973a49edefa1b86f Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 8 Mar 2021 17:09:33 +0800 Subject: [PATCH 073/134] Pass nan_option to structured covariance estimator. --- qlib/model/riskmodel/structured.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/qlib/model/riskmodel/structured.py b/qlib/model/riskmodel/structured.py index e778c2faa..69c032e81 100644 --- a/qlib/model/riskmodel/structured.py +++ b/qlib/model/riskmodel/structured.py @@ -30,7 +30,7 @@ class StructuredCovEstimator(RiskModel): FACTOR_MODEL_PCA = "pca" FACTOR_MODEL_FA = "fa" - NAN_OPTION = "fill" + DEFAULT_NAN_OPTION = "fill" def __init__( self, @@ -38,6 +38,7 @@ class StructuredCovEstimator(RiskModel): num_factors: int = 10, assume_centered: bool = False, scale_return: bool = True, + nan_option: str = DEFAULT_NAN_OPTION ): """ Args: @@ -45,8 +46,11 @@ class StructuredCovEstimator(RiskModel): num_factors (int): number of components to keep. assume_centered (bool): whether the data is assumed to be centered. scale_return (bool): whether scale returns as percentage. + nan_option (str): nan handling option (`fill`). """ - super().__init__(self.NAN_OPTION, assume_centered, scale_return) + assert nan_option in [self.DEFAULT_NAN_OPTION], "nan_option={} is not supported".format(nan_option) + + super().__init__(nan_option, assume_centered, scale_return) assert factor_model in [ self.FACTOR_MODEL_PCA, From 4d5a30b30b6766168dcc3b19e6a4420c31710da4 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 8 Mar 2021 17:14:29 +0800 Subject: [PATCH 074/134] Resolve https://github.com/microsoft/qlib/pull/280\#discussion_r589167776 --- qlib/model/riskmodel/structured.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/qlib/model/riskmodel/structured.py b/qlib/model/riskmodel/structured.py index 69c032e81..7b722e600 100644 --- a/qlib/model/riskmodel/structured.py +++ b/qlib/model/riskmodel/structured.py @@ -36,21 +36,21 @@ class StructuredCovEstimator(RiskModel): self, factor_model: str = "pca", num_factors: int = 10, - assume_centered: bool = False, - scale_return: bool = True, - nan_option: str = DEFAULT_NAN_OPTION + **kwargs ): """ Args: factor_model (str): the latent factor models used to estimate the structured covariance (`pca`/`fa`). num_factors (int): number of components to keep. - assume_centered (bool): whether the data is assumed to be centered. - scale_return (bool): whether scale returns as percentage. - nan_option (str): nan handling option (`fill`). + kwargs: see `RiskModel` for more information """ - assert nan_option in [self.DEFAULT_NAN_OPTION], "nan_option={} is not supported".format(nan_option) + if 'nan_option' in kwargs.keys(): + assert kwargs['nan_option'] in [self.DEFAULT_NAN_OPTION], \ + "nan_option={} is not supported".format(kwargs['nan_option']) + else: + kwargs['nan_option'] = self.DEFAULT_NAN_OPTION - super().__init__(nan_option, assume_centered, scale_return) + super().__init__(**kwargs) assert factor_model in [ self.FACTOR_MODEL_PCA, From 81b86f8022ef90f437e01d20e75a0f77e1c65786 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 8 Mar 2021 17:18:07 +0800 Subject: [PATCH 075/134] Update test to cover changes in structured_cov_estimator --- tests/test_structured_cov_estimator.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/tests/test_structured_cov_estimator.py b/tests/test_structured_cov_estimator.py index 6aeae3d89..8ac1e8477 100644 --- a/tests/test_structured_cov_estimator.py +++ b/tests/test_structured_cov_estimator.py @@ -27,6 +27,24 @@ class TestStructuredCovEstimator(unittest.TestCase): self.assertTrue(if_identical) + def test_nan_option_covariance(self): + # Try to estimate the covariance from a randomly generated matrix. + NUM_VARIABLE = 10 + NUM_OBSERVATION = 200 + EPS = 1e-6 + + estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, nan_option='fill') + + X = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE) + + est_cov = estimator.predict(X, is_price=False) + np_cov = np.cov(X.T) # While numpy assume row means variable, qlib assume the other wise. + + delta = abs(est_cov - np_cov) + if_identical = (delta < EPS).all() + + self.assertTrue(if_identical) + def test_constructed_covariance(self): # Try to estimate the covariance from a specially crafted matrix. # There should be some significant correlation since X is specially crafted. From 351d598c9f45a59d96fb0be1d57bbbc662d756f6 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 8 Mar 2021 17:49:59 +0800 Subject: [PATCH 076/134] Resolve https://github.com/microsoft/qlib/pull/280\#discussion_r589165409 --- qlib/model/riskmodel/base.py | 28 ++++++---- qlib/model/riskmodel/structured.py | 74 ++------------------------ tests/test_structured_cov_estimator.py | 15 +++++- 3 files changed, 34 insertions(+), 83 deletions(-) diff --git a/qlib/model/riskmodel/base.py b/qlib/model/riskmodel/base.py index 02ab8c2fb..89df80e8f 100644 --- a/qlib/model/riskmodel/base.py +++ b/qlib/model/riskmodel/base.py @@ -1,6 +1,7 @@ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. +import inspect import numpy as np import pandas as pd from typing import Union @@ -37,18 +38,24 @@ class RiskModel(BaseModel): self.scale_return = scale_return def predict( - self, X: Union[pd.Series, pd.DataFrame, np.ndarray], return_corr: bool = False, is_price: bool = True - ) -> Union[pd.DataFrame, np.ndarray]: + self, X: Union[pd.Series, pd.DataFrame, np.ndarray], return_corr: bool = False, is_price: bool = True, + return_decomposed_components=False, + ) -> Union[pd.DataFrame, np.ndarray, tuple]: """ Args: X (pd.Series, pd.DataFrame or np.ndarray): data from which to estimate the covariance, with variables as columns and observations as rows. return_corr (bool): whether return the correlation matrix. is_price (bool): whether `X` contains price (if not assume stock returns). + return_decomposed_components (bool): whether return decomposed components of the covariance matrix. Returns: pd.DataFrame or np.ndarray: estimated covariance (or correlation). """ + assert ( + not return_corr or not return_decomposed_components + ), "Can only return either correlation matrix or decomposed components." + # transform input into 2D array if not isinstance(X, (pd.Series, pd.DataFrame)): columns = None @@ -75,6 +82,14 @@ class RiskModel(BaseModel): # handle nan and centered X = self._preprocess(X) + # return decomposed components if needed + if return_decomposed_components: + assert 'return_decomposed_components' in inspect.getfullargspec(self._predict).args, \ + 'This risk model does not support return decomposed components of the covariance matrix ' + + F, cov_b, var_u = self._predict(X, return_decomposed_components=True) + return F, cov_b, var_u + # estimate covariance S = self._predict(X) @@ -126,12 +141,3 @@ class RiskModel(BaseModel): if not self.assume_centered: X = X - np.nanmean(X, axis=0) return X - - - - - - - - - diff --git a/qlib/model/riskmodel/structured.py b/qlib/model/riskmodel/structured.py index 7b722e600..39ff0166e 100644 --- a/qlib/model/riskmodel/structured.py +++ b/qlib/model/riskmodel/structured.py @@ -60,81 +60,13 @@ class StructuredCovEstimator(RiskModel): self.num_factors = num_factors - def predict( - self, - X: Union[pd.Series, pd.DataFrame, np.ndarray], - return_corr: bool = False, - is_price: bool = True, - return_decomposed_components=False, - ) -> Union[pd.DataFrame, np.ndarray, tuple]: - """ - Args: - X (pd.Series, pd.DataFrame or np.ndarray): data from which to estimate the covariance, - with variables as columns and observations as rows. - return_corr (bool): whether return the correlation matrix. - is_price (bool): whether `X` contains price (if not assume stock returns). - return_decomposed_components (bool): whether return decomposed components of the covariance matrix. - - Returns: - tuple or pd.DataFrame or np.ndarray: decomposed covariance matrix or estimated covariance or correlation. - """ - assert ( - not return_corr or not return_decomposed_components - ), "Can only return either correlation matrix or decomposed components." - - # transform input into 2D array - if not isinstance(X, (pd.Series, pd.DataFrame)): - columns = None - else: - if isinstance(X.index, pd.MultiIndex): - if isinstance(X, pd.DataFrame): - X = X.iloc[:, 0].unstack(level="instrument") # always use the first column - else: - X = X.unstack(level="instrument") - else: - # X is 2D DataFrame - pass - columns = X.columns # will be used to restore dataframe - X = X.values - - # calculate pct_change - if is_price: - X = X[1:] / X[:-1] - 1 # NOTE: resulting `n - 1` rows - - # scale return - if self.scale_return: - X *= 100 - - # handle nan and centered - X = self._preprocess(X) - - if return_decomposed_components: - F, cov_b, var_u = self._predict(X, return_structured=True) - return F, cov_b, var_u - else: - # estimate covariance - S = self._predict(X) - - # return correlation if needed - if return_corr: - vola = np.sqrt(np.diag(S)) - corr = S / np.outer(vola, vola) - if columns is None: - return corr - return pd.DataFrame(corr, index=columns, columns=columns) - - # return covariance - if columns is None: - return S - return pd.DataFrame(S, index=columns, columns=columns) - - def _predict(self, X: np.ndarray, return_structured=False) -> Union[np.ndarray, tuple]: + def _predict(self, X: np.ndarray, return_decomposed_components=False) -> Union[np.ndarray, tuple]: """ covariance estimation implementation Args: X (np.ndarray): data matrix containing multiple variables (columns) and observations (rows). - return_structured (bool): whether return decomposed components of the covariance matrix. + return_decomposed_components (bool): whether return decomposed components of the covariance matrix. Returns: tuple or np.ndarray: decomposed covariance matrix or covariance matrix. @@ -148,7 +80,7 @@ class StructuredCovEstimator(RiskModel): cov_b = np.cov(B.T) # num_factors x num_factors var_u = np.var(U, axis=0) # diagonal - if return_structured: + if return_decomposed_components: return F, cov_b, var_u cov_x = F @ cov_b @ F.T + np.diag(var_u) diff --git a/tests/test_structured_cov_estimator.py b/tests/test_structured_cov_estimator.py index 8ac1e8477..a3973be5a 100644 --- a/tests/test_structured_cov_estimator.py +++ b/tests/test_structured_cov_estimator.py @@ -28,7 +28,7 @@ class TestStructuredCovEstimator(unittest.TestCase): self.assertTrue(if_identical) def test_nan_option_covariance(self): - # Try to estimate the covariance from a randomly generated matrix. + # Test if nan_option is correctly passed. NUM_VARIABLE = 10 NUM_OBSERVATION = 200 EPS = 1e-6 @@ -45,6 +45,19 @@ class TestStructuredCovEstimator(unittest.TestCase): self.assertTrue(if_identical) + def test_decompose_covariance(self): + # Test if return_decomposed_components is correctly passed. + NUM_VARIABLE = 10 + NUM_OBSERVATION = 200 + + estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, nan_option='fill') + + X = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE) + + F, cov_b, var_u = estimator.predict(X, is_price=False, return_decomposed_components=True) + + self.assertTrue(F is not None and cov_b is not None and var_u is not None) + def test_constructed_covariance(self): # Try to estimate the covariance from a specially crafted matrix. # There should be some significant correlation since X is specially crafted. From c6675be7929afb6c0403aa6482f4bbea50bf053d Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 8 Mar 2021 17:51:36 +0800 Subject: [PATCH 077/134] Resolve https://github.com/microsoft/qlib/pull/280\#discussion_r589166143 --- qlib/portfolio/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/qlib/portfolio/__init__.py b/qlib/portfolio/__init__.py index 139597f9c..b7c525821 100644 --- a/qlib/portfolio/__init__.py +++ b/qlib/portfolio/__init__.py @@ -1,2 +1,3 @@ - +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. From fc89fec46d4c23e650ac2f9fca12c926673ab882 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 8 Mar 2021 18:56:54 +0800 Subject: [PATCH 078/134] Resolve https://github.com/microsoft/qlib/pull/280\#discussion_r589168764 --- qlib/portfolio/optimizer/enhanced_indexing.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/qlib/portfolio/optimizer/enhanced_indexing.py b/qlib/portfolio/optimizer/enhanced_indexing.py index d988c776b..a0d0bc050 100644 --- a/qlib/portfolio/optimizer/enhanced_indexing.py +++ b/qlib/portfolio/optimizer/enhanced_indexing.py @@ -19,7 +19,6 @@ class EnhancedIndexingOptimizer(BaseOptimizer): START_FROM_W0 = "w0" START_FROM_BENCH = "benchmark" - DO_NOT_START_FROM = "no_warm_start" def __init__( self, @@ -29,7 +28,7 @@ class EnhancedIndexingOptimizer(BaseOptimizer): inds_dev: float = None, scale_alpha: bool = True, verbose: bool = False, - warm_start: str = DO_NOT_START_FROM, + warm_start: str = None, max_iters: int = 10000, ): """ @@ -58,7 +57,7 @@ class EnhancedIndexingOptimizer(BaseOptimizer): self.inds_dev = inds_dev assert warm_start in [ - self.DO_NOT_START_FROM, + None, self.START_FROM_W0, self.START_FROM_BENCH, ], "illegal warm start option" From 2f9af1af8ff44a4b60a0e9e2ca5412d1453c9755 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 8 Mar 2021 19:02:40 +0800 Subject: [PATCH 079/134] Resolve https://github.com/microsoft/qlib/pull/280\#discussion_r589169769 --- qlib/portfolio/optimizer/enhanced_indexing.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/qlib/portfolio/optimizer/enhanced_indexing.py b/qlib/portfolio/optimizer/enhanced_indexing.py index a0d0bc050..1f7de6cb4 100644 --- a/qlib/portfolio/optimizer/enhanced_indexing.py +++ b/qlib/portfolio/optimizer/enhanced_indexing.py @@ -70,7 +70,7 @@ class EnhancedIndexingOptimizer(BaseOptimizer): def __call__( self, - u: np.ndarray, + u: Union[np.ndarray, pd.Series], F: np.ndarray, covB: np.ndarray, varU: np.ndarray, @@ -80,7 +80,7 @@ class EnhancedIndexingOptimizer(BaseOptimizer): ) -> Union[np.ndarray, pd.Series]: """ Args: - u (np.ndarray): expected returns (a.k.a., alpha) + u (np.ndarray or pd.Series): expected returns (a.k.a., alpha) F, covB, varU (np.ndarray): see StructuredCovEstimator w0 (np.ndarray): initial weights (for turnover control) w_bench (np.ndarray): benchmark weights @@ -91,6 +91,10 @@ class EnhancedIndexingOptimizer(BaseOptimizer): """ assert inds_onehot is not None or self.inds_dev is None, "Industry onehot vector is required." + # transform dataframe into array + if isinstance(u, pd.Series): + u = u.values + # scale alpha to match volatility if self.scale_alpha: u = u / u.std() From 7022675d003b4a603e9a04769e8a91e7232421ea Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 8 Mar 2021 19:07:28 +0800 Subject: [PATCH 080/134] Resolve https://github.com/microsoft/qlib/pull/280\#discussion_r589169489 --- qlib/portfolio/optimizer/enhanced_indexing.py | 40 +++++++++---------- qlib/portfolio/optimizer/optimizer.py | 25 ++++++------ 2 files changed, 31 insertions(+), 34 deletions(-) diff --git a/qlib/portfolio/optimizer/enhanced_indexing.py b/qlib/portfolio/optimizer/enhanced_indexing.py index 1f7de6cb4..5fdc1014d 100644 --- a/qlib/portfolio/optimizer/enhanced_indexing.py +++ b/qlib/portfolio/optimizer/enhanced_indexing.py @@ -21,15 +21,15 @@ class EnhancedIndexingOptimizer(BaseOptimizer): START_FROM_BENCH = "benchmark" def __init__( - self, - lamb: float = 10, - delta: float = 0.4, - bench_dev: float = 0.01, - inds_dev: float = None, - scale_alpha: bool = True, - verbose: bool = False, - warm_start: str = None, - max_iters: int = 10000, + self, + lamb: float = 10, + delta: float = 0.4, + bench_dev: float = 0.01, + inds_dev: float = None, + scale_alpha: bool = True, + verbose: bool = False, + warm_start: str = None, + max_iters: int = 10000, ): """ Args: @@ -56,11 +56,7 @@ class EnhancedIndexingOptimizer(BaseOptimizer): assert inds_dev is None or inds_dev >= 0, "industry deviation limit `inds_dev` should be positive or None." self.inds_dev = inds_dev - assert warm_start in [ - None, - self.START_FROM_W0, - self.START_FROM_BENCH, - ], "illegal warm start option" + assert warm_start in [None, self.START_FROM_W0, self.START_FROM_BENCH,], "illegal warm start option" self.start_from_w0 = warm_start == self.START_FROM_W0 self.start_from_bench = warm_start == self.START_FROM_BENCH @@ -69,14 +65,14 @@ class EnhancedIndexingOptimizer(BaseOptimizer): self.max_iters = max_iters def __call__( - self, - u: Union[np.ndarray, pd.Series], - F: np.ndarray, - covB: np.ndarray, - varU: np.ndarray, - w0: np.ndarray, - w_bench: np.ndarray, - inds_onehot: np.ndarray = None, + self, + u: Union[np.ndarray, pd.Series], + F: np.ndarray, + covB: np.ndarray, + varU: np.ndarray, + w0: np.ndarray, + w_bench: np.ndarray, + inds_onehot: np.ndarray = None, ) -> Union[np.ndarray, pd.Series]: """ Args: diff --git a/qlib/portfolio/optimizer/optimizer.py b/qlib/portfolio/optimizer/optimizer.py index 3daa98af3..54648a46a 100644 --- a/qlib/portfolio/optimizer/optimizer.py +++ b/qlib/portfolio/optimizer/optimizer.py @@ -30,13 +30,13 @@ class PortfolioOptimizer(BaseOptimizer): OPT_INV = "inv" def __init__( - self, - method: str = "inv", - lamb: float = 0, - delta: float = 0, - alpha: float = 0.0, - scale_alpha: bool = True, - tol: float = 1e-8, + self, + method: str = "inv", + lamb: float = 0, + delta: float = 0, + alpha: float = 0.0, + scale_alpha: bool = True, + tol: float = 1e-8, ): """ Args: @@ -63,10 +63,10 @@ class PortfolioOptimizer(BaseOptimizer): self.scale_alpha = scale_alpha def __call__( - self, - S: Union[np.ndarray, pd.DataFrame], - u: Optional[Union[np.ndarray, pd.Series]] = None, - w0: Optional[Union[np.ndarray, pd.Series]] = None, + self, + S: Union[np.ndarray, pd.DataFrame], + u: Optional[Union[np.ndarray, pd.Series]] = None, + w0: Optional[Union[np.ndarray, pd.Series]] = None, ) -> Union[np.ndarray, pd.Series]: """ Args: @@ -155,7 +155,7 @@ class PortfolioOptimizer(BaseOptimizer): return self._solve(len(S), self._get_objective_gmv(S), *self._get_constrains(w0)) def _optimize_mvo( - self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None + self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None ) -> np.ndarray: """optimize mean-variance portfolio @@ -251,6 +251,7 @@ class PortfolioOptimizer(BaseOptimizer): # add l2 regularization wrapped_obj = obj if self.alpha > 0: + def opt_obj(x): return obj(x) + self.alpha * np.sum(np.square(x)) From 6a305c73ae51254c9c8d7629b968720bc099ac6f Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 8 Mar 2021 19:08:55 +0800 Subject: [PATCH 081/134] Resolve https://github.com/microsoft/qlib/pull/280\#discussion_r589166529 --- qlib/model/riskmodel/base.py | 14 +++++++++----- qlib/model/riskmodel/poet.py | 2 +- qlib/model/riskmodel/shrink.py | 3 +-- qlib/model/riskmodel/structured.py | 16 ++++++---------- 4 files changed, 17 insertions(+), 18 deletions(-) diff --git a/qlib/model/riskmodel/base.py b/qlib/model/riskmodel/base.py index 89df80e8f..bb067e3d5 100644 --- a/qlib/model/riskmodel/base.py +++ b/qlib/model/riskmodel/base.py @@ -38,8 +38,11 @@ class RiskModel(BaseModel): self.scale_return = scale_return def predict( - self, X: Union[pd.Series, pd.DataFrame, np.ndarray], return_corr: bool = False, is_price: bool = True, - return_decomposed_components=False, + self, + X: Union[pd.Series, pd.DataFrame, np.ndarray], + return_corr: bool = False, + is_price: bool = True, + return_decomposed_components=False, ) -> Union[pd.DataFrame, np.ndarray, tuple]: """ Args: @@ -53,7 +56,7 @@ class RiskModel(BaseModel): pd.DataFrame or np.ndarray: estimated covariance (or correlation). """ assert ( - not return_corr or not return_decomposed_components + not return_corr or not return_decomposed_components ), "Can only return either correlation matrix or decomposed components." # transform input into 2D array @@ -84,8 +87,9 @@ class RiskModel(BaseModel): # return decomposed components if needed if return_decomposed_components: - assert 'return_decomposed_components' in inspect.getfullargspec(self._predict).args, \ - 'This risk model does not support return decomposed components of the covariance matrix ' + assert ( + "return_decomposed_components" in inspect.getfullargspec(self._predict).args + ), "This risk model does not support return decomposed components of the covariance matrix " F, cov_b, var_u = self._predict(X, return_decomposed_components=True) return F, cov_b, var_u diff --git a/qlib/model/riskmodel/poet.py b/qlib/model/riskmodel/poet.py index 8dbe89036..840384555 100644 --- a/qlib/model/riskmodel/poet.py +++ b/qlib/model/riskmodel/poet.py @@ -50,7 +50,7 @@ class POETCovEstimator(RiskModel): if self.num_factors > 0: Dd, V = np.linalg.eig(Y.T.dot(Y)) V = V[:, np.argsort(Dd)] - F = V[:, -self.num_factors:][:, ::-1] * np.sqrt(n) + F = V[:, -self.num_factors :][:, ::-1] * np.sqrt(n) LamPCA = Y.dot(F) / n uhat = np.asarray(Y - LamPCA.dot(F.T)) Lowrank = np.asarray(LamPCA.dot(LamPCA.T)) diff --git a/qlib/model/riskmodel/shrink.py b/qlib/model/riskmodel/shrink.py index 1298891fb..3cb2620d1 100644 --- a/qlib/model/riskmodel/shrink.py +++ b/qlib/model/riskmodel/shrink.py @@ -248,8 +248,7 @@ class ShrinkCovEstimator(RiskModel): roff1 = np.sum(v1 * cov_mkt[:, None].T) / var_mkt - np.sum(np.diag(v1) * cov_mkt) / var_mkt v3 = z.T.dot(z) / t - var_mkt * S roff3 = ( - np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt ** 2 - np.sum( - np.diag(v3) * cov_mkt ** 2) / var_mkt ** 2 + np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt ** 2 - np.sum(np.diag(v3) * cov_mkt ** 2) / var_mkt ** 2 ) roff = 2 * roff1 - roff3 rho = rdiag + roff diff --git a/qlib/model/riskmodel/structured.py b/qlib/model/riskmodel/structured.py index 39ff0166e..878503401 100644 --- a/qlib/model/riskmodel/structured.py +++ b/qlib/model/riskmodel/structured.py @@ -32,23 +32,19 @@ class StructuredCovEstimator(RiskModel): FACTOR_MODEL_FA = "fa" DEFAULT_NAN_OPTION = "fill" - def __init__( - self, - factor_model: str = "pca", - num_factors: int = 10, - **kwargs - ): + def __init__(self, factor_model: str = "pca", num_factors: int = 10, **kwargs): """ Args: factor_model (str): the latent factor models used to estimate the structured covariance (`pca`/`fa`). num_factors (int): number of components to keep. kwargs: see `RiskModel` for more information """ - if 'nan_option' in kwargs.keys(): - assert kwargs['nan_option'] in [self.DEFAULT_NAN_OPTION], \ - "nan_option={} is not supported".format(kwargs['nan_option']) + if "nan_option" in kwargs.keys(): + assert kwargs["nan_option"] in [self.DEFAULT_NAN_OPTION], "nan_option={} is not supported".format( + kwargs["nan_option"] + ) else: - kwargs['nan_option'] = self.DEFAULT_NAN_OPTION + kwargs["nan_option"] = self.DEFAULT_NAN_OPTION super().__init__(**kwargs) From 8b9065c16690057b3fdd3968262e875e3c520c87 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 8 Mar 2021 19:32:13 +0800 Subject: [PATCH 082/134] Reformat with black. --- qlib/config.py | 24 ++----- qlib/contrib/backtest/__init__.py | 18 +---- qlib/contrib/backtest/profit_attribution.py | 23 ++----- qlib/contrib/data/handler.py | 10 +-- qlib/contrib/eva/alpha.py | 6 +- qlib/contrib/evaluate.py | 7 +- qlib/contrib/evaluate_portfolio.py | 16 +---- qlib/contrib/model/catboost_model.py | 4 +- qlib/contrib/model/pytorch_alstm.py | 21 ++---- qlib/contrib/model/pytorch_alstm_ts.py | 17 ++--- qlib/contrib/model/pytorch_gats.py | 22 ++---- qlib/contrib/model/pytorch_gats_ts.py | 18 +---- qlib/contrib/model/pytorch_gru.py | 21 ++---- qlib/contrib/model/pytorch_gru_ts.py | 17 +---- qlib/contrib/model/pytorch_lstm.py | 21 ++---- qlib/contrib/model/pytorch_lstm_ts.py | 17 +---- qlib/contrib/model/pytorch_nn.py | 6 +- qlib/contrib/model/pytorch_sfm.py | 19 +---- qlib/contrib/model/pytorch_tabnet.py | 14 +--- qlib/contrib/model/xgboost.py | 4 +- qlib/contrib/online/executor.py | 24 ++----- qlib/contrib/online/manager.py | 6 +- qlib/contrib/online/operator.py | 8 +-- qlib/contrib/online/utils.py | 6 +- .../analysis_model_performance.py | 66 ++++-------------- .../analysis_position/cumulative_return.py | 36 ++-------- .../analysis_position/parse_position.py | 5 +- .../report/analysis_position/rank_label.py | 16 +---- .../report/analysis_position/report.py | 15 +--- qlib/contrib/report/graph.py | 6 +- qlib/contrib/strategy/cost_control.py | 5 +- qlib/contrib/strategy/order_generator.py | 12 +--- qlib/contrib/tuner/launcher.py | 6 +- qlib/contrib/tuner/space.py | 5 +- qlib/contrib/tuner/tuner.py | 26 ++----- qlib/data/client.py | 3 +- qlib/data/data.py | 69 +++---------------- qlib/data/dataset/utils.py | 5 +- qlib/data/filter.py | 7 +- qlib/portfolio/__init__.py | 1 - qlib/tests/__init__.py | 6 +- qlib/workflow/record_temp.py | 5 +- tests/test_all_pipeline.py | 9 +-- tests/test_dump_data.py | 9 +-- tests/test_get_data.py | 4 +- tests/test_structured_cov_estimator.py | 4 +- 46 files changed, 123 insertions(+), 546 deletions(-) diff --git a/qlib/config.py b/qlib/config.py index 52b05568d..344eb8527 100644 --- a/qlib/config.py +++ b/qlib/config.py @@ -115,12 +115,7 @@ _default_config = { "format": "[%(process)s:%(threadName)s](%(asctime)s) %(levelname)s - %(name)s - [%(filename)s:%(lineno)d] - %(message)s" } }, - "filters": { - "field_not_found": { - "()": "qlib.log.LogFilter", - "param": [".*?WARN: data not found for.*?"], - } - }, + "filters": {"field_not_found": {"()": "qlib.log.LogFilter", "param": [".*?WARN: data not found for.*?"],}}, "handlers": { "console": { "class": "logging.StreamHandler", @@ -135,10 +130,7 @@ _default_config = { "exp_manager": { "class": "MLflowExpManager", "module_path": "qlib.workflow.expm", - "kwargs": { - "uri": "file:" + str(Path(os.getcwd()).resolve() / "mlruns"), - "default_exp_name": "Experiment", - }, + "kwargs": {"uri": "file:" + str(Path(os.getcwd()).resolve() / "mlruns"), "default_exp_name": "Experiment",}, }, } @@ -200,16 +192,8 @@ HIGH_FREQ_CONFIG = { } _default_region_config = { - REG_CN: { - "trade_unit": 100, - "limit_threshold": 0.099, - "deal_price": "vwap", - }, - REG_US: { - "trade_unit": 1, - "limit_threshold": None, - "deal_price": "close", - }, + REG_CN: {"trade_unit": 100, "limit_threshold": 0.099, "deal_price": "vwap",}, + REG_US: {"trade_unit": 1, "limit_threshold": None, "deal_price": "close",}, } diff --git a/qlib/contrib/backtest/__init__.py b/qlib/contrib/backtest/__init__.py index aa24ffb0c..bd3494abf 100644 --- a/qlib/contrib/backtest/__init__.py +++ b/qlib/contrib/backtest/__init__.py @@ -18,13 +18,7 @@ logger = get_module_logger("backtest caller") def get_strategy( - strategy=None, - topk=50, - margin=0.5, - n_drop=5, - risk_degree=0.95, - str_type="dropout", - adjust_dates=None, + strategy=None, topk=50, margin=0.5, n_drop=5, risk_degree=0.95, str_type="dropout", adjust_dates=None, ): """get_strategy @@ -75,11 +69,7 @@ def get_strategy( str_cls = getattr(strategy_pool, str_cls_dict.get(str_type)) strategy = str_cls( - topk=topk, - buffer_margin=margin, - n_drop=n_drop, - risk_degree=risk_degree, - adjust_dates=adjust_dates, + topk=topk, buffer_margin=margin, n_drop=n_drop, risk_degree=risk_degree, adjust_dates=adjust_dates, ) elif isinstance(strategy, (dict, str)): # 2) create strategy with init_instance_by_config @@ -172,9 +162,7 @@ def get_exchange( def get_executor( - executor=None, - trade_exchange=None, - verbose=True, + executor=None, trade_exchange=None, verbose=True, ): """get_executor diff --git a/qlib/contrib/backtest/profit_attribution.py b/qlib/contrib/backtest/profit_attribution.py index 20c6f638f..355f06373 100644 --- a/qlib/contrib/backtest/profit_attribution.py +++ b/qlib/contrib/backtest/profit_attribution.py @@ -12,10 +12,7 @@ from pathlib import Path def get_benchmark_weight( - bench, - start_date=None, - end_date=None, - path=None, + bench, start_date=None, end_date=None, path=None, ): """get_benchmark_weight @@ -216,12 +213,7 @@ def get_stock_group(stock_group_field_df, bench_stock_weight_df, group_method, g def brinson_pa( - positions, - bench="SH000905", - group_field="industry", - group_method="category", - group_n=None, - deal_price="vwap", + positions, bench="SH000905", group_field="industry", group_method="category", group_n=None, deal_price="vwap", ): """brinson profit attribution @@ -255,17 +247,10 @@ def brinson_pa( # suspend stock is NAN. So we have to get more date to forward fill the NAN shift_start_date = start_date - datetime.timedelta(days=250) instruments = D.list_instruments( - D.instruments(market="all"), - start_time=shift_start_date, - end_time=end_date, - as_list=True, + D.instruments(market="all"), start_time=shift_start_date, end_time=end_date, as_list=True, ) stock_df = D.features( - instruments, - [group_field, deal_price], - start_time=shift_start_date, - end_time=end_date, - freq="day", + instruments, [group_field, deal_price], start_time=shift_start_date, end_time=end_date, freq="day", ) stock_df.columns = [group_field, "deal_price"] diff --git a/qlib/contrib/data/handler.py b/qlib/contrib/data/handler.py index 970b032d6..574287819 100644 --- a/qlib/contrib/data/handler.py +++ b/qlib/contrib/data/handler.py @@ -21,10 +21,7 @@ def check_transform_proc(proc_l, fit_start_time, fit_end_time): fit_start_time is not None and fit_end_time is not None ), "Make sure `fit_start_time` and `fit_end_time` are not None." pkwargs.update( - { - "fit_start_time": fit_start_time, - "fit_end_time": fit_end_time, - } + {"fit_start_time": fit_start_time, "fit_end_time": fit_end_time,} ) new_l.append({"class": klass.__name__, "kwargs": pkwargs}) else: @@ -170,10 +167,7 @@ class Alpha158(DataHandlerLP): def get_feature_config(self): conf = { "kbar": {}, - "price": { - "windows": [0], - "feature": ["OPEN", "HIGH", "LOW", "VWAP"], - }, + "price": {"windows": [0], "feature": ["OPEN", "HIGH", "LOW", "VWAP"],}, "rolling": {}, } return self.parse_config_to_fields(conf) diff --git a/qlib/contrib/eva/alpha.py b/qlib/contrib/eva/alpha.py index c68571853..363a18458 100644 --- a/qlib/contrib/eva/alpha.py +++ b/qlib/contrib/eva/alpha.py @@ -35,11 +35,7 @@ def calc_ic(pred: pd.Series, label: pd.Series, date_col="datetime", dropna=False def calc_long_short_return( - pred: pd.Series, - label: pd.Series, - date_col: str = "datetime", - quantile: float = 0.2, - dropna: bool = False, + pred: pd.Series, label: pd.Series, date_col: str = "datetime", quantile: float = 0.2, dropna: bool = False, ) -> Tuple[pd.Series, pd.Series]: """ calculate long-short return diff --git a/qlib/contrib/evaluate.py b/qlib/contrib/evaluate.py index 4aa5b5515..5cb1ce4eb 100644 --- a/qlib/contrib/evaluate.py +++ b/qlib/contrib/evaluate.py @@ -244,12 +244,7 @@ def long_short_backtest( short_returns[date] = np.mean(short_profit) + np.mean(all_profit) ls_returns[date] = np.mean(short_profit) + np.mean(long_profit) - return dict( - zip( - ["long", "short", "long_short"], - map(pd.Series, [long_returns, short_returns, ls_returns]), - ) - ) + return dict(zip(["long", "short", "long_short"], map(pd.Series, [long_returns, short_returns, ls_returns]),)) def t_run(): diff --git a/qlib/contrib/evaluate_portfolio.py b/qlib/contrib/evaluate_portfolio.py index 04ddd8db0..2d94105e4 100644 --- a/qlib/contrib/evaluate_portfolio.py +++ b/qlib/contrib/evaluate_portfolio.py @@ -64,12 +64,7 @@ def get_position_value(evaluate_date, position): instruments = list(set(instruments) - set(["cash"])) # filter 'cash' fields = ["$close"] close_data_df = D.features( - instruments, - fields, - start_time=evaluate_date, - end_time=evaluate_date, - freq="day", - disk_cache=0, + instruments, fields, start_time=evaluate_date, end_time=evaluate_date, freq="day", disk_cache=0, ) value = _get_position_value_from_df(evaluate_date, position, close_data_df) return value @@ -87,14 +82,7 @@ def get_position_list_value(positions): start_date, end_date = day_list[0], day_list[-1] # load data fields = ["$close"] - close_data_df = D.features( - instruments, - fields, - start_time=start_date, - end_time=end_date, - freq="day", - disk_cache=0, - ) + close_data_df = D.features(instruments, fields, start_time=start_date, end_time=end_date, freq="day", disk_cache=0,) # generate value # return dict for time:position_value value_dict = OrderedDict() diff --git a/qlib/contrib/model/catboost_model.py b/qlib/contrib/model/catboost_model.py index d57c32b70..2840c2cef 100644 --- a/qlib/contrib/model/catboost_model.py +++ b/qlib/contrib/model/catboost_model.py @@ -32,9 +32,7 @@ class CatBoostModel(Model): **kwargs ): df_train, df_valid = dataset.prepare( - ["train", "valid"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] diff --git a/qlib/contrib/model/pytorch_alstm.py b/qlib/contrib/model/pytorch_alstm.py index bbbb61851..306e68aad 100644 --- a/qlib/contrib/model/pytorch_alstm.py +++ b/qlib/contrib/model/pytorch_alstm.py @@ -118,10 +118,7 @@ class ALSTM(Model): torch.manual_seed(self.seed) self.ALSTM_model = ALSTMModel( - d_feat=self.d_feat, - hidden_size=self.hidden_size, - num_layers=self.num_layers, - dropout=self.dropout, + d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, ) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.ALSTM_model.parameters(), lr=self.lr) @@ -211,17 +208,11 @@ class ALSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset: DatasetH, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -328,14 +319,12 @@ class ALSTMModel(nn.Module): self.fc_out = nn.Linear(in_features=self.hid_size * 2, out_features=1) self.att_net = nn.Sequential() self.att_net.add_module( - "att_fc_in", - nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), + "att_fc_in", nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), ) self.att_net.add_module("att_dropout", torch.nn.Dropout(self.dropout)) self.att_net.add_module("att_act", nn.Tanh()) self.att_net.add_module( - "att_fc_out", - nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), + "att_fc_out", nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), ) self.att_net.add_module("att_softmax", nn.Softmax(dim=1)) diff --git a/qlib/contrib/model/pytorch_alstm_ts.py b/qlib/contrib/model/pytorch_alstm_ts.py index 725568de8..612bacbec 100644 --- a/qlib/contrib/model/pytorch_alstm_ts.py +++ b/qlib/contrib/model/pytorch_alstm_ts.py @@ -123,10 +123,7 @@ class ALSTM(Model): torch.manual_seed(self.seed) self.ALSTM_model = ALSTMModel( - d_feat=self.d_feat, - hidden_size=self.hidden_size, - num_layers=self.num_layers, - dropout=self.dropout, + d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, ).to(self.device) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.ALSTM_model.parameters(), lr=self.lr) @@ -198,11 +195,7 @@ class ALSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset, evals_result=dict(), verbose=True, save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -309,14 +302,12 @@ class ALSTMModel(nn.Module): self.fc_out = nn.Linear(in_features=self.hid_size * 2, out_features=1) self.att_net = nn.Sequential() self.att_net.add_module( - "att_fc_in", - nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), + "att_fc_in", nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), ) self.att_net.add_module("att_dropout", torch.nn.Dropout(self.dropout)) self.att_net.add_module("att_act", nn.Tanh()) self.att_net.add_module( - "att_fc_out", - nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), + "att_fc_out", nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), ) self.att_net.add_module("att_softmax", nn.Softmax(dim=1)) diff --git a/qlib/contrib/model/pytorch_gats.py b/qlib/contrib/model/pytorch_gats.py index 07048e1bc..c59dc9197 100644 --- a/qlib/contrib/model/pytorch_gats.py +++ b/qlib/contrib/model/pytorch_gats.py @@ -229,17 +229,11 @@ class GATs(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset: DatasetH, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -340,19 +334,11 @@ class GATModel(nn.Module): if base_model == "GRU": self.rnn = nn.GRU( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) elif base_model == "LSTM": self.rnn = nn.LSTM( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) else: raise ValueError("unknown base model name `%s`" % base_model) diff --git a/qlib/contrib/model/pytorch_gats_ts.py b/qlib/contrib/model/pytorch_gats_ts.py index 1e94f56e4..dfc5f4ab5 100644 --- a/qlib/contrib/model/pytorch_gats_ts.py +++ b/qlib/contrib/model/pytorch_gats_ts.py @@ -242,11 +242,7 @@ class GATs(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset, evals_result=dict(), verbose=True, save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -361,19 +357,11 @@ class GATModel(nn.Module): if base_model == "GRU": self.rnn = nn.GRU( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) elif base_model == "LSTM": self.rnn = nn.LSTM( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) else: raise ValueError("unknown base model name `%s`" % base_model) diff --git a/qlib/contrib/model/pytorch_gru.py b/qlib/contrib/model/pytorch_gru.py index 84f863b9f..d2a774b65 100755 --- a/qlib/contrib/model/pytorch_gru.py +++ b/qlib/contrib/model/pytorch_gru.py @@ -118,10 +118,7 @@ class GRU(Model): torch.manual_seed(self.seed) self.gru_model = GRUModel( - d_feat=self.d_feat, - hidden_size=self.hidden_size, - num_layers=self.num_layers, - dropout=self.dropout, + d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, ) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.gru_model.parameters(), lr=self.lr) @@ -211,17 +208,11 @@ class GRU(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset: DatasetH, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -305,11 +296,7 @@ class GRUModel(nn.Module): super().__init__() self.rnn = nn.GRU( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_gru_ts.py b/qlib/contrib/model/pytorch_gru_ts.py index bb6618b85..49f438cc3 100755 --- a/qlib/contrib/model/pytorch_gru_ts.py +++ b/qlib/contrib/model/pytorch_gru_ts.py @@ -123,10 +123,7 @@ class GRU(Model): torch.manual_seed(self.seed) self.GRU_model = GRUModel( - d_feat=self.d_feat, - hidden_size=self.hidden_size, - num_layers=self.num_layers, - dropout=self.dropout, + d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, ).to(self.device) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.GRU_model.parameters(), lr=self.lr) @@ -198,11 +195,7 @@ class GRU(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset, evals_result=dict(), verbose=True, save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -286,11 +279,7 @@ class GRUModel(nn.Module): super().__init__() self.rnn = nn.GRU( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_lstm.py b/qlib/contrib/model/pytorch_lstm.py index 163d500ec..02ca16e36 100755 --- a/qlib/contrib/model/pytorch_lstm.py +++ b/qlib/contrib/model/pytorch_lstm.py @@ -118,10 +118,7 @@ class LSTM(Model): torch.manual_seed(self.seed) self.lstm_model = LSTMModel( - d_feat=self.d_feat, - hidden_size=self.hidden_size, - num_layers=self.num_layers, - dropout=self.dropout, + d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, ) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.lstm_model.parameters(), lr=self.lr) @@ -211,17 +208,11 @@ class LSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset: DatasetH, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -305,11 +296,7 @@ class LSTMModel(nn.Module): super().__init__() self.rnn = nn.LSTM( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_lstm_ts.py b/qlib/contrib/model/pytorch_lstm_ts.py index cf4f8fb9f..2ec36f96e 100755 --- a/qlib/contrib/model/pytorch_lstm_ts.py +++ b/qlib/contrib/model/pytorch_lstm_ts.py @@ -123,10 +123,7 @@ class LSTM(Model): torch.manual_seed(self.seed) self.LSTM_model = LSTMModel( - d_feat=self.d_feat, - hidden_size=self.hidden_size, - num_layers=self.num_layers, - dropout=self.dropout, + d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, ).to(self.device) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.LSTM_model.parameters(), lr=self.lr) @@ -198,11 +195,7 @@ class LSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, - dataset, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset, evals_result=dict(), verbose=True, save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -286,11 +279,7 @@ class LSTMModel(nn.Module): super().__init__() self.rnn = nn.LSTM( - input_size=d_feat, - hidden_size=hidden_size, - num_layers=num_layers, - batch_first=True, - dropout=dropout, + input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_nn.py b/qlib/contrib/model/pytorch_nn.py index 16fcea9ff..8c1a77ec3 100644 --- a/qlib/contrib/model/pytorch_nn.py +++ b/qlib/contrib/model/pytorch_nn.py @@ -154,11 +154,7 @@ class DNNModelPytorch(Model): self.dnn_model.to(self.device) def fit( - self, - dataset: DatasetH, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, ): df_train, df_valid = dataset.prepare( ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L diff --git a/qlib/contrib/model/pytorch_sfm.py b/qlib/contrib/model/pytorch_sfm.py index d5169e6c7..1f7433e05 100644 --- a/qlib/contrib/model/pytorch_sfm.py +++ b/qlib/contrib/model/pytorch_sfm.py @@ -30,14 +30,7 @@ from ...data.dataset.handler import DataHandlerLP class SFM_Model(nn.Module): def __init__( - self, - d_feat=6, - output_dim=1, - freq_dim=10, - hidden_size=64, - dropout_W=0.0, - dropout_U=0.0, - device="cpu", + self, d_feat=6, output_dim=1, freq_dim=10, hidden_size=64, dropout_W=0.0, dropout_U=0.0, device="cpu", ): super().__init__() @@ -362,17 +355,11 @@ class SFM(Model): self.train_optimizer.step() def fit( - self, - dataset: DatasetH, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, ): df_train, df_valid = dataset.prepare( - ["train", "valid"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] diff --git a/qlib/contrib/model/pytorch_tabnet.py b/qlib/contrib/model/pytorch_tabnet.py index 62e32d701..18e9d8eb4 100644 --- a/qlib/contrib/model/pytorch_tabnet.py +++ b/qlib/contrib/model/pytorch_tabnet.py @@ -120,9 +120,7 @@ class TabnetModel(Model): os.makedirs("pretrain") [df_train, df_valid] = dataset.prepare( - ["pretrain", "pretrain_validation"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["pretrain", "pretrain_validation"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) df_train.fillna(df_train.mean(), inplace=True) @@ -156,11 +154,7 @@ class TabnetModel(Model): break def fit( - self, - dataset: DatasetH, - evals_result=dict(), - verbose=True, - save_path=None, + self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, ): if self.pretrain: # there is a pretrained model, load the model @@ -172,9 +166,7 @@ class TabnetModel(Model): # adding one more linear layer to fit the final output dimension self.tabnet_model = FinetuneModel(self.out_dim, self.final_out_dim, self.tabnet_model).to(self.device) df_train, df_valid = dataset.prepare( - ["train", "valid"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) df_train.fillna(df_train.mean(), inplace=True) x_train, y_train = df_train["feature"], df_train["label"] diff --git a/qlib/contrib/model/xgboost.py b/qlib/contrib/model/xgboost.py index ba2e5789b..e37725c2e 100755 --- a/qlib/contrib/model/xgboost.py +++ b/qlib/contrib/model/xgboost.py @@ -29,9 +29,7 @@ class XGBModel(Model): ): df_train, df_valid = dataset.prepare( - ["train", "valid"], - col_set=["feature", "label"], - data_key=DataHandlerLP.DK_L, + ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] diff --git a/qlib/contrib/online/executor.py b/qlib/contrib/online/executor.py index 2bd0937a0..52b868881 100644 --- a/qlib/contrib/online/executor.py +++ b/qlib/contrib/online/executor.py @@ -150,21 +150,13 @@ class SimulatorExecutor(BaseExecutor): if order.direction == Order.SELL: # sell print( "[I {:%Y-%m-%d}]: sell {}, price {:.2f}, amount {}, value {:.2f}.".format( - trade_date, - order.stock_id, - trade_price, - order.deal_amount, - trade_val, + trade_date, order.stock_id, trade_price, order.deal_amount, trade_val, ) ) else: print( "[I {:%Y-%m-%d}]: buy {}, price {:.2f}, amount {}, value {:.2f}.".format( - trade_date, - order.stock_id, - trade_price, - order.deal_amount, - trade_val, + trade_date, order.stock_id, trade_price, order.deal_amount, trade_val, ) ) @@ -271,21 +263,13 @@ def load_order_list(user_path, trade_date): for stock_id in order_dict["sell"]: amount, factor = order_dict["sell"][stock_id] order = Order( - stock_id=stock_id, - amount=amount, - trade_date=pd.Timestamp(trade_date), - direction=Order.SELL, - factor=factor, + stock_id=stock_id, amount=amount, trade_date=pd.Timestamp(trade_date), direction=Order.SELL, factor=factor, ) order_list.append(order) for stock_id in order_dict["buy"]: amount, factor = order_dict["buy"][stock_id] order = Order( - stock_id=stock_id, - amount=amount, - trade_date=pd.Timestamp(trade_date), - direction=Order.BUY, - factor=factor, + stock_id=stock_id, amount=amount, trade_date=pd.Timestamp(trade_date), direction=Order.BUY, factor=factor, ) order_list.append(order) return order_list diff --git a/qlib/contrib/online/manager.py b/qlib/contrib/online/manager.py index cf850b9da..a4476709d 100644 --- a/qlib/contrib/online/manager.py +++ b/qlib/contrib/online/manager.py @@ -84,12 +84,10 @@ class UserManager: raise ValueError("Cannot find user {}".format(user_id)) self.users[user_id].account.save_account(self.data_path / user_id) save_instance( - self.users[user_id].strategy, - self.data_path / user_id / "strategy_{}.pickle".format(user_id), + self.users[user_id].strategy, self.data_path / user_id / "strategy_{}.pickle".format(user_id), ) save_instance( - self.users[user_id].model, - self.data_path / user_id / "model_{}.pickle".format(user_id), + self.users[user_id].model, self.data_path / user_id / "model_{}.pickle".format(user_id), ) def add_user(self, user_id, config_file, add_date): diff --git a/qlib/contrib/online/operator.py b/qlib/contrib/online/operator.py index c8b44f578..c82deb394 100644 --- a/qlib/contrib/online/operator.py +++ b/qlib/contrib/online/operator.py @@ -125,9 +125,7 @@ class Operator: trade_date=trade_date, ) save_order_list( - order_list=order_list, - user_path=(pathlib.Path(path) / user_id), - trade_date=trade_date, + order_list=order_list, user_path=(pathlib.Path(path) / user_id), trade_date=trade_date, ) self.logger.info("Generate order list at {} for {}".format(trade_date, user_id)) um.save_user_data(user_id) @@ -160,9 +158,7 @@ class Operator: order_list = load_order_list(user_path=(pathlib.Path(path) / user_id), trade_date=trade_date) trade_info = executor.execute(order_list=order_list, trade_account=user.account, trade_date=trade_date) executor.save_executed_file_from_trade_info( - trade_info=trade_info, - user_path=(pathlib.Path(path) / user_id), - trade_date=trade_date, + trade_info=trade_info, user_path=(pathlib.Path(path) / user_id), trade_date=trade_date, ) self.logger.info("execute order list at {} for {}".format(trade_date.date(), user_id)) diff --git a/qlib/contrib/online/utils.py b/qlib/contrib/online/utils.py index 611af63e4..fb96c87bd 100644 --- a/qlib/contrib/online/utils.py +++ b/qlib/contrib/online/utils.py @@ -79,11 +79,7 @@ def prepare(um, today, user_id, exchange_config=None): log.warning("user_id:{}, last trading date {} after today {}".format(user_id, latest_trading_date, today)) return [pd.Timestamp(latest_trading_date)], None - dates = D.calendar( - start_time=pd.Timestamp(latest_trading_date), - end_time=pd.Timestamp(today), - future=True, - ) + dates = D.calendar(start_time=pd.Timestamp(latest_trading_date), end_time=pd.Timestamp(today), future=True,) dates = list(dates) dates.append(get_next_trading_date(dates[-1], future=True)) if exchange_config: diff --git a/qlib/contrib/report/analysis_model/analysis_model_performance.py b/qlib/contrib/report/analysis_model/analysis_model_performance.py index 1cb14d261..ef1447a12 100644 --- a/qlib/contrib/report/analysis_model/analysis_model_performance.py +++ b/qlib/contrib/report/analysis_model/analysis_model_performance.py @@ -53,8 +53,7 @@ def _group_return(pred_label: pd.DataFrame = None, reverse: bool = False, N: int t_df.index = t_df.index.strftime("%Y-%m-%d") # Cumulative Return By Group group_scatter_figure = ScatterGraph( - t_df.cumsum(), - layout=dict(title="Cumulative Return", xaxis=dict(type="category", tickangle=45)), + t_df.cumsum(), layout=dict(title="Cumulative Return", xaxis=dict(type="category", tickangle=45)), ).figure t_df = t_df.loc[:, ["long-short", "long-average"]] @@ -62,12 +61,7 @@ def _group_return(pred_label: pd.DataFrame = None, reverse: bool = False, N: int group_hist_figure = SubplotsGraph( t_df, kind_map=dict(kind="DistplotGraph", kwargs=dict(bin_size=_bin_size)), - subplots_kwargs=dict( - rows=1, - cols=2, - print_grid=False, - subplot_titles=["long-short", "long-average"], - ), + subplots_kwargs=dict(rows=1, cols=2, print_grid=False, subplot_titles=["long-short", "long-average"],), ).figure return group_scatter_figure, group_hist_figure @@ -102,15 +96,12 @@ def _pred_ic(pred_label: pd.DataFrame = None, rank: bool = False, **kwargs) -> t _index = ic.index.get_level_values(0).astype("str").str.replace("-", "").str.slice(0, 6) _monthly_ic = ic.groupby(_index).mean() _monthly_ic.index = pd.MultiIndex.from_arrays( - [_monthly_ic.index.str.slice(0, 4), _monthly_ic.index.str.slice(4, 6)], - names=["year", "month"], + [_monthly_ic.index.str.slice(0, 4), _monthly_ic.index.str.slice(4, 6)], names=["year", "month"], ) # fill month _month_list = pd.date_range( - start=pd.Timestamp(f"{_index.min()[:4]}0101"), - end=pd.Timestamp(f"{_index.max()[:4]}1231"), - freq="1M", + start=pd.Timestamp(f"{_index.min()[:4]}0101"), end=pd.Timestamp(f"{_index.max()[:4]}1231"), freq="1M", ) _years = [] _month = [] @@ -142,32 +133,15 @@ def _pred_ic(pred_label: pd.DataFrame = None, rank: bool = False, **kwargs) -> t _bin_size = ((_ic_df.max() - _ic_df.min()) / 20).min() _sub_graph_data = [ - ( - "ic", - dict( - row=1, - col=1, - name="", - kind="DistplotGraph", - graph_kwargs=dict(bin_size=_bin_size), - ), - ), + ("ic", dict(row=1, col=1, name="", kind="DistplotGraph", graph_kwargs=dict(bin_size=_bin_size),),), (_qqplot_fig, dict(row=1, col=2)), ] ic_hist_figure = SubplotsGraph( _ic_df.dropna(), kind_map=dict(kind="HistogramGraph", kwargs=dict()), - subplots_kwargs=dict( - rows=1, - cols=2, - print_grid=False, - subplot_titles=["IC", "IC %s Dist. Q-Q" % dist_name], - ), + subplots_kwargs=dict(rows=1, cols=2, print_grid=False, subplot_titles=["IC", "IC %s Dist. Q-Q" % dist_name],), sub_graph_data=_sub_graph_data, - layout=dict( - yaxis2=dict(title="Observed Quantile"), - xaxis2=dict(title=f"{dist_name} Distribution Quantile"), - ), + layout=dict(yaxis2=dict(title="Observed Quantile"), xaxis2=dict(title=f"{dist_name} Distribution Quantile"),), ).figure return ic_bar_figure, ic_heatmap_figure, ic_hist_figure @@ -181,8 +155,7 @@ def _pred_autocorr(pred_label: pd.DataFrame, lag=1, **kwargs) -> tuple: _df = ac.to_frame("value") _df.index = _df.index.strftime("%Y-%m-%d") ac_figure = ScatterGraph( - _df, - layout=dict(title="Auto Correlation", xaxis=dict(type="category", tickangle=45)), + _df, layout=dict(title="Auto Correlation", xaxis=dict(type="category", tickangle=45)), ).figure return (ac_figure,) @@ -202,17 +175,11 @@ def _pred_turnover(pred_label: pd.DataFrame, N=5, lag=1, **kwargs) -> tuple: .sum() / (len(x) // N) ) - r_df = pd.DataFrame( - { - "Top": top, - "Bottom": bottom, - } - ) + r_df = pd.DataFrame({"Top": top, "Bottom": bottom,}) # FIXME: support HIGH-FREQ r_df.index = r_df.index.strftime("%Y-%m-%d") turnover_figure = ScatterGraph( - r_df, - layout=dict(title="Top-Bottom Turnover", xaxis=dict(type="category", tickangle=45)), + r_df, layout=dict(title="Top-Bottom Turnover", xaxis=dict(type="category", tickangle=45)), ).figure return (turnover_figure,) @@ -230,11 +197,7 @@ def ic_figure(ic_df: pd.DataFrame, show_nature_day=True, **kwargs) -> go.Figure: # FIXME: support HIGH-FREQ ic_df.index = ic_df.index.strftime("%Y-%m-%d") ic_bar_figure = BarGraph( - ic_df, - layout=dict( - title="Information Coefficient (IC)", - xaxis=dict(type="category", tickangle=45), - ), + ic_df, layout=dict(title="Information Coefficient (IC)", xaxis=dict(type="category", tickangle=45),), ).figure return ic_bar_figure @@ -277,12 +240,7 @@ def model_performance_graph( figure_list = [] for graph_name in graph_names: fun_res = eval(f"_{graph_name}")( - pred_label=pred_label, - lag=lag, - N=N, - reverse=reverse, - rank=rank, - show_nature_day=show_nature_day, + pred_label=pred_label, lag=lag, N=N, reverse=reverse, rank=rank, show_nature_day=show_nature_day, ) figure_list += fun_res diff --git a/qlib/contrib/report/analysis_position/cumulative_return.py b/qlib/contrib/report/analysis_position/cumulative_return.py index abb68ea60..604189c94 100644 --- a/qlib/contrib/report/analysis_position/cumulative_return.py +++ b/qlib/contrib/report/analysis_position/cumulative_return.py @@ -13,11 +13,7 @@ from ..analysis_position.parse_position import get_position_data def _get_cum_return_data_with_position( - position: dict, - report_normal: pd.DataFrame, - label_data: pd.DataFrame, - start_date=None, - end_date=None, + position: dict, report_normal: pd.DataFrame, label_data: pd.DataFrame, start_date=None, end_date=None, ): """ @@ -29,11 +25,7 @@ def _get_cum_return_data_with_position( :return: """ _cumulative_return_df = get_position_data( - position=position, - report_normal=report_normal, - label_data=label_data, - start_date=start_date, - end_date=end_date, + position=position, report_normal=report_normal, label_data=label_data, start_date=start_date, end_date=end_date, ).copy() _cumulative_return_df["label"] = _cumulative_return_df["label"] - _cumulative_return_df["bench"] @@ -87,11 +79,7 @@ def _get_cum_return_data_with_position( def _get_figure_with_position( - position: dict, - report_normal: pd.DataFrame, - label_data: pd.DataFrame, - start_date=None, - end_date=None, + position: dict, report_normal: pd.DataFrame, label_data: pd.DataFrame, start_date=None, end_date=None, ) -> Iterable[go.Figure]: """Get average analysis figures @@ -111,18 +99,12 @@ def _get_figure_with_position( # Create figures for _t_name in ["buy", "sell", "buy_minus_sell", "hold"]: sub_graph_data = [ - ( - "cum_{}".format(_t_name), - dict(row=1, col=1, graph_kwargs={"mode": "lines+markers", "xaxis": "x3"}), - ), + ("cum_{}".format(_t_name), dict(row=1, col=1, graph_kwargs={"mode": "lines+markers", "xaxis": "x3"}),), ( "{}_weight".format(_t_name.replace("minus", "plus") if "minus" in _t_name else _t_name), dict(row=2, col=1), ), - ( - "{}_value".format(_t_name), - dict(row=1, col=2, kind="HistogramGraph", graph_kwargs={}), - ), + ("{}_value".format(_t_name), dict(row=1, col=2, kind="HistogramGraph", graph_kwargs={}),), ] _default_xaxis = dict(showline=False, zeroline=True, tickangle=45) @@ -161,13 +143,7 @@ def _get_figure_with_position( [{"rowspan": 1}, None], ] subplots_kwargs = dict( - vertical_spacing=0.01, - rows=2, - cols=2, - row_width=[1, 2], - column_width=[3, 1], - print_grid=False, - specs=specs, + vertical_spacing=0.01, rows=2, cols=2, row_width=[1, 2], column_width=[3, 1], print_grid=False, specs=specs, ) yield SubplotsGraph( cum_return_df, diff --git a/qlib/contrib/report/analysis_position/parse_position.py b/qlib/contrib/report/analysis_position/parse_position.py index fe1d61137..23f9c592c 100644 --- a/qlib/contrib/report/analysis_position/parse_position.py +++ b/qlib/contrib/report/analysis_position/parse_position.py @@ -72,10 +72,7 @@ def parse_position(position: dict = None) -> pd.DataFrame: result_df = result_df.append(_trading_day_df, sort=True) - previous_data = dict( - date=_trading_date, - code_list=_trading_day_df[_trading_day_df["status"] != -1].index, - ) + previous_data = dict(date=_trading_date, code_list=_trading_day_df[_trading_day_df["status"] != -1].index,) result_df.reset_index(inplace=True) result_df.rename(columns={"date": "datetime", "index": "instrument"}, inplace=True) diff --git a/qlib/contrib/report/analysis_position/rank_label.py b/qlib/contrib/report/analysis_position/rank_label.py index 72a358adc..9a4d834ed 100644 --- a/qlib/contrib/report/analysis_position/rank_label.py +++ b/qlib/contrib/report/analysis_position/rank_label.py @@ -23,11 +23,7 @@ def _get_figure_with_position( :return: """ _position_df = get_position_data( - position, - label_data, - calculate_label_rank=True, - start_date=start_date, - end_date=end_date, + position, label_data, calculate_label_rank=True, start_date=start_date, end_date=end_date, ) res_dict = dict() @@ -51,20 +47,14 @@ def _get_figure_with_position( yield ScatterGraph( _res_df.loc[:, [_col]], layout=dict( - title=_col, - xaxis=dict(type="category", tickangle=45), - yaxis=dict(title="lable-rank-ratio: %"), + title=_col, xaxis=dict(type="category", tickangle=45), yaxis=dict(title="lable-rank-ratio: %"), ), graph_kwargs=dict(mode="lines+markers"), ).figure def rank_label_graph( - position: dict, - label_data: pd.DataFrame, - start_date=None, - end_date=None, - show_notebook=True, + position: dict, label_data: pd.DataFrame, start_date=None, end_date=None, show_notebook=True, ) -> Iterable[go.Figure]: """Ranking percentage of stocks buy, sell, and holding on the trading day. Average rank-ratio(similar to **sell_df['label'].rank(ascending=False) / len(sell_df)**) of daily trading diff --git a/qlib/contrib/report/analysis_position/report.py b/qlib/contrib/report/analysis_position/report.py index f82e654c4..8e2c05c0a 100644 --- a/qlib/contrib/report/analysis_position/report.py +++ b/qlib/contrib/report/analysis_position/report.py @@ -123,9 +123,7 @@ def _report_figure(df: pd.DataFrame) -> [list, tuple]: "y1": 1, "fillcolor": "#d3d3d3", "opacity": 0.3, - "line": { - "width": 0, - }, + "line": {"width": 0,}, }, { "type": "rect", @@ -137,20 +135,13 @@ def _report_figure(df: pd.DataFrame) -> [list, tuple]: "y1": 0.55, "fillcolor": "#d3d3d3", "opacity": 0.3, - "line": { - "width": 0, - }, + "line": {"width": 0,}, }, ], ) _subplot_kwargs = dict( - shared_xaxes=True, - vertical_spacing=0.01, - rows=7, - cols=1, - row_width=[1, 1, 1, 3, 1, 1, 3], - print_grid=False, + shared_xaxes=True, vertical_spacing=0.01, rows=7, cols=1, row_width=[1, 1, 1, 3, 1, 1, 3], print_grid=False, ) figure = SubplotsGraph( df=report_df, diff --git a/qlib/contrib/report/graph.py b/qlib/contrib/report/graph.py index 70e382fb1..dbbc41110 100644 --- a/qlib/contrib/report/graph.py +++ b/qlib/contrib/report/graph.py @@ -311,11 +311,7 @@ class SubplotsGraph: _temp_row_data = ( column_name, dict( - row=row, - col=col, - name=res_name, - kind=self._kind_map["kind"], - graph_kwargs=self._kind_map["kwargs"], + row=row, col=col, name=res_name, kind=self._kind_map["kind"], graph_kwargs=self._kind_map["kwargs"], ), ) self._sub_graph_data.append(_temp_row_data) diff --git a/qlib/contrib/strategy/cost_control.py b/qlib/contrib/strategy/cost_control.py index dd90437b0..ee3ee03ec 100644 --- a/qlib/contrib/strategy/cost_control.py +++ b/qlib/contrib/strategy/cost_control.py @@ -57,10 +57,7 @@ class SoftTopkStrategy(WeightStrategyBase): final_stock_weight[stock_id] -= sw if self.buy_method == "first_fill": for stock_id in buy_signal_stocks: - add_weight = min( - max(1 / self.topk - final_stock_weight.get(stock_id, 0), 0.0), - sold_stock_weight, - ) + add_weight = min(max(1 / self.topk - final_stock_weight.get(stock_id, 0), 0.0), sold_stock_weight,) final_stock_weight[stock_id] = final_stock_weight.get(stock_id, 0.0) + add_weight sold_stock_weight -= add_weight elif self.buy_method == "average_fill": diff --git a/qlib/contrib/strategy/order_generator.py b/qlib/contrib/strategy/order_generator.py index 494981ecc..6f168b4dd 100644 --- a/qlib/contrib/strategy/order_generator.py +++ b/qlib/contrib/strategy/order_generator.py @@ -102,14 +102,10 @@ class OrderGenWInteract(OrderGenerator): # strategy 1 : generate amount_position by weight_position # Use API in Exchange() target_amount_dict = trade_exchange.generate_amount_position_from_weight_position( - weight_position=target_weight_position, - cash=current_tradable_value, - trade_date=trade_date, + weight_position=target_weight_position, cash=current_tradable_value, trade_date=trade_date, ) order_list = trade_exchange.generate_order_for_target_amount_position( - target_position=target_amount_dict, - current_position=current_amount_dict, - trade_date=trade_date, + target_position=target_amount_dict, current_position=current_amount_dict, trade_date=trade_date, ) return order_list @@ -164,8 +160,6 @@ class OrderGenWOInteract(OrderGenerator): else: continue order_list = trade_exchange.generate_order_for_target_amount_position( - target_position=amount_dict, - current_position=current.get_stock_amount_dict(), - trade_date=trade_date, + target_position=amount_dict, current_position=current.get_stock_amount_dict(), trade_date=trade_date, ) return order_list diff --git a/qlib/contrib/tuner/launcher.py b/qlib/contrib/tuner/launcher.py index 711658c9a..409410a2a 100644 --- a/qlib/contrib/tuner/launcher.py +++ b/qlib/contrib/tuner/launcher.py @@ -13,11 +13,7 @@ from .config import TunerConfigManager args_parser = argparse.ArgumentParser(prog="tuner") args_parser.add_argument( - "-c", - "--config_path", - required=True, - type=str, - help="config path indicates where to load yaml config.", + "-c", "--config_path", required=True, type=str, help="config path indicates where to load yaml config.", ) args = args_parser.parse_args() diff --git a/qlib/contrib/tuner/space.py b/qlib/contrib/tuner/space.py index 76f101671..57f57a6c3 100644 --- a/qlib/contrib/tuner/space.py +++ b/qlib/contrib/tuner/space.py @@ -10,8 +10,5 @@ TopkAmountStrategySpace = { } QLibDataLabelSpace = { - "labels": hp.choice( - "labels", - [["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["Ref($close, -5)/$close - 1"]], - ) + "labels": hp.choice("labels", [["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["Ref($close, -5)/$close - 1"]],) } diff --git a/qlib/contrib/tuner/tuner.py b/qlib/contrib/tuner/tuner.py index 2ce957859..e81d41a9a 100644 --- a/qlib/contrib/tuner/tuner.py +++ b/qlib/contrib/tuner/tuner.py @@ -28,10 +28,7 @@ class Tuner: self.optim_config = optim_config self.max_evals = self.tuner_config.get("max_evals", 10) - self.ex_dir = os.path.join( - self.tuner_config["experiment"]["dir"], - self.tuner_config["experiment"]["name"], - ) + self.ex_dir = os.path.join(self.tuner_config["experiment"]["dir"], self.tuner_config["experiment"]["name"],) self.best_params = None self.best_res = None @@ -42,10 +39,7 @@ class Tuner: TimeInspector.set_time_mark() fmin( - fn=self.objective, - space=self.space, - algo=tpe.suggest, - max_evals=self.max_evals, + fn=self.objective, space=self.space, algo=tpe.suggest, max_evals=self.max_evals, ) self.logger.info("Local best params: {} ".format(self.best_params)) TimeInspector.log_cost_time( @@ -159,8 +153,7 @@ class QLibTuner(Tuner): estimator_config["data"]["args"].update(params["data_label_space"]) estimator_path = os.path.join( - self.tuner_config["experiment"].get("dir", "../"), - QLibTuner.ESTIMATOR_CONFIG_NAME, + self.tuner_config["experiment"].get("dir", "../"), QLibTuner.ESTIMATOR_CONFIG_NAME, ) with open(estimator_path, "w") as fp: @@ -173,27 +166,20 @@ class QLibTuner(Tuner): model_space_name = self.tuner_config["model"].get("space", None) if model_space_name is None: raise ValueError("Please give the search space of model.") - model_space = getattr( - importlib.import_module(".space", package="qlib.contrib.tuner"), - model_space_name, - ) + model_space = getattr(importlib.import_module(".space", package="qlib.contrib.tuner"), model_space_name,) # 2. Setup strategy space strategy_space_name = self.tuner_config["strategy"].get("space", None) if strategy_space_name is None: raise ValueError("Please give the search space of strategy.") - strategy_space = getattr( - importlib.import_module(".space", package="qlib.contrib.tuner"), - strategy_space_name, - ) + strategy_space = getattr(importlib.import_module(".space", package="qlib.contrib.tuner"), strategy_space_name,) # 3. Setup data label space if given if self.tuner_config.get("data_label", None) is not None: data_label_space_name = self.tuner_config["data_label"].get("space", None) if data_label_space_name is not None: data_label_space = getattr( - importlib.import_module(".space", package="qlib.contrib.tuner"), - data_label_space_name, + importlib.import_module(".space", package="qlib.contrib.tuner"), data_label_space_name, ) else: data_label_space_name = None diff --git a/qlib/data/client.py b/qlib/data/client.py index 5244a7e45..d1a68cb38 100644 --- a/qlib/data/client.py +++ b/qlib/data/client.py @@ -26,8 +26,7 @@ class Client: self.logger = get_module_logger(self.__class__.__name__) # bind connect/disconnect callbacks self.sio.on( - "connect", - lambda: self.logger.debug("Connect to server {}".format(self.sio.connection_url)), + "connect", lambda: self.logger.debug("Connect to server {}".format(self.sio.connection_url)), ) self.sio.on("disconnect", lambda: self.logger.debug("Disconnect from server!")) diff --git a/qlib/data/data.py b/qlib/data/data.py index 762467da3..47cded79c 100644 --- a/qlib/data/data.py +++ b/qlib/data/data.py @@ -328,14 +328,7 @@ class DatasetProvider(abc.ABC): raise NotImplementedError("Subclass of DatasetProvider must implement `Dataset` method") def _uri( - self, - instruments, - fields, - start_time=None, - end_time=None, - freq="day", - disk_cache=1, - **kwargs, + self, instruments, fields, start_time=None, end_time=None, freq="day", disk_cache=1, **kwargs, ): """Get task uri, used when generating rabbitmq task in qlib_server @@ -414,29 +407,13 @@ class DatasetProvider(abc.ABC): for inst, spans in instruments_d.items(): data[inst] = p.apply_async( DatasetProvider.expression_calculator, - args=( - inst, - start_time, - end_time, - freq, - normalize_column_names, - spans, - C, - ), + args=(inst, start_time, end_time, freq, normalize_column_names, spans, C,), ) else: for inst in instruments_d: data[inst] = p.apply_async( DatasetProvider.expression_calculator, - args=( - inst, - start_time, - end_time, - freq, - normalize_column_names, - None, - C, - ), + args=(inst, start_time, end_time, freq, normalize_column_names, None, C,), ) p.close() @@ -598,12 +575,7 @@ class LocalInstrumentProvider(InstrumentProvider): start_time = pd.Timestamp(start_time or cal[0]) end_time = pd.Timestamp(end_time or cal[-1]) _instruments_filtered = { - inst: list( - filter( - lambda x: x[0] <= x[1], - [(max(start_time, x[0]), min(end_time, x[1])) for x in spans], - ) - ) + inst: list(filter(lambda x: x[0] <= x[1], [(max(start_time, x[0]), min(end_time, x[1])) for x in spans],)) for inst, spans in _instruments.items() } _instruments_filtered = {key: value for key, value in _instruments_filtered.items() if value} @@ -723,14 +695,7 @@ class LocalDatasetProvider(DatasetProvider): for inst in instruments_d: p.apply_async( - LocalDatasetProvider.cache_walker, - args=( - inst, - start_time, - end_time, - freq, - column_names, - ), + LocalDatasetProvider.cache_walker, args=(inst, start_time, end_time, freq, column_names,), ) p.close() @@ -763,12 +728,7 @@ class ClientCalendarProvider(CalendarProvider): def calendar(self, start_time=None, end_time=None, freq="day", future=False): self.conn.send_request( request_type="calendar", - request_content={ - "start_time": str(start_time), - "end_time": str(end_time), - "freq": freq, - "future": future, - }, + request_content={"start_time": str(start_time), "end_time": str(end_time), "freq": freq, "future": future,}, msg_queue=self.queue, msg_proc_func=lambda response_content: [pd.Timestamp(c) for c in response_content], ) @@ -832,14 +792,7 @@ class ClientDatasetProvider(DatasetProvider): self.queue = queue.Queue() def dataset( - self, - instruments, - fields, - start_time=None, - end_time=None, - freq="day", - disk_cache=0, - return_uri=False, + self, instruments, fields, start_time=None, end_time=None, freq="day", disk_cache=0, return_uri=False, ): if Inst.get_inst_type(instruments) == Inst.DICT: get_module_logger("data").warning( @@ -942,13 +895,7 @@ class BaseProvider: return Inst.list_instruments(instruments, start_time, end_time, freq, as_list) def features( - self, - instruments, - fields, - start_time=None, - end_time=None, - freq="day", - disk_cache=None, + self, instruments, fields, start_time=None, end_time=None, freq="day", disk_cache=None, ): """ Parameters: diff --git a/qlib/data/dataset/utils.py b/qlib/data/dataset/utils.py index feda19044..58e2bd968 100644 --- a/qlib/data/dataset/utils.py +++ b/qlib/data/dataset/utils.py @@ -32,10 +32,7 @@ def get_level_index(df: pd.DataFrame, level=Union[str, int]) -> int: def fetch_df_by_index( - df: pd.DataFrame, - selector: Union[pd.Timestamp, slice, str, list], - level: Union[str, int], - fetch_orig=True, + df: pd.DataFrame, selector: Union[pd.Timestamp, slice, str, list], level: Union[str, int], fetch_orig=True, ) -> pd.DataFrame: """ fetch data from `data` with `selector` and `level` diff --git a/qlib/data/filter.py b/qlib/data/filter.py index 70f9d3278..811fd387f 100644 --- a/qlib/data/filter.py +++ b/qlib/data/filter.py @@ -341,12 +341,7 @@ class ExpressionDFilter(SeriesDFilter): # do not use dataset cache try: _features = DatasetD.dataset( - instruments, - [self.rule_expression], - fstart, - fend, - freq=self.filter_freq, - disk_cache=0, + instruments, [self.rule_expression], fstart, fend, freq=self.filter_freq, disk_cache=0, ) except TypeError: # use LocalDatasetProvider diff --git a/qlib/portfolio/__init__.py b/qlib/portfolio/__init__.py index b7c525821..59e481eb9 100644 --- a/qlib/portfolio/__init__.py +++ b/qlib/portfolio/__init__.py @@ -1,3 +1,2 @@ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. - diff --git a/qlib/tests/__init__.py b/qlib/tests/__init__.py index f92e72787..eb6f9c5ed 100644 --- a/qlib/tests/__init__.py +++ b/qlib/tests/__init__.py @@ -18,10 +18,6 @@ class TestAutoData(unittest.TestCase): print(f"Qlib data is not found in {provider_uri}") GetData().qlib_data( - name="qlib_data_simple", - region="cn", - interval="1d", - target_dir=provider_uri, - delete_old=False, + name="qlib_data_simple", region="cn", interval="1d", target_dir=provider_uri, delete_old=False, ) init(provider_uri=provider_uri, region=REG_CN, **cls._setup_kwargs) diff --git a/qlib/workflow/record_temp.py b/qlib/workflow/record_temp.py index be458a24d..0c704b896 100644 --- a/qlib/workflow/record_temp.py +++ b/qlib/workflow/record_temp.py @@ -193,10 +193,7 @@ class SigAnaRecord(SignalRecord): } ) objects.update( - { - "long_short_r.pkl": long_short_r, - "long_avg_r.pkl": long_avg_r, - } + {"long_short_r.pkl": long_short_r, "long_avg_r.pkl": long_avg_r,} ) self.recorder.log_metrics(**metrics) self.recorder.save_objects(**objects, artifact_path=self.get_path()) diff --git a/tests/test_all_pipeline.py b/tests/test_all_pipeline.py index f6e77cba4..8b3819c83 100644 --- a/tests/test_all_pipeline.py +++ b/tests/test_all_pipeline.py @@ -78,10 +78,7 @@ port_analysis_config = { "strategy": { "class": "TopkDropoutStrategy", "module_path": "qlib.contrib.strategy.strategy", - "kwargs": { - "topk": 50, - "n_drop": 5, - }, + "kwargs": {"topk": 50, "n_drop": 5,}, }, "backtest": { "verbose": False, @@ -176,9 +173,7 @@ class TestAllFlow(TestAutoData): def test_1_backtest(self): analyze_df = backtest_analysis(TestAllFlow.PRED_SCORE, TestAllFlow.RID) self.assertGreaterEqual( - analyze_df.loc(axis=0)["excess_return_with_cost", "annualized_return"].values[0], - 0.10, - "backtest failed", + analyze_df.loc(axis=0)["excess_return_with_cost", "annualized_return"].values[0], 0.10, "backtest failed", ) diff --git a/tests/test_dump_data.py b/tests/test_dump_data.py index dfa7f8556..de649c37e 100644 --- a/tests/test_dump_data.py +++ b/tests/test_dump_data.py @@ -40,9 +40,7 @@ class TestDumpData(unittest.TestCase): TestDumpData.STOCK_NAMES = list(map(lambda x: x.name[:-4].upper(), SOURCE_DIR.glob("*.csv"))) provider_uri = str(QLIB_DIR.resolve()) qlib.init( - provider_uri=provider_uri, - expression_cache=None, - dataset_cache=None, + provider_uri=provider_uri, expression_cache=None, dataset_cache=None, ) @classmethod @@ -54,10 +52,7 @@ class TestDumpData(unittest.TestCase): def test_1_dump_calendars(self): ori_calendars = set( - map( - pd.Timestamp, - pd.read_csv(QLIB_DIR.joinpath("calendars", "day.txt"), header=None).loc[:, 0].values, - ) + map(pd.Timestamp, pd.read_csv(QLIB_DIR.joinpath("calendars", "day.txt"), header=None).loc[:, 0].values,) ) res_calendars = set(D.calendar()) assert len(ori_calendars - res_calendars) == len(res_calendars - ori_calendars) == 0, "dump calendars failed" diff --git a/tests/test_get_data.py b/tests/test_get_data.py index c511d1b91..d5637b025 100644 --- a/tests/test_get_data.py +++ b/tests/test_get_data.py @@ -26,9 +26,7 @@ class TestGetData(unittest.TestCase): def setUpClass(cls) -> None: provider_uri = str(QLIB_DIR.resolve()) qlib.init( - provider_uri=provider_uri, - expression_cache=None, - dataset_cache=None, + provider_uri=provider_uri, expression_cache=None, dataset_cache=None, ) @classmethod diff --git a/tests/test_structured_cov_estimator.py b/tests/test_structured_cov_estimator.py index a3973be5a..494962cc3 100644 --- a/tests/test_structured_cov_estimator.py +++ b/tests/test_structured_cov_estimator.py @@ -33,7 +33,7 @@ class TestStructuredCovEstimator(unittest.TestCase): NUM_OBSERVATION = 200 EPS = 1e-6 - estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, nan_option='fill') + estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, nan_option="fill") X = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE) @@ -50,7 +50,7 @@ class TestStructuredCovEstimator(unittest.TestCase): NUM_VARIABLE = 10 NUM_OBSERVATION = 200 - estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, nan_option='fill') + estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, nan_option="fill") X = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE) From 53cf89d7c22f42234a452507cca67a98662e4ad9 Mon Sep 17 00:00:00 2001 From: Charles Young Date: Mon, 8 Mar 2021 19:43:03 +0800 Subject: [PATCH 083/134] Reformat with black. --- qlib/config.py | 24 +++++-- qlib/contrib/backtest/__init__.py | 18 ++++- qlib/contrib/backtest/profit_attribution.py | 23 +++++-- qlib/contrib/data/handler.py | 10 ++- qlib/contrib/eva/alpha.py | 6 +- qlib/contrib/evaluate.py | 7 +- qlib/contrib/evaluate_portfolio.py | 16 ++++- qlib/contrib/model/catboost_model.py | 4 +- qlib/contrib/model/pytorch_alstm.py | 21 ++++-- qlib/contrib/model/pytorch_alstm_ts.py | 17 +++-- qlib/contrib/model/pytorch_gats.py | 22 ++++-- qlib/contrib/model/pytorch_gats_ts.py | 18 ++++- qlib/contrib/model/pytorch_gru.py | 21 ++++-- qlib/contrib/model/pytorch_gru_ts.py | 17 ++++- qlib/contrib/model/pytorch_lstm.py | 21 ++++-- qlib/contrib/model/pytorch_lstm_ts.py | 17 ++++- qlib/contrib/model/pytorch_nn.py | 6 +- qlib/contrib/model/pytorch_sfm.py | 19 ++++- qlib/contrib/model/pytorch_tabnet.py | 14 +++- qlib/contrib/model/xgboost.py | 4 +- qlib/contrib/online/executor.py | 24 +++++-- qlib/contrib/online/manager.py | 6 +- qlib/contrib/online/operator.py | 8 ++- qlib/contrib/online/utils.py | 6 +- .../analysis_model_performance.py | 66 ++++++++++++++---- .../analysis_position/cumulative_return.py | 36 ++++++++-- .../analysis_position/parse_position.py | 5 +- .../report/analysis_position/rank_label.py | 16 ++++- .../report/analysis_position/report.py | 15 +++- qlib/contrib/report/graph.py | 6 +- qlib/contrib/strategy/cost_control.py | 5 +- qlib/contrib/strategy/order_generator.py | 12 +++- qlib/contrib/tuner/launcher.py | 6 +- qlib/contrib/tuner/space.py | 5 +- qlib/contrib/tuner/tuner.py | 26 +++++-- qlib/data/client.py | 3 +- qlib/data/data.py | 69 ++++++++++++++++--- qlib/data/dataset/utils.py | 5 +- qlib/data/filter.py | 7 +- qlib/portfolio/optimizer/enhanced_indexing.py | 6 +- qlib/tests/__init__.py | 6 +- qlib/workflow/record_temp.py | 5 +- tests/test_all_pipeline.py | 9 ++- tests/test_dump_data.py | 9 ++- tests/test_get_data.py | 4 +- 45 files changed, 548 insertions(+), 122 deletions(-) diff --git a/qlib/config.py b/qlib/config.py index 344eb8527..52b05568d 100644 --- a/qlib/config.py +++ b/qlib/config.py @@ -115,7 +115,12 @@ _default_config = { "format": "[%(process)s:%(threadName)s](%(asctime)s) %(levelname)s - %(name)s - [%(filename)s:%(lineno)d] - %(message)s" } }, - "filters": {"field_not_found": {"()": "qlib.log.LogFilter", "param": [".*?WARN: data not found for.*?"],}}, + "filters": { + "field_not_found": { + "()": "qlib.log.LogFilter", + "param": [".*?WARN: data not found for.*?"], + } + }, "handlers": { "console": { "class": "logging.StreamHandler", @@ -130,7 +135,10 @@ _default_config = { "exp_manager": { "class": "MLflowExpManager", "module_path": "qlib.workflow.expm", - "kwargs": {"uri": "file:" + str(Path(os.getcwd()).resolve() / "mlruns"), "default_exp_name": "Experiment",}, + "kwargs": { + "uri": "file:" + str(Path(os.getcwd()).resolve() / "mlruns"), + "default_exp_name": "Experiment", + }, }, } @@ -192,8 +200,16 @@ HIGH_FREQ_CONFIG = { } _default_region_config = { - REG_CN: {"trade_unit": 100, "limit_threshold": 0.099, "deal_price": "vwap",}, - REG_US: {"trade_unit": 1, "limit_threshold": None, "deal_price": "close",}, + REG_CN: { + "trade_unit": 100, + "limit_threshold": 0.099, + "deal_price": "vwap", + }, + REG_US: { + "trade_unit": 1, + "limit_threshold": None, + "deal_price": "close", + }, } diff --git a/qlib/contrib/backtest/__init__.py b/qlib/contrib/backtest/__init__.py index bd3494abf..aa24ffb0c 100644 --- a/qlib/contrib/backtest/__init__.py +++ b/qlib/contrib/backtest/__init__.py @@ -18,7 +18,13 @@ logger = get_module_logger("backtest caller") def get_strategy( - strategy=None, topk=50, margin=0.5, n_drop=5, risk_degree=0.95, str_type="dropout", adjust_dates=None, + strategy=None, + topk=50, + margin=0.5, + n_drop=5, + risk_degree=0.95, + str_type="dropout", + adjust_dates=None, ): """get_strategy @@ -69,7 +75,11 @@ def get_strategy( str_cls = getattr(strategy_pool, str_cls_dict.get(str_type)) strategy = str_cls( - topk=topk, buffer_margin=margin, n_drop=n_drop, risk_degree=risk_degree, adjust_dates=adjust_dates, + topk=topk, + buffer_margin=margin, + n_drop=n_drop, + risk_degree=risk_degree, + adjust_dates=adjust_dates, ) elif isinstance(strategy, (dict, str)): # 2) create strategy with init_instance_by_config @@ -162,7 +172,9 @@ def get_exchange( def get_executor( - executor=None, trade_exchange=None, verbose=True, + executor=None, + trade_exchange=None, + verbose=True, ): """get_executor diff --git a/qlib/contrib/backtest/profit_attribution.py b/qlib/contrib/backtest/profit_attribution.py index 355f06373..20c6f638f 100644 --- a/qlib/contrib/backtest/profit_attribution.py +++ b/qlib/contrib/backtest/profit_attribution.py @@ -12,7 +12,10 @@ from pathlib import Path def get_benchmark_weight( - bench, start_date=None, end_date=None, path=None, + bench, + start_date=None, + end_date=None, + path=None, ): """get_benchmark_weight @@ -213,7 +216,12 @@ def get_stock_group(stock_group_field_df, bench_stock_weight_df, group_method, g def brinson_pa( - positions, bench="SH000905", group_field="industry", group_method="category", group_n=None, deal_price="vwap", + positions, + bench="SH000905", + group_field="industry", + group_method="category", + group_n=None, + deal_price="vwap", ): """brinson profit attribution @@ -247,10 +255,17 @@ def brinson_pa( # suspend stock is NAN. So we have to get more date to forward fill the NAN shift_start_date = start_date - datetime.timedelta(days=250) instruments = D.list_instruments( - D.instruments(market="all"), start_time=shift_start_date, end_time=end_date, as_list=True, + D.instruments(market="all"), + start_time=shift_start_date, + end_time=end_date, + as_list=True, ) stock_df = D.features( - instruments, [group_field, deal_price], start_time=shift_start_date, end_time=end_date, freq="day", + instruments, + [group_field, deal_price], + start_time=shift_start_date, + end_time=end_date, + freq="day", ) stock_df.columns = [group_field, "deal_price"] diff --git a/qlib/contrib/data/handler.py b/qlib/contrib/data/handler.py index 574287819..970b032d6 100644 --- a/qlib/contrib/data/handler.py +++ b/qlib/contrib/data/handler.py @@ -21,7 +21,10 @@ def check_transform_proc(proc_l, fit_start_time, fit_end_time): fit_start_time is not None and fit_end_time is not None ), "Make sure `fit_start_time` and `fit_end_time` are not None." pkwargs.update( - {"fit_start_time": fit_start_time, "fit_end_time": fit_end_time,} + { + "fit_start_time": fit_start_time, + "fit_end_time": fit_end_time, + } ) new_l.append({"class": klass.__name__, "kwargs": pkwargs}) else: @@ -167,7 +170,10 @@ class Alpha158(DataHandlerLP): def get_feature_config(self): conf = { "kbar": {}, - "price": {"windows": [0], "feature": ["OPEN", "HIGH", "LOW", "VWAP"],}, + "price": { + "windows": [0], + "feature": ["OPEN", "HIGH", "LOW", "VWAP"], + }, "rolling": {}, } return self.parse_config_to_fields(conf) diff --git a/qlib/contrib/eva/alpha.py b/qlib/contrib/eva/alpha.py index 363a18458..c68571853 100644 --- a/qlib/contrib/eva/alpha.py +++ b/qlib/contrib/eva/alpha.py @@ -35,7 +35,11 @@ def calc_ic(pred: pd.Series, label: pd.Series, date_col="datetime", dropna=False def calc_long_short_return( - pred: pd.Series, label: pd.Series, date_col: str = "datetime", quantile: float = 0.2, dropna: bool = False, + pred: pd.Series, + label: pd.Series, + date_col: str = "datetime", + quantile: float = 0.2, + dropna: bool = False, ) -> Tuple[pd.Series, pd.Series]: """ calculate long-short return diff --git a/qlib/contrib/evaluate.py b/qlib/contrib/evaluate.py index 5cb1ce4eb..4aa5b5515 100644 --- a/qlib/contrib/evaluate.py +++ b/qlib/contrib/evaluate.py @@ -244,7 +244,12 @@ def long_short_backtest( short_returns[date] = np.mean(short_profit) + np.mean(all_profit) ls_returns[date] = np.mean(short_profit) + np.mean(long_profit) - return dict(zip(["long", "short", "long_short"], map(pd.Series, [long_returns, short_returns, ls_returns]),)) + return dict( + zip( + ["long", "short", "long_short"], + map(pd.Series, [long_returns, short_returns, ls_returns]), + ) + ) def t_run(): diff --git a/qlib/contrib/evaluate_portfolio.py b/qlib/contrib/evaluate_portfolio.py index 2d94105e4..04ddd8db0 100644 --- a/qlib/contrib/evaluate_portfolio.py +++ b/qlib/contrib/evaluate_portfolio.py @@ -64,7 +64,12 @@ def get_position_value(evaluate_date, position): instruments = list(set(instruments) - set(["cash"])) # filter 'cash' fields = ["$close"] close_data_df = D.features( - instruments, fields, start_time=evaluate_date, end_time=evaluate_date, freq="day", disk_cache=0, + instruments, + fields, + start_time=evaluate_date, + end_time=evaluate_date, + freq="day", + disk_cache=0, ) value = _get_position_value_from_df(evaluate_date, position, close_data_df) return value @@ -82,7 +87,14 @@ def get_position_list_value(positions): start_date, end_date = day_list[0], day_list[-1] # load data fields = ["$close"] - close_data_df = D.features(instruments, fields, start_time=start_date, end_time=end_date, freq="day", disk_cache=0,) + close_data_df = D.features( + instruments, + fields, + start_time=start_date, + end_time=end_date, + freq="day", + disk_cache=0, + ) # generate value # return dict for time:position_value value_dict = OrderedDict() diff --git a/qlib/contrib/model/catboost_model.py b/qlib/contrib/model/catboost_model.py index 2840c2cef..d57c32b70 100644 --- a/qlib/contrib/model/catboost_model.py +++ b/qlib/contrib/model/catboost_model.py @@ -32,7 +32,9 @@ class CatBoostModel(Model): **kwargs ): df_train, df_valid = dataset.prepare( - ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] diff --git a/qlib/contrib/model/pytorch_alstm.py b/qlib/contrib/model/pytorch_alstm.py index 306e68aad..bbbb61851 100644 --- a/qlib/contrib/model/pytorch_alstm.py +++ b/qlib/contrib/model/pytorch_alstm.py @@ -118,7 +118,10 @@ class ALSTM(Model): torch.manual_seed(self.seed) self.ALSTM_model = ALSTMModel( - d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, + d_feat=self.d_feat, + hidden_size=self.hidden_size, + num_layers=self.num_layers, + dropout=self.dropout, ) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.ALSTM_model.parameters(), lr=self.lr) @@ -208,11 +211,17 @@ class ALSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, + self, + dataset: DatasetH, + evals_result=dict(), + verbose=True, + save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -319,12 +328,14 @@ class ALSTMModel(nn.Module): self.fc_out = nn.Linear(in_features=self.hid_size * 2, out_features=1) self.att_net = nn.Sequential() self.att_net.add_module( - "att_fc_in", nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), + "att_fc_in", + nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), ) self.att_net.add_module("att_dropout", torch.nn.Dropout(self.dropout)) self.att_net.add_module("att_act", nn.Tanh()) self.att_net.add_module( - "att_fc_out", nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), + "att_fc_out", + nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), ) self.att_net.add_module("att_softmax", nn.Softmax(dim=1)) diff --git a/qlib/contrib/model/pytorch_alstm_ts.py b/qlib/contrib/model/pytorch_alstm_ts.py index 612bacbec..725568de8 100644 --- a/qlib/contrib/model/pytorch_alstm_ts.py +++ b/qlib/contrib/model/pytorch_alstm_ts.py @@ -123,7 +123,10 @@ class ALSTM(Model): torch.manual_seed(self.seed) self.ALSTM_model = ALSTMModel( - d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, + d_feat=self.d_feat, + hidden_size=self.hidden_size, + num_layers=self.num_layers, + dropout=self.dropout, ).to(self.device) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.ALSTM_model.parameters(), lr=self.lr) @@ -195,7 +198,11 @@ class ALSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset, evals_result=dict(), verbose=True, save_path=None, + self, + dataset, + evals_result=dict(), + verbose=True, + save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -302,12 +309,14 @@ class ALSTMModel(nn.Module): self.fc_out = nn.Linear(in_features=self.hid_size * 2, out_features=1) self.att_net = nn.Sequential() self.att_net.add_module( - "att_fc_in", nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), + "att_fc_in", + nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)), ) self.att_net.add_module("att_dropout", torch.nn.Dropout(self.dropout)) self.att_net.add_module("att_act", nn.Tanh()) self.att_net.add_module( - "att_fc_out", nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), + "att_fc_out", + nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False), ) self.att_net.add_module("att_softmax", nn.Softmax(dim=1)) diff --git a/qlib/contrib/model/pytorch_gats.py b/qlib/contrib/model/pytorch_gats.py index c59dc9197..07048e1bc 100644 --- a/qlib/contrib/model/pytorch_gats.py +++ b/qlib/contrib/model/pytorch_gats.py @@ -229,11 +229,17 @@ class GATs(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, + self, + dataset: DatasetH, + evals_result=dict(), + verbose=True, + save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -334,11 +340,19 @@ class GATModel(nn.Module): if base_model == "GRU": self.rnn = nn.GRU( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) elif base_model == "LSTM": self.rnn = nn.LSTM( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) else: raise ValueError("unknown base model name `%s`" % base_model) diff --git a/qlib/contrib/model/pytorch_gats_ts.py b/qlib/contrib/model/pytorch_gats_ts.py index dfc5f4ab5..1e94f56e4 100644 --- a/qlib/contrib/model/pytorch_gats_ts.py +++ b/qlib/contrib/model/pytorch_gats_ts.py @@ -242,7 +242,11 @@ class GATs(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset, evals_result=dict(), verbose=True, save_path=None, + self, + dataset, + evals_result=dict(), + verbose=True, + save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -357,11 +361,19 @@ class GATModel(nn.Module): if base_model == "GRU": self.rnn = nn.GRU( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) elif base_model == "LSTM": self.rnn = nn.LSTM( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) else: raise ValueError("unknown base model name `%s`" % base_model) diff --git a/qlib/contrib/model/pytorch_gru.py b/qlib/contrib/model/pytorch_gru.py index d2a774b65..84f863b9f 100755 --- a/qlib/contrib/model/pytorch_gru.py +++ b/qlib/contrib/model/pytorch_gru.py @@ -118,7 +118,10 @@ class GRU(Model): torch.manual_seed(self.seed) self.gru_model = GRUModel( - d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, + d_feat=self.d_feat, + hidden_size=self.hidden_size, + num_layers=self.num_layers, + dropout=self.dropout, ) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.gru_model.parameters(), lr=self.lr) @@ -208,11 +211,17 @@ class GRU(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, + self, + dataset: DatasetH, + evals_result=dict(), + verbose=True, + save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -296,7 +305,11 @@ class GRUModel(nn.Module): super().__init__() self.rnn = nn.GRU( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_gru_ts.py b/qlib/contrib/model/pytorch_gru_ts.py index 49f438cc3..bb6618b85 100755 --- a/qlib/contrib/model/pytorch_gru_ts.py +++ b/qlib/contrib/model/pytorch_gru_ts.py @@ -123,7 +123,10 @@ class GRU(Model): torch.manual_seed(self.seed) self.GRU_model = GRUModel( - d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, + d_feat=self.d_feat, + hidden_size=self.hidden_size, + num_layers=self.num_layers, + dropout=self.dropout, ).to(self.device) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.GRU_model.parameters(), lr=self.lr) @@ -195,7 +198,11 @@ class GRU(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset, evals_result=dict(), verbose=True, save_path=None, + self, + dataset, + evals_result=dict(), + verbose=True, + save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -279,7 +286,11 @@ class GRUModel(nn.Module): super().__init__() self.rnn = nn.GRU( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_lstm.py b/qlib/contrib/model/pytorch_lstm.py index 02ca16e36..163d500ec 100755 --- a/qlib/contrib/model/pytorch_lstm.py +++ b/qlib/contrib/model/pytorch_lstm.py @@ -118,7 +118,10 @@ class LSTM(Model): torch.manual_seed(self.seed) self.lstm_model = LSTMModel( - d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, + d_feat=self.d_feat, + hidden_size=self.hidden_size, + num_layers=self.num_layers, + dropout=self.dropout, ) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.lstm_model.parameters(), lr=self.lr) @@ -208,11 +211,17 @@ class LSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, + self, + dataset: DatasetH, + evals_result=dict(), + verbose=True, + save_path=None, ): df_train, df_valid, df_test = dataset.prepare( - ["train", "valid", "test"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid", "test"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] @@ -296,7 +305,11 @@ class LSTMModel(nn.Module): super().__init__() self.rnn = nn.LSTM( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_lstm_ts.py b/qlib/contrib/model/pytorch_lstm_ts.py index 2ec36f96e..cf4f8fb9f 100755 --- a/qlib/contrib/model/pytorch_lstm_ts.py +++ b/qlib/contrib/model/pytorch_lstm_ts.py @@ -123,7 +123,10 @@ class LSTM(Model): torch.manual_seed(self.seed) self.LSTM_model = LSTMModel( - d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout, + d_feat=self.d_feat, + hidden_size=self.hidden_size, + num_layers=self.num_layers, + dropout=self.dropout, ).to(self.device) if optimizer.lower() == "adam": self.train_optimizer = optim.Adam(self.LSTM_model.parameters(), lr=self.lr) @@ -195,7 +198,11 @@ class LSTM(Model): return np.mean(losses), np.mean(scores) def fit( - self, dataset, evals_result=dict(), verbose=True, save_path=None, + self, + dataset, + evals_result=dict(), + verbose=True, + save_path=None, ): dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L) @@ -279,7 +286,11 @@ class LSTMModel(nn.Module): super().__init__() self.rnn = nn.LSTM( - input_size=d_feat, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout, + input_size=d_feat, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + dropout=dropout, ) self.fc_out = nn.Linear(hidden_size, 1) diff --git a/qlib/contrib/model/pytorch_nn.py b/qlib/contrib/model/pytorch_nn.py index 8c1a77ec3..16fcea9ff 100644 --- a/qlib/contrib/model/pytorch_nn.py +++ b/qlib/contrib/model/pytorch_nn.py @@ -154,7 +154,11 @@ class DNNModelPytorch(Model): self.dnn_model.to(self.device) def fit( - self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, + self, + dataset: DatasetH, + evals_result=dict(), + verbose=True, + save_path=None, ): df_train, df_valid = dataset.prepare( ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L diff --git a/qlib/contrib/model/pytorch_sfm.py b/qlib/contrib/model/pytorch_sfm.py index 1f7433e05..d5169e6c7 100644 --- a/qlib/contrib/model/pytorch_sfm.py +++ b/qlib/contrib/model/pytorch_sfm.py @@ -30,7 +30,14 @@ from ...data.dataset.handler import DataHandlerLP class SFM_Model(nn.Module): def __init__( - self, d_feat=6, output_dim=1, freq_dim=10, hidden_size=64, dropout_W=0.0, dropout_U=0.0, device="cpu", + self, + d_feat=6, + output_dim=1, + freq_dim=10, + hidden_size=64, + dropout_W=0.0, + dropout_U=0.0, + device="cpu", ): super().__init__() @@ -355,11 +362,17 @@ class SFM(Model): self.train_optimizer.step() def fit( - self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, + self, + dataset: DatasetH, + evals_result=dict(), + verbose=True, + save_path=None, ): df_train, df_valid = dataset.prepare( - ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] diff --git a/qlib/contrib/model/pytorch_tabnet.py b/qlib/contrib/model/pytorch_tabnet.py index 18e9d8eb4..62e32d701 100644 --- a/qlib/contrib/model/pytorch_tabnet.py +++ b/qlib/contrib/model/pytorch_tabnet.py @@ -120,7 +120,9 @@ class TabnetModel(Model): os.makedirs("pretrain") [df_train, df_valid] = dataset.prepare( - ["pretrain", "pretrain_validation"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["pretrain", "pretrain_validation"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) df_train.fillna(df_train.mean(), inplace=True) @@ -154,7 +156,11 @@ class TabnetModel(Model): break def fit( - self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, + self, + dataset: DatasetH, + evals_result=dict(), + verbose=True, + save_path=None, ): if self.pretrain: # there is a pretrained model, load the model @@ -166,7 +172,9 @@ class TabnetModel(Model): # adding one more linear layer to fit the final output dimension self.tabnet_model = FinetuneModel(self.out_dim, self.final_out_dim, self.tabnet_model).to(self.device) df_train, df_valid = dataset.prepare( - ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) df_train.fillna(df_train.mean(), inplace=True) x_train, y_train = df_train["feature"], df_train["label"] diff --git a/qlib/contrib/model/xgboost.py b/qlib/contrib/model/xgboost.py index e37725c2e..ba2e5789b 100755 --- a/qlib/contrib/model/xgboost.py +++ b/qlib/contrib/model/xgboost.py @@ -29,7 +29,9 @@ class XGBModel(Model): ): df_train, df_valid = dataset.prepare( - ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L, + ["train", "valid"], + col_set=["feature", "label"], + data_key=DataHandlerLP.DK_L, ) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] diff --git a/qlib/contrib/online/executor.py b/qlib/contrib/online/executor.py index 52b868881..2bd0937a0 100644 --- a/qlib/contrib/online/executor.py +++ b/qlib/contrib/online/executor.py @@ -150,13 +150,21 @@ class SimulatorExecutor(BaseExecutor): if order.direction == Order.SELL: # sell print( "[I {:%Y-%m-%d}]: sell {}, price {:.2f}, amount {}, value {:.2f}.".format( - trade_date, order.stock_id, trade_price, order.deal_amount, trade_val, + trade_date, + order.stock_id, + trade_price, + order.deal_amount, + trade_val, ) ) else: print( "[I {:%Y-%m-%d}]: buy {}, price {:.2f}, amount {}, value {:.2f}.".format( - trade_date, order.stock_id, trade_price, order.deal_amount, trade_val, + trade_date, + order.stock_id, + trade_price, + order.deal_amount, + trade_val, ) ) @@ -263,13 +271,21 @@ def load_order_list(user_path, trade_date): for stock_id in order_dict["sell"]: amount, factor = order_dict["sell"][stock_id] order = Order( - stock_id=stock_id, amount=amount, trade_date=pd.Timestamp(trade_date), direction=Order.SELL, factor=factor, + stock_id=stock_id, + amount=amount, + trade_date=pd.Timestamp(trade_date), + direction=Order.SELL, + factor=factor, ) order_list.append(order) for stock_id in order_dict["buy"]: amount, factor = order_dict["buy"][stock_id] order = Order( - stock_id=stock_id, amount=amount, trade_date=pd.Timestamp(trade_date), direction=Order.BUY, factor=factor, + stock_id=stock_id, + amount=amount, + trade_date=pd.Timestamp(trade_date), + direction=Order.BUY, + factor=factor, ) order_list.append(order) return order_list diff --git a/qlib/contrib/online/manager.py b/qlib/contrib/online/manager.py index a4476709d..cf850b9da 100644 --- a/qlib/contrib/online/manager.py +++ b/qlib/contrib/online/manager.py @@ -84,10 +84,12 @@ class UserManager: raise ValueError("Cannot find user {}".format(user_id)) self.users[user_id].account.save_account(self.data_path / user_id) save_instance( - self.users[user_id].strategy, self.data_path / user_id / "strategy_{}.pickle".format(user_id), + self.users[user_id].strategy, + self.data_path / user_id / "strategy_{}.pickle".format(user_id), ) save_instance( - self.users[user_id].model, self.data_path / user_id / "model_{}.pickle".format(user_id), + self.users[user_id].model, + self.data_path / user_id / "model_{}.pickle".format(user_id), ) def add_user(self, user_id, config_file, add_date): diff --git a/qlib/contrib/online/operator.py b/qlib/contrib/online/operator.py index c82deb394..c8b44f578 100644 --- a/qlib/contrib/online/operator.py +++ b/qlib/contrib/online/operator.py @@ -125,7 +125,9 @@ class Operator: trade_date=trade_date, ) save_order_list( - order_list=order_list, user_path=(pathlib.Path(path) / user_id), trade_date=trade_date, + order_list=order_list, + user_path=(pathlib.Path(path) / user_id), + trade_date=trade_date, ) self.logger.info("Generate order list at {} for {}".format(trade_date, user_id)) um.save_user_data(user_id) @@ -158,7 +160,9 @@ class Operator: order_list = load_order_list(user_path=(pathlib.Path(path) / user_id), trade_date=trade_date) trade_info = executor.execute(order_list=order_list, trade_account=user.account, trade_date=trade_date) executor.save_executed_file_from_trade_info( - trade_info=trade_info, user_path=(pathlib.Path(path) / user_id), trade_date=trade_date, + trade_info=trade_info, + user_path=(pathlib.Path(path) / user_id), + trade_date=trade_date, ) self.logger.info("execute order list at {} for {}".format(trade_date.date(), user_id)) diff --git a/qlib/contrib/online/utils.py b/qlib/contrib/online/utils.py index fb96c87bd..611af63e4 100644 --- a/qlib/contrib/online/utils.py +++ b/qlib/contrib/online/utils.py @@ -79,7 +79,11 @@ def prepare(um, today, user_id, exchange_config=None): log.warning("user_id:{}, last trading date {} after today {}".format(user_id, latest_trading_date, today)) return [pd.Timestamp(latest_trading_date)], None - dates = D.calendar(start_time=pd.Timestamp(latest_trading_date), end_time=pd.Timestamp(today), future=True,) + dates = D.calendar( + start_time=pd.Timestamp(latest_trading_date), + end_time=pd.Timestamp(today), + future=True, + ) dates = list(dates) dates.append(get_next_trading_date(dates[-1], future=True)) if exchange_config: diff --git a/qlib/contrib/report/analysis_model/analysis_model_performance.py b/qlib/contrib/report/analysis_model/analysis_model_performance.py index ef1447a12..1cb14d261 100644 --- a/qlib/contrib/report/analysis_model/analysis_model_performance.py +++ b/qlib/contrib/report/analysis_model/analysis_model_performance.py @@ -53,7 +53,8 @@ def _group_return(pred_label: pd.DataFrame = None, reverse: bool = False, N: int t_df.index = t_df.index.strftime("%Y-%m-%d") # Cumulative Return By Group group_scatter_figure = ScatterGraph( - t_df.cumsum(), layout=dict(title="Cumulative Return", xaxis=dict(type="category", tickangle=45)), + t_df.cumsum(), + layout=dict(title="Cumulative Return", xaxis=dict(type="category", tickangle=45)), ).figure t_df = t_df.loc[:, ["long-short", "long-average"]] @@ -61,7 +62,12 @@ def _group_return(pred_label: pd.DataFrame = None, reverse: bool = False, N: int group_hist_figure = SubplotsGraph( t_df, kind_map=dict(kind="DistplotGraph", kwargs=dict(bin_size=_bin_size)), - subplots_kwargs=dict(rows=1, cols=2, print_grid=False, subplot_titles=["long-short", "long-average"],), + subplots_kwargs=dict( + rows=1, + cols=2, + print_grid=False, + subplot_titles=["long-short", "long-average"], + ), ).figure return group_scatter_figure, group_hist_figure @@ -96,12 +102,15 @@ def _pred_ic(pred_label: pd.DataFrame = None, rank: bool = False, **kwargs) -> t _index = ic.index.get_level_values(0).astype("str").str.replace("-", "").str.slice(0, 6) _monthly_ic = ic.groupby(_index).mean() _monthly_ic.index = pd.MultiIndex.from_arrays( - [_monthly_ic.index.str.slice(0, 4), _monthly_ic.index.str.slice(4, 6)], names=["year", "month"], + [_monthly_ic.index.str.slice(0, 4), _monthly_ic.index.str.slice(4, 6)], + names=["year", "month"], ) # fill month _month_list = pd.date_range( - start=pd.Timestamp(f"{_index.min()[:4]}0101"), end=pd.Timestamp(f"{_index.max()[:4]}1231"), freq="1M", + start=pd.Timestamp(f"{_index.min()[:4]}0101"), + end=pd.Timestamp(f"{_index.max()[:4]}1231"), + freq="1M", ) _years = [] _month = [] @@ -133,15 +142,32 @@ def _pred_ic(pred_label: pd.DataFrame = None, rank: bool = False, **kwargs) -> t _bin_size = ((_ic_df.max() - _ic_df.min()) / 20).min() _sub_graph_data = [ - ("ic", dict(row=1, col=1, name="", kind="DistplotGraph", graph_kwargs=dict(bin_size=_bin_size),),), + ( + "ic", + dict( + row=1, + col=1, + name="", + kind="DistplotGraph", + graph_kwargs=dict(bin_size=_bin_size), + ), + ), (_qqplot_fig, dict(row=1, col=2)), ] ic_hist_figure = SubplotsGraph( _ic_df.dropna(), kind_map=dict(kind="HistogramGraph", kwargs=dict()), - subplots_kwargs=dict(rows=1, cols=2, print_grid=False, subplot_titles=["IC", "IC %s Dist. Q-Q" % dist_name],), + subplots_kwargs=dict( + rows=1, + cols=2, + print_grid=False, + subplot_titles=["IC", "IC %s Dist. Q-Q" % dist_name], + ), sub_graph_data=_sub_graph_data, - layout=dict(yaxis2=dict(title="Observed Quantile"), xaxis2=dict(title=f"{dist_name} Distribution Quantile"),), + layout=dict( + yaxis2=dict(title="Observed Quantile"), + xaxis2=dict(title=f"{dist_name} Distribution Quantile"), + ), ).figure return ic_bar_figure, ic_heatmap_figure, ic_hist_figure @@ -155,7 +181,8 @@ def _pred_autocorr(pred_label: pd.DataFrame, lag=1, **kwargs) -> tuple: _df = ac.to_frame("value") _df.index = _df.index.strftime("%Y-%m-%d") ac_figure = ScatterGraph( - _df, layout=dict(title="Auto Correlation", xaxis=dict(type="category", tickangle=45)), + _df, + layout=dict(title="Auto Correlation", xaxis=dict(type="category", tickangle=45)), ).figure return (ac_figure,) @@ -175,11 +202,17 @@ def _pred_turnover(pred_label: pd.DataFrame, N=5, lag=1, **kwargs) -> tuple: .sum() / (len(x) // N) ) - r_df = pd.DataFrame({"Top": top, "Bottom": bottom,}) + r_df = pd.DataFrame( + { + "Top": top, + "Bottom": bottom, + } + ) # FIXME: support HIGH-FREQ r_df.index = r_df.index.strftime("%Y-%m-%d") turnover_figure = ScatterGraph( - r_df, layout=dict(title="Top-Bottom Turnover", xaxis=dict(type="category", tickangle=45)), + r_df, + layout=dict(title="Top-Bottom Turnover", xaxis=dict(type="category", tickangle=45)), ).figure return (turnover_figure,) @@ -197,7 +230,11 @@ def ic_figure(ic_df: pd.DataFrame, show_nature_day=True, **kwargs) -> go.Figure: # FIXME: support HIGH-FREQ ic_df.index = ic_df.index.strftime("%Y-%m-%d") ic_bar_figure = BarGraph( - ic_df, layout=dict(title="Information Coefficient (IC)", xaxis=dict(type="category", tickangle=45),), + ic_df, + layout=dict( + title="Information Coefficient (IC)", + xaxis=dict(type="category", tickangle=45), + ), ).figure return ic_bar_figure @@ -240,7 +277,12 @@ def model_performance_graph( figure_list = [] for graph_name in graph_names: fun_res = eval(f"_{graph_name}")( - pred_label=pred_label, lag=lag, N=N, reverse=reverse, rank=rank, show_nature_day=show_nature_day, + pred_label=pred_label, + lag=lag, + N=N, + reverse=reverse, + rank=rank, + show_nature_day=show_nature_day, ) figure_list += fun_res diff --git a/qlib/contrib/report/analysis_position/cumulative_return.py b/qlib/contrib/report/analysis_position/cumulative_return.py index 604189c94..abb68ea60 100644 --- a/qlib/contrib/report/analysis_position/cumulative_return.py +++ b/qlib/contrib/report/analysis_position/cumulative_return.py @@ -13,7 +13,11 @@ from ..analysis_position.parse_position import get_position_data def _get_cum_return_data_with_position( - position: dict, report_normal: pd.DataFrame, label_data: pd.DataFrame, start_date=None, end_date=None, + position: dict, + report_normal: pd.DataFrame, + label_data: pd.DataFrame, + start_date=None, + end_date=None, ): """ @@ -25,7 +29,11 @@ def _get_cum_return_data_with_position( :return: """ _cumulative_return_df = get_position_data( - position=position, report_normal=report_normal, label_data=label_data, start_date=start_date, end_date=end_date, + position=position, + report_normal=report_normal, + label_data=label_data, + start_date=start_date, + end_date=end_date, ).copy() _cumulative_return_df["label"] = _cumulative_return_df["label"] - _cumulative_return_df["bench"] @@ -79,7 +87,11 @@ def _get_cum_return_data_with_position( def _get_figure_with_position( - position: dict, report_normal: pd.DataFrame, label_data: pd.DataFrame, start_date=None, end_date=None, + position: dict, + report_normal: pd.DataFrame, + label_data: pd.DataFrame, + start_date=None, + end_date=None, ) -> Iterable[go.Figure]: """Get average analysis figures @@ -99,12 +111,18 @@ def _get_figure_with_position( # Create figures for _t_name in ["buy", "sell", "buy_minus_sell", "hold"]: sub_graph_data = [ - ("cum_{}".format(_t_name), dict(row=1, col=1, graph_kwargs={"mode": "lines+markers", "xaxis": "x3"}),), + ( + "cum_{}".format(_t_name), + dict(row=1, col=1, graph_kwargs={"mode": "lines+markers", "xaxis": "x3"}), + ), ( "{}_weight".format(_t_name.replace("minus", "plus") if "minus" in _t_name else _t_name), dict(row=2, col=1), ), - ("{}_value".format(_t_name), dict(row=1, col=2, kind="HistogramGraph", graph_kwargs={}),), + ( + "{}_value".format(_t_name), + dict(row=1, col=2, kind="HistogramGraph", graph_kwargs={}), + ), ] _default_xaxis = dict(showline=False, zeroline=True, tickangle=45) @@ -143,7 +161,13 @@ def _get_figure_with_position( [{"rowspan": 1}, None], ] subplots_kwargs = dict( - vertical_spacing=0.01, rows=2, cols=2, row_width=[1, 2], column_width=[3, 1], print_grid=False, specs=specs, + vertical_spacing=0.01, + rows=2, + cols=2, + row_width=[1, 2], + column_width=[3, 1], + print_grid=False, + specs=specs, ) yield SubplotsGraph( cum_return_df, diff --git a/qlib/contrib/report/analysis_position/parse_position.py b/qlib/contrib/report/analysis_position/parse_position.py index 23f9c592c..fe1d61137 100644 --- a/qlib/contrib/report/analysis_position/parse_position.py +++ b/qlib/contrib/report/analysis_position/parse_position.py @@ -72,7 +72,10 @@ def parse_position(position: dict = None) -> pd.DataFrame: result_df = result_df.append(_trading_day_df, sort=True) - previous_data = dict(date=_trading_date, code_list=_trading_day_df[_trading_day_df["status"] != -1].index,) + previous_data = dict( + date=_trading_date, + code_list=_trading_day_df[_trading_day_df["status"] != -1].index, + ) result_df.reset_index(inplace=True) result_df.rename(columns={"date": "datetime", "index": "instrument"}, inplace=True) diff --git a/qlib/contrib/report/analysis_position/rank_label.py b/qlib/contrib/report/analysis_position/rank_label.py index 9a4d834ed..72a358adc 100644 --- a/qlib/contrib/report/analysis_position/rank_label.py +++ b/qlib/contrib/report/analysis_position/rank_label.py @@ -23,7 +23,11 @@ def _get_figure_with_position( :return: """ _position_df = get_position_data( - position, label_data, calculate_label_rank=True, start_date=start_date, end_date=end_date, + position, + label_data, + calculate_label_rank=True, + start_date=start_date, + end_date=end_date, ) res_dict = dict() @@ -47,14 +51,20 @@ def _get_figure_with_position( yield ScatterGraph( _res_df.loc[:, [_col]], layout=dict( - title=_col, xaxis=dict(type="category", tickangle=45), yaxis=dict(title="lable-rank-ratio: %"), + title=_col, + xaxis=dict(type="category", tickangle=45), + yaxis=dict(title="lable-rank-ratio: %"), ), graph_kwargs=dict(mode="lines+markers"), ).figure def rank_label_graph( - position: dict, label_data: pd.DataFrame, start_date=None, end_date=None, show_notebook=True, + position: dict, + label_data: pd.DataFrame, + start_date=None, + end_date=None, + show_notebook=True, ) -> Iterable[go.Figure]: """Ranking percentage of stocks buy, sell, and holding on the trading day. Average rank-ratio(similar to **sell_df['label'].rank(ascending=False) / len(sell_df)**) of daily trading diff --git a/qlib/contrib/report/analysis_position/report.py b/qlib/contrib/report/analysis_position/report.py index 8e2c05c0a..f82e654c4 100644 --- a/qlib/contrib/report/analysis_position/report.py +++ b/qlib/contrib/report/analysis_position/report.py @@ -123,7 +123,9 @@ def _report_figure(df: pd.DataFrame) -> [list, tuple]: "y1": 1, "fillcolor": "#d3d3d3", "opacity": 0.3, - "line": {"width": 0,}, + "line": { + "width": 0, + }, }, { "type": "rect", @@ -135,13 +137,20 @@ def _report_figure(df: pd.DataFrame) -> [list, tuple]: "y1": 0.55, "fillcolor": "#d3d3d3", "opacity": 0.3, - "line": {"width": 0,}, + "line": { + "width": 0, + }, }, ], ) _subplot_kwargs = dict( - shared_xaxes=True, vertical_spacing=0.01, rows=7, cols=1, row_width=[1, 1, 1, 3, 1, 1, 3], print_grid=False, + shared_xaxes=True, + vertical_spacing=0.01, + rows=7, + cols=1, + row_width=[1, 1, 1, 3, 1, 1, 3], + print_grid=False, ) figure = SubplotsGraph( df=report_df, diff --git a/qlib/contrib/report/graph.py b/qlib/contrib/report/graph.py index dbbc41110..70e382fb1 100644 --- a/qlib/contrib/report/graph.py +++ b/qlib/contrib/report/graph.py @@ -311,7 +311,11 @@ class SubplotsGraph: _temp_row_data = ( column_name, dict( - row=row, col=col, name=res_name, kind=self._kind_map["kind"], graph_kwargs=self._kind_map["kwargs"], + row=row, + col=col, + name=res_name, + kind=self._kind_map["kind"], + graph_kwargs=self._kind_map["kwargs"], ), ) self._sub_graph_data.append(_temp_row_data) diff --git a/qlib/contrib/strategy/cost_control.py b/qlib/contrib/strategy/cost_control.py index ee3ee03ec..dd90437b0 100644 --- a/qlib/contrib/strategy/cost_control.py +++ b/qlib/contrib/strategy/cost_control.py @@ -57,7 +57,10 @@ class SoftTopkStrategy(WeightStrategyBase): final_stock_weight[stock_id] -= sw if self.buy_method == "first_fill": for stock_id in buy_signal_stocks: - add_weight = min(max(1 / self.topk - final_stock_weight.get(stock_id, 0), 0.0), sold_stock_weight,) + add_weight = min( + max(1 / self.topk - final_stock_weight.get(stock_id, 0), 0.0), + sold_stock_weight, + ) final_stock_weight[stock_id] = final_stock_weight.get(stock_id, 0.0) + add_weight sold_stock_weight -= add_weight elif self.buy_method == "average_fill": diff --git a/qlib/contrib/strategy/order_generator.py b/qlib/contrib/strategy/order_generator.py index 6f168b4dd..494981ecc 100644 --- a/qlib/contrib/strategy/order_generator.py +++ b/qlib/contrib/strategy/order_generator.py @@ -102,10 +102,14 @@ class OrderGenWInteract(OrderGenerator): # strategy 1 : generate amount_position by weight_position # Use API in Exchange() target_amount_dict = trade_exchange.generate_amount_position_from_weight_position( - weight_position=target_weight_position, cash=current_tradable_value, trade_date=trade_date, + weight_position=target_weight_position, + cash=current_tradable_value, + trade_date=trade_date, ) order_list = trade_exchange.generate_order_for_target_amount_position( - target_position=target_amount_dict, current_position=current_amount_dict, trade_date=trade_date, + target_position=target_amount_dict, + current_position=current_amount_dict, + trade_date=trade_date, ) return order_list @@ -160,6 +164,8 @@ class OrderGenWOInteract(OrderGenerator): else: continue order_list = trade_exchange.generate_order_for_target_amount_position( - target_position=amount_dict, current_position=current.get_stock_amount_dict(), trade_date=trade_date, + target_position=amount_dict, + current_position=current.get_stock_amount_dict(), + trade_date=trade_date, ) return order_list diff --git a/qlib/contrib/tuner/launcher.py b/qlib/contrib/tuner/launcher.py index 409410a2a..711658c9a 100644 --- a/qlib/contrib/tuner/launcher.py +++ b/qlib/contrib/tuner/launcher.py @@ -13,7 +13,11 @@ from .config import TunerConfigManager args_parser = argparse.ArgumentParser(prog="tuner") args_parser.add_argument( - "-c", "--config_path", required=True, type=str, help="config path indicates where to load yaml config.", + "-c", + "--config_path", + required=True, + type=str, + help="config path indicates where to load yaml config.", ) args = args_parser.parse_args() diff --git a/qlib/contrib/tuner/space.py b/qlib/contrib/tuner/space.py index 57f57a6c3..76f101671 100644 --- a/qlib/contrib/tuner/space.py +++ b/qlib/contrib/tuner/space.py @@ -10,5 +10,8 @@ TopkAmountStrategySpace = { } QLibDataLabelSpace = { - "labels": hp.choice("labels", [["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["Ref($close, -5)/$close - 1"]],) + "labels": hp.choice( + "labels", + [["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["Ref($close, -5)/$close - 1"]], + ) } diff --git a/qlib/contrib/tuner/tuner.py b/qlib/contrib/tuner/tuner.py index e81d41a9a..2ce957859 100644 --- a/qlib/contrib/tuner/tuner.py +++ b/qlib/contrib/tuner/tuner.py @@ -28,7 +28,10 @@ class Tuner: self.optim_config = optim_config self.max_evals = self.tuner_config.get("max_evals", 10) - self.ex_dir = os.path.join(self.tuner_config["experiment"]["dir"], self.tuner_config["experiment"]["name"],) + self.ex_dir = os.path.join( + self.tuner_config["experiment"]["dir"], + self.tuner_config["experiment"]["name"], + ) self.best_params = None self.best_res = None @@ -39,7 +42,10 @@ class Tuner: TimeInspector.set_time_mark() fmin( - fn=self.objective, space=self.space, algo=tpe.suggest, max_evals=self.max_evals, + fn=self.objective, + space=self.space, + algo=tpe.suggest, + max_evals=self.max_evals, ) self.logger.info("Local best params: {} ".format(self.best_params)) TimeInspector.log_cost_time( @@ -153,7 +159,8 @@ class QLibTuner(Tuner): estimator_config["data"]["args"].update(params["data_label_space"]) estimator_path = os.path.join( - self.tuner_config["experiment"].get("dir", "../"), QLibTuner.ESTIMATOR_CONFIG_NAME, + self.tuner_config["experiment"].get("dir", "../"), + QLibTuner.ESTIMATOR_CONFIG_NAME, ) with open(estimator_path, "w") as fp: @@ -166,20 +173,27 @@ class QLibTuner(Tuner): model_space_name = self.tuner_config["model"].get("space", None) if model_space_name is None: raise ValueError("Please give the search space of model.") - model_space = getattr(importlib.import_module(".space", package="qlib.contrib.tuner"), model_space_name,) + model_space = getattr( + importlib.import_module(".space", package="qlib.contrib.tuner"), + model_space_name, + ) # 2. Setup strategy space strategy_space_name = self.tuner_config["strategy"].get("space", None) if strategy_space_name is None: raise ValueError("Please give the search space of strategy.") - strategy_space = getattr(importlib.import_module(".space", package="qlib.contrib.tuner"), strategy_space_name,) + strategy_space = getattr( + importlib.import_module(".space", package="qlib.contrib.tuner"), + strategy_space_name, + ) # 3. Setup data label space if given if self.tuner_config.get("data_label", None) is not None: data_label_space_name = self.tuner_config["data_label"].get("space", None) if data_label_space_name is not None: data_label_space = getattr( - importlib.import_module(".space", package="qlib.contrib.tuner"), data_label_space_name, + importlib.import_module(".space", package="qlib.contrib.tuner"), + data_label_space_name, ) else: data_label_space_name = None diff --git a/qlib/data/client.py b/qlib/data/client.py index d1a68cb38..5244a7e45 100644 --- a/qlib/data/client.py +++ b/qlib/data/client.py @@ -26,7 +26,8 @@ class Client: self.logger = get_module_logger(self.__class__.__name__) # bind connect/disconnect callbacks self.sio.on( - "connect", lambda: self.logger.debug("Connect to server {}".format(self.sio.connection_url)), + "connect", + lambda: self.logger.debug("Connect to server {}".format(self.sio.connection_url)), ) self.sio.on("disconnect", lambda: self.logger.debug("Disconnect from server!")) diff --git a/qlib/data/data.py b/qlib/data/data.py index 47cded79c..762467da3 100644 --- a/qlib/data/data.py +++ b/qlib/data/data.py @@ -328,7 +328,14 @@ class DatasetProvider(abc.ABC): raise NotImplementedError("Subclass of DatasetProvider must implement `Dataset` method") def _uri( - self, instruments, fields, start_time=None, end_time=None, freq="day", disk_cache=1, **kwargs, + self, + instruments, + fields, + start_time=None, + end_time=None, + freq="day", + disk_cache=1, + **kwargs, ): """Get task uri, used when generating rabbitmq task in qlib_server @@ -407,13 +414,29 @@ class DatasetProvider(abc.ABC): for inst, spans in instruments_d.items(): data[inst] = p.apply_async( DatasetProvider.expression_calculator, - args=(inst, start_time, end_time, freq, normalize_column_names, spans, C,), + args=( + inst, + start_time, + end_time, + freq, + normalize_column_names, + spans, + C, + ), ) else: for inst in instruments_d: data[inst] = p.apply_async( DatasetProvider.expression_calculator, - args=(inst, start_time, end_time, freq, normalize_column_names, None, C,), + args=( + inst, + start_time, + end_time, + freq, + normalize_column_names, + None, + C, + ), ) p.close() @@ -575,7 +598,12 @@ class LocalInstrumentProvider(InstrumentProvider): start_time = pd.Timestamp(start_time or cal[0]) end_time = pd.Timestamp(end_time or cal[-1]) _instruments_filtered = { - inst: list(filter(lambda x: x[0] <= x[1], [(max(start_time, x[0]), min(end_time, x[1])) for x in spans],)) + inst: list( + filter( + lambda x: x[0] <= x[1], + [(max(start_time, x[0]), min(end_time, x[1])) for x in spans], + ) + ) for inst, spans in _instruments.items() } _instruments_filtered = {key: value for key, value in _instruments_filtered.items() if value} @@ -695,7 +723,14 @@ class LocalDatasetProvider(DatasetProvider): for inst in instruments_d: p.apply_async( - LocalDatasetProvider.cache_walker, args=(inst, start_time, end_time, freq, column_names,), + LocalDatasetProvider.cache_walker, + args=( + inst, + start_time, + end_time, + freq, + column_names, + ), ) p.close() @@ -728,7 +763,12 @@ class ClientCalendarProvider(CalendarProvider): def calendar(self, start_time=None, end_time=None, freq="day", future=False): self.conn.send_request( request_type="calendar", - request_content={"start_time": str(start_time), "end_time": str(end_time), "freq": freq, "future": future,}, + request_content={ + "start_time": str(start_time), + "end_time": str(end_time), + "freq": freq, + "future": future, + }, msg_queue=self.queue, msg_proc_func=lambda response_content: [pd.Timestamp(c) for c in response_content], ) @@ -792,7 +832,14 @@ class ClientDatasetProvider(DatasetProvider): self.queue = queue.Queue() def dataset( - self, instruments, fields, start_time=None, end_time=None, freq="day", disk_cache=0, return_uri=False, + self, + instruments, + fields, + start_time=None, + end_time=None, + freq="day", + disk_cache=0, + return_uri=False, ): if Inst.get_inst_type(instruments) == Inst.DICT: get_module_logger("data").warning( @@ -895,7 +942,13 @@ class BaseProvider: return Inst.list_instruments(instruments, start_time, end_time, freq, as_list) def features( - self, instruments, fields, start_time=None, end_time=None, freq="day", disk_cache=None, + self, + instruments, + fields, + start_time=None, + end_time=None, + freq="day", + disk_cache=None, ): """ Parameters: diff --git a/qlib/data/dataset/utils.py b/qlib/data/dataset/utils.py index 58e2bd968..feda19044 100644 --- a/qlib/data/dataset/utils.py +++ b/qlib/data/dataset/utils.py @@ -32,7 +32,10 @@ def get_level_index(df: pd.DataFrame, level=Union[str, int]) -> int: def fetch_df_by_index( - df: pd.DataFrame, selector: Union[pd.Timestamp, slice, str, list], level: Union[str, int], fetch_orig=True, + df: pd.DataFrame, + selector: Union[pd.Timestamp, slice, str, list], + level: Union[str, int], + fetch_orig=True, ) -> pd.DataFrame: """ fetch data from `data` with `selector` and `level` diff --git a/qlib/data/filter.py b/qlib/data/filter.py index 811fd387f..70f9d3278 100644 --- a/qlib/data/filter.py +++ b/qlib/data/filter.py @@ -341,7 +341,12 @@ class ExpressionDFilter(SeriesDFilter): # do not use dataset cache try: _features = DatasetD.dataset( - instruments, [self.rule_expression], fstart, fend, freq=self.filter_freq, disk_cache=0, + instruments, + [self.rule_expression], + fstart, + fend, + freq=self.filter_freq, + disk_cache=0, ) except TypeError: # use LocalDatasetProvider diff --git a/qlib/portfolio/optimizer/enhanced_indexing.py b/qlib/portfolio/optimizer/enhanced_indexing.py index 5fdc1014d..5a7a0804d 100644 --- a/qlib/portfolio/optimizer/enhanced_indexing.py +++ b/qlib/portfolio/optimizer/enhanced_indexing.py @@ -56,7 +56,11 @@ class EnhancedIndexingOptimizer(BaseOptimizer): assert inds_dev is None or inds_dev >= 0, "industry deviation limit `inds_dev` should be positive or None." self.inds_dev = inds_dev - assert warm_start in [None, self.START_FROM_W0, self.START_FROM_BENCH,], "illegal warm start option" + assert warm_start in [ + None, + self.START_FROM_W0, + self.START_FROM_BENCH, + ], "illegal warm start option" self.start_from_w0 = warm_start == self.START_FROM_W0 self.start_from_bench = warm_start == self.START_FROM_BENCH diff --git a/qlib/tests/__init__.py b/qlib/tests/__init__.py index eb6f9c5ed..f92e72787 100644 --- a/qlib/tests/__init__.py +++ b/qlib/tests/__init__.py @@ -18,6 +18,10 @@ class TestAutoData(unittest.TestCase): print(f"Qlib data is not found in {provider_uri}") GetData().qlib_data( - name="qlib_data_simple", region="cn", interval="1d", target_dir=provider_uri, delete_old=False, + name="qlib_data_simple", + region="cn", + interval="1d", + target_dir=provider_uri, + delete_old=False, ) init(provider_uri=provider_uri, region=REG_CN, **cls._setup_kwargs) diff --git a/qlib/workflow/record_temp.py b/qlib/workflow/record_temp.py index 0c704b896..be458a24d 100644 --- a/qlib/workflow/record_temp.py +++ b/qlib/workflow/record_temp.py @@ -193,7 +193,10 @@ class SigAnaRecord(SignalRecord): } ) objects.update( - {"long_short_r.pkl": long_short_r, "long_avg_r.pkl": long_avg_r,} + { + "long_short_r.pkl": long_short_r, + "long_avg_r.pkl": long_avg_r, + } ) self.recorder.log_metrics(**metrics) self.recorder.save_objects(**objects, artifact_path=self.get_path()) diff --git a/tests/test_all_pipeline.py b/tests/test_all_pipeline.py index 8b3819c83..f6e77cba4 100644 --- a/tests/test_all_pipeline.py +++ b/tests/test_all_pipeline.py @@ -78,7 +78,10 @@ port_analysis_config = { "strategy": { "class": "TopkDropoutStrategy", "module_path": "qlib.contrib.strategy.strategy", - "kwargs": {"topk": 50, "n_drop": 5,}, + "kwargs": { + "topk": 50, + "n_drop": 5, + }, }, "backtest": { "verbose": False, @@ -173,7 +176,9 @@ class TestAllFlow(TestAutoData): def test_1_backtest(self): analyze_df = backtest_analysis(TestAllFlow.PRED_SCORE, TestAllFlow.RID) self.assertGreaterEqual( - analyze_df.loc(axis=0)["excess_return_with_cost", "annualized_return"].values[0], 0.10, "backtest failed", + analyze_df.loc(axis=0)["excess_return_with_cost", "annualized_return"].values[0], + 0.10, + "backtest failed", ) diff --git a/tests/test_dump_data.py b/tests/test_dump_data.py index de649c37e..dfa7f8556 100644 --- a/tests/test_dump_data.py +++ b/tests/test_dump_data.py @@ -40,7 +40,9 @@ class TestDumpData(unittest.TestCase): TestDumpData.STOCK_NAMES = list(map(lambda x: x.name[:-4].upper(), SOURCE_DIR.glob("*.csv"))) provider_uri = str(QLIB_DIR.resolve()) qlib.init( - provider_uri=provider_uri, expression_cache=None, dataset_cache=None, + provider_uri=provider_uri, + expression_cache=None, + dataset_cache=None, ) @classmethod @@ -52,7 +54,10 @@ class TestDumpData(unittest.TestCase): def test_1_dump_calendars(self): ori_calendars = set( - map(pd.Timestamp, pd.read_csv(QLIB_DIR.joinpath("calendars", "day.txt"), header=None).loc[:, 0].values,) + map( + pd.Timestamp, + pd.read_csv(QLIB_DIR.joinpath("calendars", "day.txt"), header=None).loc[:, 0].values, + ) ) res_calendars = set(D.calendar()) assert len(ori_calendars - res_calendars) == len(res_calendars - ori_calendars) == 0, "dump calendars failed" diff --git a/tests/test_get_data.py b/tests/test_get_data.py index d5637b025..c511d1b91 100644 --- a/tests/test_get_data.py +++ b/tests/test_get_data.py @@ -26,7 +26,9 @@ class TestGetData(unittest.TestCase): def setUpClass(cls) -> None: provider_uri = str(QLIB_DIR.resolve()) qlib.init( - provider_uri=provider_uri, expression_cache=None, dataset_cache=None, + provider_uri=provider_uri, + expression_cache=None, + dataset_cache=None, ) @classmethod From 81987bb1436d2d3990662c89cb9c14e88270c0f9 Mon Sep 17 00:00:00 2001 From: bxdd <45119470+bxdd@users.noreply.github.com> Date: Tue, 9 Mar 2021 15:38:04 +0800 Subject: [PATCH 084/134] Update ops.py --- qlib/data/ops.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/qlib/data/ops.py b/qlib/data/ops.py index 8bc7e1fa7..cbc101f47 100644 --- a/qlib/data/ops.py +++ b/qlib/data/ops.py @@ -74,7 +74,6 @@ class NpElemOperator(ElemOperator): """ def __init__(self, feature, func): - self.feature = feature self.func = func super(NpElemOperator, self).__init__(feature) @@ -289,8 +288,6 @@ class NpPairOperator(PairOperator): """ def __init__(self, feature_left, feature_right, func): - self.feature_left = feature_left - self.feature_right = feature_right self.func = func super(NpPairOperator, self).__init__(feature_left, feature_right) From 78bc2c874891b567e10740b9573f4a9e4c06f003 Mon Sep 17 00:00:00 2001 From: Jactus Date: Tue, 9 Mar 2021 17:31:27 +0800 Subject: [PATCH 085/134] Update Filter doc --- docs/component/data.rst | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/docs/component/data.rst b/docs/component/data.rst index 4b0962d49..9e5d7de2f 100644 --- a/docs/component/data.rst +++ b/docs/component/data.rst @@ -218,6 +218,25 @@ Filter - `cross-sectional features filter` \: rule_expression = '$rank($close)<10' - `time-sequence features filter`: rule_expression = '$Ref($close, 3)>100' +Here is a simple example showing how to use filter in a basic ``Qlib`` workflow configuration file: + +.. code-block:: yaml + + filter: &filter + filter_type: ExpressionDFilter + rule_expression: "Ref($close, -2) / Ref($close, -1) > 1" + filter_start_time: 2010-01-01 + filter_end_time: 2010-01-07 + keep: False + + data_handler_config: &data_handler_config + start_time: 2010-01-01 + end_time: 2021-01-22 + fit_start_time: 2010-01-01 + fit_end_time: 2015-12-31 + instruments: *market + filter_pipe: [*filter] + To know more about ``Filter``, please refer to `Filter API <../reference/api.html#module-qlib.data.filter>`_. Reference From 105fe1d3edde2ac35d88b2cc2154d6effd64339a Mon Sep 17 00:00:00 2001 From: Jactus Date: Wed, 10 Mar 2021 10:38:43 +0800 Subject: [PATCH 086/134] Remove deprecated warning for numpy>=1.20.0 --- qlib/data/dataset/__init__.py | 2 +- qlib/utils/__init__.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/qlib/data/dataset/__init__.py b/qlib/data/dataset/__init__.py index ecbeebc95..690436ba9 100644 --- a/qlib/data/dataset/__init__.py +++ b/qlib/data/dataset/__init__.py @@ -413,7 +413,7 @@ class TSDataSampler: # 1) for better performance, use the last nan line for padding the lost date # 2) In case of precision problems. We use np.float64. # TODO: I'm not sure if whether np.float64 will result in # precision problems. It will not cause any problems in my tests at least - indices = np.nan_to_num(indices.astype(np.float64), nan=self.nan_idx).astype(np.int) + indices = np.nan_to_num(indices.astype(np.float64), nan=self.nan_idx).astype(int) data = self.data_arr[indices] if isinstance(idx, mtit): diff --git a/qlib/utils/__init__.py b/qlib/utils/__init__.py index 6640dae2c..f550a0419 100644 --- a/qlib/utils/__init__.py +++ b/qlib/utils/__init__.py @@ -64,7 +64,7 @@ def np_ffill(arr: np.array): arr : np.array Input numpy 1D array """ - mask = np.isnan(arr.astype(np.float)) # np.isnan only works on np.float + mask = np.isnan(arr.astype(float)) # np.isnan only works on np.float # get fill index idx = np.where(~mask, np.arange(mask.shape[0]), 0) np.maximum.accumulate(idx, out=idx) From 119fe90570374c99332d6afd816c34c3c7884e29 Mon Sep 17 00:00:00 2001 From: Jactus Date: Wed, 10 Mar 2021 16:43:32 +0800 Subject: [PATCH 087/134] Fix pytorch ts model loader bug --- qlib/contrib/model/pytorch_alstm_ts.py | 8 ++++++-- qlib/contrib/model/pytorch_gats_ts.py | 4 ++-- qlib/contrib/model/pytorch_gru_ts.py | 8 ++++++-- qlib/contrib/model/pytorch_lstm_ts.py | 8 ++++++-- 4 files changed, 20 insertions(+), 8 deletions(-) diff --git a/qlib/contrib/model/pytorch_alstm_ts.py b/qlib/contrib/model/pytorch_alstm_ts.py index 725568de8..ad3945368 100644 --- a/qlib/contrib/model/pytorch_alstm_ts.py +++ b/qlib/contrib/model/pytorch_alstm_ts.py @@ -210,8 +210,12 @@ class ALSTM(Model): dl_train.config(fillna_type="ffill+bfill") # process nan brought by dataloader dl_valid.config(fillna_type="ffill+bfill") # process nan brought by dataloader - train_loader = DataLoader(dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs) - valid_loader = DataLoader(dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs) + train_loader = DataLoader( + dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs, drop_last=True + ) + valid_loader = DataLoader( + dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True + ) if save_path == None: save_path = create_save_path(save_path) diff --git a/qlib/contrib/model/pytorch_gats_ts.py b/qlib/contrib/model/pytorch_gats_ts.py index 1e94f56e4..241debe61 100644 --- a/qlib/contrib/model/pytorch_gats_ts.py +++ b/qlib/contrib/model/pytorch_gats_ts.py @@ -258,8 +258,8 @@ class GATs(Model): sampler_train = DailyBatchSampler(dl_train) sampler_valid = DailyBatchSampler(dl_valid) - train_loader = DataLoader(dl_train, sampler=sampler_train, num_workers=self.n_jobs) - valid_loader = DataLoader(dl_valid, sampler=sampler_valid, num_workers=self.n_jobs) + train_loader = DataLoader(dl_train, sampler=sampler_train, num_workers=self.n_jobs, drop_last=True) + valid_loader = DataLoader(dl_valid, sampler=sampler_valid, num_workers=self.n_jobs, drop_last=True) if save_path == None: save_path = create_save_path(save_path) diff --git a/qlib/contrib/model/pytorch_gru_ts.py b/qlib/contrib/model/pytorch_gru_ts.py index bb6618b85..a0b240ef4 100755 --- a/qlib/contrib/model/pytorch_gru_ts.py +++ b/qlib/contrib/model/pytorch_gru_ts.py @@ -210,8 +210,12 @@ class GRU(Model): dl_train.config(fillna_type="ffill+bfill") # process nan brought by dataloader dl_valid.config(fillna_type="ffill+bfill") # process nan brought by dataloader - train_loader = DataLoader(dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs) - valid_loader = DataLoader(dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs) + train_loader = DataLoader( + dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs, drop_last=True + ) + valid_loader = DataLoader( + dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True + ) if save_path == None: save_path = create_save_path(save_path) diff --git a/qlib/contrib/model/pytorch_lstm_ts.py b/qlib/contrib/model/pytorch_lstm_ts.py index cf4f8fb9f..d0decc5fb 100755 --- a/qlib/contrib/model/pytorch_lstm_ts.py +++ b/qlib/contrib/model/pytorch_lstm_ts.py @@ -210,8 +210,12 @@ class LSTM(Model): dl_train.config(fillna_type="ffill+bfill") # process nan brought by dataloader dl_valid.config(fillna_type="ffill+bfill") # process nan brought by dataloader - train_loader = DataLoader(dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs) - valid_loader = DataLoader(dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs) + train_loader = DataLoader( + dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs, drop_last=True + ) + valid_loader = DataLoader( + dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True + ) if save_path == None: save_path = create_save_path(save_path) From f6ed1750702eae3fce06f0f3f1c0fc5a0f377e3c Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Thu, 11 Mar 2021 02:33:00 +0000 Subject: [PATCH 088/134] Remove set_log_basic_config, refine count_parameters, rename root_uri as get_local_dir --- qlib/contrib/model/pytorch_utils.py | 27 ++++++++++++++++++++------- qlib/log.py | 21 --------------------- qlib/workflow/recorder.py | 16 +++++++++++----- tests/test_all_pipeline.py | 2 ++ 4 files changed, 33 insertions(+), 33 deletions(-) diff --git a/qlib/contrib/model/pytorch_utils.py b/qlib/contrib/model/pytorch_utils.py index e7a8e8d67..1148a596a 100644 --- a/qlib/contrib/model/pytorch_utils.py +++ b/qlib/contrib/model/pytorch_utils.py @@ -4,7 +4,19 @@ import torch.nn as nn -def count_parameters(models_or_parameters, unit="mb"): +def count_parameters(models_or_parameters, unit="m"): + """ + This function is to obtain the storage size unit of a (or multiple) models. + + Parameters + ---------- + models_or_parameters : PyTorch model(s) or a list of parameters. + unit : the storage size unit. + + Returns + ------- + The number of parameters of the given model(s) or parameters. + """ if isinstance(models_or_parameters, nn.Module): counts = sum(v.numel() for v in models_or_parameters.parameters()) elif isinstance(models_or_parameters, nn.Parameter): @@ -13,12 +25,13 @@ def count_parameters(models_or_parameters, unit="mb"): return sum(count_parameters(x, unit) for x in models_or_parameters) else: counts = sum(v.numel() for v in models_or_parameters) - if unit.lower() == "mb": - counts /= 1e6 - elif unit.lower() == "kb": - counts /= 1e3 - elif unit.lower() == "gb": - counts /= 1e9 + unit = unit.lower() + if unit == "kb" or unit == "k": + counts /= 2 ** 10 + elif unit == "mb" or unit == "m": + counts /= 2 ** 20 + elif unit == "gb" or unit == "g": + counts /= 2 ** 30 elif unit is not None: raise ValueError("Unknow unit: {:}".format(unit)) return counts diff --git a/qlib/log.py b/qlib/log.py index 78f12eb09..126acb9d2 100644 --- a/qlib/log.py +++ b/qlib/log.py @@ -108,27 +108,6 @@ def set_log_with_config(log_config: Dict[Text, Any]): logging_config.dictConfig(log_config) -def set_log_basic_config(filename: Optional[Text] = None, format: Optional[Text] = None, level: Optional[int] = None): - """ - Set the basic configuration for the logging system. - See details at https://docs.python.org/3/library/logging.html#logging.basicConfig - - :param filename: str or None - The path to save the logs. - :param format: the logging format - :param level: int - :return: Logger - Logger object. - """ - if level is None: - level = C.logging_level - - if format is None: - format = C.logging_config["formatters"]["logger_format"]["format"] - - logging.basicConfig(filename=filename, format=format, level=level) - - class LogFilter(logging.Filter): def __init__(self, param=None): self.param = param diff --git a/qlib/workflow/recorder.py b/qlib/workflow/recorder.py index 519b69710..bc0a9ef77 100644 --- a/qlib/workflow/recorder.py +++ b/qlib/workflow/recorder.py @@ -240,12 +240,18 @@ class MLflowRecorder(Recorder): def artifact_uri(self): return self._artifact_uri - @property - def root_uri(self): - start_str = "file:" + def get_local_dir(self): + """ + This function will return the directory path of this recorder. + """ if self.artifact_uri is not None: - xpath = self.artifact_uri.strip(start_str) - return (Path(xpath) / "..").resolve() + local_file_prefix = "file:" + if self.artifact_uri.startswith(local_file_prefix): + xpath = self.artifact_uri.lstrip(local_file_prefix) + return (Path(xpath) / "..").resolve() + else: + raise RuntimeError("This recorder is not saved in the local file system.") + else: raise Exception( "Please make sure the recorder has been created and started properly before getting artifact uri." diff --git a/tests/test_all_pipeline.py b/tests/test_all_pipeline.py index d9d684697..fbf15d29a 100644 --- a/tests/test_all_pipeline.py +++ b/tests/test_all_pipeline.py @@ -123,6 +123,8 @@ def train(): recorder = R.get_recorder() # To test __repr__ print(recorder) + # To test get_local_dir + print(recorder.get_local_dir()) rid = recorder.id sr = SignalRecord(model, dataset, recorder) sr.generate() From cda96be8c3cf8554587c06857c6e35cbe5adbd6d Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Thu, 11 Mar 2021 02:49:03 +0000 Subject: [PATCH 089/134] Refine default uri in expm --- qlib/workflow/__init__.py | 6 +++--- qlib/workflow/expm.py | 25 ++++++++++++++----------- 2 files changed, 17 insertions(+), 14 deletions(-) diff --git a/qlib/workflow/__init__.py b/qlib/workflow/__init__.py index 54297ecd7..834bd059e 100644 --- a/qlib/workflow/__init__.py +++ b/qlib/workflow/__init__.py @@ -285,11 +285,11 @@ class QlibRecorder: """ return self.exp_manager.uri - def reset_default_uri(self, uri: Text): + def set_uri(self, uri: Optional[Text]): """ - Method to reset the default uri of current experiment manager. + Method to reset the current uri of current experiment manager. """ - self.exp_manager.reset_default_uri(uri) + self.exp_manager.set_uri(uri) def get_recorder(self, recorder_id=None, recorder_name=None, experiment_name=None): """ diff --git a/qlib/workflow/expm.py b/qlib/workflow/expm.py index f9a5d0252..56fe810c3 100644 --- a/qlib/workflow/expm.py +++ b/qlib/workflow/expm.py @@ -10,6 +10,7 @@ from contextlib import contextmanager from typing import Optional, Text from .exp import MLflowExperiment, Experiment +from ..config import C from .recorder import Recorder from ..log import get_module_logger @@ -23,19 +24,12 @@ class ExpManager: """ def __init__(self, uri: Text, default_exp_name: Optional[Text]): - self._default_uri = uri - self._current_uri = None + self._current_uri = uri self.default_exp_name = default_exp_name self.active_experiment = None # only one experiment can active each time def __repr__(self): - return "{name}(default_uri={duri}, current_uri={curi})".format( - name=self.__class__.__name__, duri=self._default_uri, curi=self._current_uri - ) - - def reset_default_uri(self, uri: Text): - self._default_uri = uri - self.set_uri(None) + return "{name}(current_uri={curi})".format(name=self.__class__.__name__, curi=self._current_uri) def start_exp( self, @@ -221,6 +215,15 @@ class ExpManager: """ raise NotImplementedError(f"Please implement the `delete_exp` method.") + @property + def default_uri(self): + """ + Get the default tracking URI from qlib.config.C + """ + if "kwargs" not in C.exp_manager or "uri" not in C.exp_manager["kwargs"]: + raise ValueError("The default URI is not set in qlib.config.C") + return C.exp_manager["kwargs"]["uri"] + @property def uri(self): """ @@ -230,7 +233,7 @@ class ExpManager: ------- The tracking URI string. """ - return self._current_uri or self._default_uri + return self._current_uri or self.default_uri def set_uri(self, uri: Optional[Text] = None): """ @@ -243,7 +246,7 @@ class ExpManager: """ if uri is None: logger.info("No tracking URI is provided. Use the default tracking URI.") - self._current_uri = self._default_uri + self._current_uri = self.default_uri else: # Temporarily re-set the current uri as the uri argument. self._current_uri = uri From 0ef7c8e0e62f1b08f89f676eac97cbbd2bcc1657 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Thu, 11 Mar 2021 03:05:31 +0000 Subject: [PATCH 090/134] Fix bugs for get_local_dir --- qlib/contrib/backtest/account.py | 7 +++---- qlib/contrib/online/operator.py | 2 +- qlib/workflow/cli.py | 2 +- qlib/workflow/recorder.py | 8 ++++---- 4 files changed, 9 insertions(+), 10 deletions(-) diff --git a/qlib/contrib/backtest/account.py b/qlib/contrib/backtest/account.py index d8b285019..a614f08b6 100644 --- a/qlib/contrib/backtest/account.py +++ b/qlib/contrib/backtest/account.py @@ -104,10 +104,9 @@ class Account: # if suspend, no new price to be updated, profit is 0 if trader.check_stock_suspended(code, today): continue - else: - today_close = trader.get_close(code, today) - profit += (today_close - self.current.position[code]["price"]) * self.current.position[code]["amount"] - self.current.update_stock_price(stock_id=code, price=today_close) + today_close = trader.get_close(code, today) + profit += (today_close - self.current.position[code]["price"]) * self.current.position[code]["amount"] + self.current.update_stock_price(stock_id=code, price=today_close) self.rtn += profit # update holding day count self.current.add_count_all() diff --git a/qlib/contrib/online/operator.py b/qlib/contrib/online/operator.py index c8b44f578..d2307dad5 100644 --- a/qlib/contrib/online/operator.py +++ b/qlib/contrib/online/operator.py @@ -148,7 +148,7 @@ class Operator: for user_id, user in um.users.items(): dates, trade_exchange = prepare(um, trade_date, user_id, exchange_config) executor = SimulatorExecutor(trade_exchange=trade_exchange) - if not str(dates[0].date()) == str(pred_date.date()): + if str(dates[0].date()) != str(pred_date.date()): raise ValueError( "The account data is not newest! last trading date {}, today {}".format( dates[0].date(), trade_date.date() diff --git a/qlib/workflow/cli.py b/qlib/workflow/cli.py index 6eba96277..879c0aaeb 100644 --- a/qlib/workflow/cli.py +++ b/qlib/workflow/cli.py @@ -16,7 +16,7 @@ def get_path_list(path): if isinstance(path, str): return [path] else: - return [p for p in path] + return list(path) def sys_config(config, config_path): diff --git a/qlib/workflow/recorder.py b/qlib/workflow/recorder.py index bc0a9ef77..5915e58da 100644 --- a/qlib/workflow/recorder.py +++ b/qlib/workflow/recorder.py @@ -245,10 +245,10 @@ class MLflowRecorder(Recorder): This function will return the directory path of this recorder. """ if self.artifact_uri is not None: - local_file_prefix = "file:" - if self.artifact_uri.startswith(local_file_prefix): - xpath = self.artifact_uri.lstrip(local_file_prefix) - return (Path(xpath) / "..").resolve() + local_dir_path = Path(self.artifact_uri.lstrip("file:")) / ".." + local_dir_path = str(local_dir_path.resolve()) + if os.path.isdir(local_dir_path): + return local_dir_path else: raise RuntimeError("This recorder is not saved in the local file system.") From b99de068f8dc585f93a1b95b47e7bd3ee0973d24 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Thu, 11 Mar 2021 12:52:26 +0000 Subject: [PATCH 091/134] Move save_path to get_or_create_path, and fix bugs in sfm / tabnet --- qlib/contrib/model/pytorch_alstm.py | 5 ++--- qlib/contrib/model/pytorch_alstm_ts.py | 5 ++--- qlib/contrib/model/pytorch_gats.py | 5 ++--- qlib/contrib/model/pytorch_gats_ts.py | 5 ++--- qlib/contrib/model/pytorch_gru.py | 5 ++--- qlib/contrib/model/pytorch_gru_ts.py | 5 ++--- qlib/contrib/model/pytorch_lstm.py | 5 ++--- qlib/contrib/model/pytorch_lstm_ts.py | 5 ++--- qlib/contrib/model/pytorch_nn.py | 4 ++-- qlib/contrib/model/pytorch_sfm.py | 6 +++++- qlib/contrib/model/pytorch_tabnet.py | 12 +++++++----- qlib/utils/__init__.py | 26 +++++++++++++++++--------- 12 files changed, 47 insertions(+), 41 deletions(-) diff --git a/qlib/contrib/model/pytorch_alstm.py b/qlib/contrib/model/pytorch_alstm.py index e8b316795..0cb09a05a 100644 --- a/qlib/contrib/model/pytorch_alstm.py +++ b/qlib/contrib/model/pytorch_alstm.py @@ -14,7 +14,7 @@ import logging from ...utils import ( unpack_archive_with_buffer, save_multiple_parts_file, - create_save_path, + get_or_create_path, drop_nan_by_y_index, ) from ...log import get_module_logger, TimeInspector @@ -230,8 +230,7 @@ class ALSTM(Model): x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] - if save_path == None: - save_path = create_save_path(save_path) + save_path = get_or_create_path(save_path) stop_steps = 0 train_loss = 0 best_score = -np.inf diff --git a/qlib/contrib/model/pytorch_alstm_ts.py b/qlib/contrib/model/pytorch_alstm_ts.py index 1907b9d76..0272dd2e5 100644 --- a/qlib/contrib/model/pytorch_alstm_ts.py +++ b/qlib/contrib/model/pytorch_alstm_ts.py @@ -14,7 +14,7 @@ import logging from ...utils import ( unpack_archive_with_buffer, save_multiple_parts_file, - create_save_path, + get_or_create_path, drop_nan_by_y_index, ) from ...log import get_module_logger, TimeInspector @@ -216,8 +216,7 @@ class ALSTM(Model): train_loader = DataLoader(dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs) valid_loader = DataLoader(dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs) - if save_path == None: - save_path = create_save_path(save_path) + save_path = get_or_create_path(save_path) stop_steps = 0 train_loss = 0 diff --git a/qlib/contrib/model/pytorch_gats.py b/qlib/contrib/model/pytorch_gats.py index 7d3b00232..b5330146f 100644 --- a/qlib/contrib/model/pytorch_gats.py +++ b/qlib/contrib/model/pytorch_gats.py @@ -14,7 +14,7 @@ import logging from ...utils import ( unpack_archive_with_buffer, save_multiple_parts_file, - create_save_path, + get_or_create_path, drop_nan_by_y_index, ) from ...log import get_module_logger, TimeInspector @@ -248,8 +248,7 @@ class GATs(Model): x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] - if save_path == None: - save_path = create_save_path(save_path) + save_path = get_or_create_path(save_path) stop_steps = 0 best_score = -np.inf best_epoch = 0 diff --git a/qlib/contrib/model/pytorch_gats_ts.py b/qlib/contrib/model/pytorch_gats_ts.py index 99ae3d4da..7c8015437 100644 --- a/qlib/contrib/model/pytorch_gats_ts.py +++ b/qlib/contrib/model/pytorch_gats_ts.py @@ -14,7 +14,7 @@ import logging from ...utils import ( unpack_archive_with_buffer, save_multiple_parts_file, - create_save_path, + get_or_create_path, drop_nan_by_y_index, ) from ...log import get_module_logger, TimeInspector @@ -264,8 +264,7 @@ class GATs(Model): train_loader = DataLoader(dl_train, sampler=sampler_train, num_workers=self.n_jobs) valid_loader = DataLoader(dl_valid, sampler=sampler_valid, num_workers=self.n_jobs) - if save_path == None: - save_path = create_save_path(save_path) + save_path = get_or_create_path(save_path) stop_steps = 0 train_loss = 0 diff --git a/qlib/contrib/model/pytorch_gru.py b/qlib/contrib/model/pytorch_gru.py index d4dc88452..697b71cc9 100755 --- a/qlib/contrib/model/pytorch_gru.py +++ b/qlib/contrib/model/pytorch_gru.py @@ -14,7 +14,7 @@ import logging from ...utils import ( unpack_archive_with_buffer, save_multiple_parts_file, - create_save_path, + get_or_create_path, drop_nan_by_y_index, ) from ...log import get_module_logger, TimeInspector @@ -230,8 +230,7 @@ class GRU(Model): x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] - if save_path == None: - save_path = create_save_path(save_path) + save_path = get_or_create_path(save_path) stop_steps = 0 train_loss = 0 best_score = -np.inf diff --git a/qlib/contrib/model/pytorch_gru_ts.py b/qlib/contrib/model/pytorch_gru_ts.py index 1f2137c8f..da449c714 100755 --- a/qlib/contrib/model/pytorch_gru_ts.py +++ b/qlib/contrib/model/pytorch_gru_ts.py @@ -14,7 +14,7 @@ import logging from ...utils import ( unpack_archive_with_buffer, save_multiple_parts_file, - create_save_path, + get_or_create_path, drop_nan_by_y_index, ) from ...log import get_module_logger, TimeInspector @@ -216,8 +216,7 @@ class GRU(Model): train_loader = DataLoader(dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs) valid_loader = DataLoader(dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs) - if save_path == None: - save_path = create_save_path(save_path) + save_path = get_or_create_path(save_path) stop_steps = 0 train_loss = 0 diff --git a/qlib/contrib/model/pytorch_lstm.py b/qlib/contrib/model/pytorch_lstm.py index be8d20a15..648a909c7 100755 --- a/qlib/contrib/model/pytorch_lstm.py +++ b/qlib/contrib/model/pytorch_lstm.py @@ -14,7 +14,7 @@ import logging from ...utils import ( unpack_archive_with_buffer, save_multiple_parts_file, - create_save_path, + get_or_create_path, drop_nan_by_y_index, ) from ...log import get_module_logger, TimeInspector @@ -226,8 +226,7 @@ class LSTM(Model): x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] - if save_path == None: - save_path = create_save_path(save_path) + save_path = get_or_create_path(save_path) stop_steps = 0 train_loss = 0 best_score = -np.inf diff --git a/qlib/contrib/model/pytorch_lstm_ts.py b/qlib/contrib/model/pytorch_lstm_ts.py index 3142f15c5..94d9a3f12 100755 --- a/qlib/contrib/model/pytorch_lstm_ts.py +++ b/qlib/contrib/model/pytorch_lstm_ts.py @@ -14,7 +14,7 @@ import logging from ...utils import ( unpack_archive_with_buffer, save_multiple_parts_file, - create_save_path, + get_or_create_path, drop_nan_by_y_index, ) from ...log import get_module_logger, TimeInspector @@ -212,8 +212,7 @@ class LSTM(Model): train_loader = DataLoader(dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs) valid_loader = DataLoader(dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs) - if save_path == None: - save_path = create_save_path(save_path) + save_path = get_or_create_path(save_path) stop_steps = 0 train_loss = 0 diff --git a/qlib/contrib/model/pytorch_nn.py b/qlib/contrib/model/pytorch_nn.py index a51481c85..37d8dec3e 100644 --- a/qlib/contrib/model/pytorch_nn.py +++ b/qlib/contrib/model/pytorch_nn.py @@ -19,7 +19,7 @@ from .pytorch_utils import count_parameters from ...model.base import Model from ...data.dataset import DatasetH from ...data.dataset.handler import DataHandlerLP -from ...utils import unpack_archive_with_buffer, save_multiple_parts_file, create_save_path, drop_nan_by_y_index +from ...utils import unpack_archive_with_buffer, save_multiple_parts_file, get_or_create_path, drop_nan_by_y_index from ...log import get_module_logger, TimeInspector from ...workflow import R @@ -176,7 +176,7 @@ class DNNModelPytorch(Model): w_train = pd.DataFrame(np.ones_like(y_train.values), index=y_train.index) w_valid = pd.DataFrame(np.ones_like(y_valid.values), index=y_valid.index) - save_path = create_save_path(save_path) + save_path = get_or_create_path(save_path) stop_steps = 0 train_loss = 0 best_loss = np.inf diff --git a/qlib/contrib/model/pytorch_sfm.py b/qlib/contrib/model/pytorch_sfm.py index 40b991c9c..cc600a955 100644 --- a/qlib/contrib/model/pytorch_sfm.py +++ b/qlib/contrib/model/pytorch_sfm.py @@ -13,7 +13,7 @@ import logging from ...utils import ( unpack_archive_with_buffer, save_multiple_parts_file, - create_save_path, + get_or_create_path, drop_nan_by_y_index, ) from ...log import get_module_logger, TimeInspector @@ -380,6 +380,7 @@ class SFM(Model): x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] + save_path = get_or_create_path(save_path) stop_steps = 0 train_loss = 0 best_score = -np.inf @@ -412,7 +413,10 @@ class SFM(Model): if stop_steps >= self.early_stop: self.logger.info("early stop") break + self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch)) + self.sfm_model.load_state_dict(best_param) + torch.save(best_param, save_path) if self.device != "cpu": torch.cuda.empty_cache() diff --git a/qlib/contrib/model/pytorch_tabnet.py b/qlib/contrib/model/pytorch_tabnet.py index 020bbaff2..93b2a36da 100644 --- a/qlib/contrib/model/pytorch_tabnet.py +++ b/qlib/contrib/model/pytorch_tabnet.py @@ -12,7 +12,7 @@ import logging from ...utils import ( unpack_archive_with_buffer, save_multiple_parts_file, - create_save_path, + get_or_create_path, drop_nan_by_y_index, ) from ...log import get_module_logger, TimeInspector @@ -117,10 +117,7 @@ class TabnetModel(Model): raise NotImplementedError("optimizer {} is not supported!".format(optimizer)) def pretrain_fn(self, dataset=DatasetH, pretrain_file="./pretrain/best.model"): - # make a directory if pretrian director does not exist - if pretrain_file.startswith("./pretrain") and not os.path.exists("pretrain"): - self.logger.info("make folder to store model...") - os.makedirs("pretrain") + get_or_create_path(pretrain_file) [df_train, df_valid] = dataset.prepare( ["pretrain", "pretrain_validation"], @@ -181,6 +178,7 @@ class TabnetModel(Model): df_train.fillna(df_train.mean(), inplace=True) x_train, y_train = df_train["feature"], df_train["label"] x_valid, y_valid = df_valid["feature"], df_valid["label"] + save_path = get_or_create_path(save_path) stop_steps = 0 train_loss = 0 @@ -207,12 +205,16 @@ class TabnetModel(Model): best_score = val_score stop_steps = 0 best_epoch = epoch_idx + best_param = copy.deepcopy(self.tabnet_model.state_dict()) else: stop_steps += 1 if stop_steps >= self.early_stop: self.logger.info("early stop") break + self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch)) + self.tabnet_model.load_state_dict(best_param) + torch.save(best_param, save_path) def predict(self, dataset): if not self.fitted: diff --git a/qlib/utils/__init__.py b/qlib/utils/__init__.py index 6640dae2c..e5933cd9d 100644 --- a/qlib/utils/__init__.py +++ b/qlib/utils/__init__.py @@ -24,7 +24,7 @@ import collections import numpy as np import pandas as pd from pathlib import Path -from typing import Union, Tuple +from typing import Union, Tuple, Text, Optional from ..config import C from ..log import get_module_logger, set_log_with_config @@ -276,23 +276,31 @@ def compare_dict_value(src_data: dict, dst_data: dict): return changes -def create_save_path(save_path=None): - """Create save path +def get_or_create_path(path: Optional[Text] = None, return_dir: bool = False): + """Create or get a file or directory given the path and return_dir. Parameters ---------- - save_path: str + path: a string indicates the path or None indicates creating a temporary path. + return_dir: if True, create and return a directory; otherwise c&r a file. """ - if save_path: - if not os.path.exists(save_path): - os.makedirs(save_path) + if path: + if return_dir and not os.path.exists(path): + os.makedirs(path) + elif not return_dir: # return a file, thus we need to create its parent directory + xpath = os.path.abspath(os.path.join(path, "..")) + if not os.path.exists(xpath): + os.makedirs(xpath) else: temp_dir = os.path.expanduser("~/tmp") if not os.path.exists(temp_dir): os.makedirs(temp_dir) - _, save_path = tempfile.mkstemp(dir=temp_dir) - return save_path + if return_dir: + _, path = tempfile.mkdtemp(dir=temp_dir) + else: + _, path = tempfile.mkstemp(dir=temp_dir) + return path @contextlib.contextmanager From 42be8ac312ae35ac4af3d9f5021621708657f020 Mon Sep 17 00:00:00 2001 From: zhupr Date: Wed, 10 Mar 2021 16:23:20 +0800 Subject: [PATCH 092/134] Add BaseCollector --- scripts/data_collector/base.py | 430 +++++++++++++++++++ scripts/data_collector/yahoo/collector.py | 488 ++++++---------------- 2 files changed, 548 insertions(+), 370 deletions(-) create mode 100644 scripts/data_collector/base.py diff --git a/scripts/data_collector/base.py b/scripts/data_collector/base.py new file mode 100644 index 000000000..ccd8f59e5 --- /dev/null +++ b/scripts/data_collector/base.py @@ -0,0 +1,430 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + + +import abc +import time +import datetime +import importlib +from pathlib import Path +from typing import Type +from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor + +import pandas as pd +from tqdm import tqdm +from loguru import logger +from qlib.utils import code_to_fname + + +class BaseCollector(abc.ABC): + + CACHE_FLAG = "CACHED" + NORMAL_FLAG = "NORMAL" + + DEFAULT_START_DATETIME_1D = pd.Timestamp("2000-01-01") + DEFAULT_START_DATETIME_1MIN = pd.Timestamp(datetime.datetime.now() - pd.Timedelta(days=5 * 6)) + DEFAULT_END_DATETIME_1D = pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1)) + DEFAULT_END_DATETIME_1MIN = pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1)) + + INTERVAL_1min = "1min" + INTERVAL_1d = "1d" + + def __init__( + self, + save_dir: [str, Path], + start=None, + end=None, + interval="1d", + max_workers=4, + max_collector_count=2, + delay=0, + check_data_length: bool = False, + limit_nums: int = None, + ): + """ + + Parameters + ---------- + save_dir: str + stock save dir + max_workers: int + workers, default 4 + max_collector_count: int + default 2 + delay: float + time.sleep(delay), default 0 + interval: str + freq, value from [1min, 1d], default 1d + start: str + start datetime, default None + end: str + end datetime, default None + check_data_length: bool + check data length, by default False + limit_nums: int + using for debug, by default None + """ + self.save_dir = Path(save_dir).expanduser().resolve() + self.save_dir.mkdir(parents=True, exist_ok=True) + + self.delay = delay + self.max_workers = max_workers + self.max_collector_count = max_collector_count + self.mini_symbol_map = {} + self.interval = interval + self.check_small_data = check_data_length + + self.start_datetime = self.normalize_start_datetime(start) + self.end_datetime = self.normalize_end_datetime(end) + + self.stock_list = sorted(set(self.get_stock_list())) + + if limit_nums is not None: + try: + self.stock_list = self.stock_list[: int(limit_nums)] + except Exception as e: + logger.warning(f"Cannot use limit_nums={limit_nums}, the parameter will be ignored") + + def normalize_start_datetime(self, start_datetime: [str, pd.Timestamp] = None): + return ( + pd.Timestamp(str(start_datetime)) + if start_datetime + else getattr(self, f"DEFAULT_START_DATETIME_{self.interval.upper()}") + ) + + def normalize_end_datetime(self, end_datetime: [str, pd.Timestamp] = None): + return ( + pd.Timestamp(str(end_datetime)) + if end_datetime + else getattr(self, f"DEFAULT_END_DATETIME_{self.interval.upper()}") + ) + + @property + @abc.abstractmethod + def min_numbers_trading(self): + # daily, one year: 252 / 4 + # us 1min, a week: 6.5 * 60 * 5 + # cn 1min, a week: 4 * 60 * 5 + raise NotImplementedError("rewrite min_numbers_trading") + + @abc.abstractmethod + def get_stock_list(self): + raise NotImplementedError("rewrite get_stock_list") + + @abc.abstractmethod + def normalize_symbol(self, symbol: str): + """normalize symbol""" + raise NotImplementedError("rewrite normalize_symbol") + + @abc.abstractmethod + def get_data( + self, symbol: str, interval: str, start_datetime: pd.Timestamp, end_datetime: pd.Timestamp + ) -> pd.DataFrame: + """get data with symbol + + Parameters + ---------- + symbol: str + interval: str + value from [1min, 1d] + start_datetime: pd.Timestamp + end_datetime: pd.Timestamp + + Returns + --------- + pd.DataFrame, "symbol" in pd.columns + + """ + raise NotImplementedError("rewrite get_timezone") + + def sleep(self): + time.sleep(self.delay) + + def _simple_collector(self, symbol: str): + """ + + Parameters + ---------- + symbol: str + + """ + self.sleep() + df = self.get_data(symbol, self.interval, self.start_datetime, self.end_datetime) + _result = self.NORMAL_FLAG + if self.check_small_data: + _result = self.cache_small_data(symbol, df) + if _result == self.NORMAL_FLAG: + self.save_instrument(symbol, df) + return _result + + def save_instrument(self, symbol, df: pd.DataFrame): + """save stock data to file + + Parameters + ---------- + symbol: str + stock code + df : pd.DataFrame + df.columns must contain "symbol" and "datetime" + """ + if df.empty: + logger.warning(f"{symbol} is empty") + return + + symbol = self.normalize_symbol(symbol) + symbol = code_to_fname(symbol) + stock_path = self.save_dir.joinpath(f"{symbol}.csv") + df["symbol"] = symbol + if stock_path.exists(): + _old_df = pd.read_csv(stock_path) + df = _old_df.append(df, sort=False) + df.to_csv(stock_path, index=False) + + def cache_small_data(self, symbol, df): + if len(df) <= self.min_numbers_trading: + logger.warning(f"the number of trading days of {symbol} is less than {self.min_numbers_trading}!") + _temp = self.mini_symbol_map.setdefault(symbol, []) + _temp.append(df.copy()) + return self.CACHE_FLAG + else: + if symbol in self.mini_symbol_map: + self.mini_symbol_map.pop(symbol) + return self.NORMAL_FLAG + + def _collector(self, stock_list): + + error_symbol = [] + with ThreadPoolExecutor(max_workers=self.max_workers) as executor: + with tqdm(total=len(stock_list)) as p_bar: + for _symbol, _result in zip(stock_list, executor.map(self._simple_collector, stock_list)): + if _result != self.NORMAL_FLAG: + error_symbol.append(_symbol) + p_bar.update() + print(error_symbol) + logger.info(f"error symbol nums: {len(error_symbol)}") + logger.info(f"current get symbol nums: {len(stock_list)}") + error_symbol.extend(self.mini_symbol_map.keys()) + return sorted(set(error_symbol)) + + def collector_data(self): + """collector data""" + logger.info("start collector data......") + stock_list = self.stock_list + for i in range(self.max_collector_count): + if not stock_list: + break + logger.info(f"getting data: {i+1}") + stock_list = self._collector(stock_list) + logger.info(f"{i+1} finish.") + for _symbol, _df_list in self.mini_symbol_map.items(): + self.save_instrument( + _symbol, pd.concat(_df_list, sort=False).drop_duplicates(["date"]).sort_values(["date"]) + ) + if self.mini_symbol_map: + logger.warning(f"less than {self.min_numbers_trading} stock list: {list(self.mini_symbol_map.keys())}") + logger.info(f"total {len(self.stock_list)}, error: {len(set(stock_list))}") + + +class BaseNormalize(abc.ABC): + def __init__( + self, + date_field_name: str = "date", + symbol_field_name: str = "symbol", + ): + """ + + Parameters + ---------- + date_field_name: str + date field name, default is date + symbol_field_name: str + symbol field name, default is symbol + """ + self._date_field_name = date_field_name + self._symbol_field_name = symbol_field_name + + self._calendar_list = self._get_calendar_list() + + @abc.abstractmethod + def normalize(self, df: pd.DataFrame) -> pd.DataFrame: + # normalize + raise NotImplementedError("") + + @abc.abstractmethod + def _get_calendar_list(self): + """Get benchmark calendar""" + raise NotImplementedError("") + + +class Normalize: + def __init__( + self, + source_dir: [str, Path], + target_dir: [str, Path], + normalize_class: Type[BaseNormalize], + max_workers: int = 16, + date_field_name: str = "date", + symbol_field_name: str = "symbol", + ): + """ + + Parameters + ---------- + source_dir: str or Path + The directory where the raw data collected from the Internet is saved + target_dir: str or Path + Directory for normalize data + normalize_class: Type[YahooNormalize] + normalize class + max_workers: int + Concurrent number, default is 16 + date_field_name: str + date field name, default is date + symbol_field_name: str + symbol field name, default is symbol + """ + if not (source_dir and target_dir): + raise ValueError("source_dir and target_dir cannot be None") + self._source_dir = Path(source_dir).expanduser() + self._target_dir = Path(target_dir).expanduser() + self._target_dir.mkdir(parents=True, exist_ok=True) + + self._max_workers = max_workers + + self._normalize_obj = normalize_class(date_field_name=date_field_name, symbol_field_name=symbol_field_name) + + def _executor(self, file_path: Path): + file_path = Path(file_path) + df = pd.read_csv(file_path) + df = self._normalize_obj.normalize(df) + if not df.empty: + df.to_csv(self._target_dir.joinpath(file_path.name), index=False) + + def normalize(self): + logger.info("normalize data......") + + with ProcessPoolExecutor(max_workers=self._max_workers) as worker: + file_list = list(self._source_dir.glob("*.csv")) + with tqdm(total=len(file_list)) as p_bar: + for _ in worker.map(self._executor, file_list): + p_bar.update() + + +class BaseRun(abc.ABC): + def __init__(self, source_dir=None, normalize_dir=None, max_workers=4, interval="1d"): + """ + + Parameters + ---------- + source_dir: str + The directory where the raw data collected from the Internet is saved, default "Path(__file__).parent/source" + normalize_dir: str + Directory for normalize data, default "Path(__file__).parent/normalize" + max_workers: int + Concurrent number, default is 4 + interval: str + freq, value from [1min, 1d], default 1d + """ + if source_dir is None: + source_dir = Path(self.default_base_dir).joinpath("_source") + self.source_dir = Path(source_dir).expanduser().resolve() + self.source_dir.mkdir(parents=True, exist_ok=True) + + if normalize_dir is None: + normalize_dir = Path(self.default_base_dir).joinpath("normalize") + self.normalize_dir = Path(normalize_dir).expanduser().resolve() + self.normalize_dir.mkdir(parents=True, exist_ok=True) + + self._cur_module = importlib.import_module("collector") + self.max_workers = max_workers + self.interval = interval + + @property + @abc.abstractmethod + def collector_class_name(self): + raise NotImplementedError("rewrite normalize_symbol") + + @property + @abc.abstractmethod + def normalize_class_name(self): + raise NotImplementedError("rewrite normalize_symbol") + + @property + @abc.abstractmethod + def default_base_dir(self) -> [Path, str]: + raise NotImplementedError("rewrite normalize_symbol") + + def download_data( + self, + max_collector_count=2, + delay=0, + start=None, + end=None, + interval="1d", + check_data_length=False, + limit_nums=None, + ): + """download data from Internet + + Parameters + ---------- + max_collector_count: int + default 2 + delay: float + time.sleep(delay), default 0 + interval: str + freq, value from [1min, 1d], default 1d + start: str + start datetime, default "2000-01-01" + end: str + end datetime, default ``pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1))`` + check_data_length: bool + check data length, by default False + limit_nums: int + using for debug, by default None + + Examples + --------- + # get daily data + $ python collector.py download_data --source_dir ~/.qlib/stock_data/source --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d + # get 1m data + $ python collector.py download_data --source_dir ~/.qlib/stock_data/source --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1m + """ + + _class = getattr(self._cur_module, self.collector_class_name) # type: Type[BaseCollector] + _class( + self.source_dir, + max_workers=self.max_workers, + max_collector_count=max_collector_count, + delay=delay, + start=start, + end=end, + interval=interval, + check_data_length=check_data_length, + limit_nums=limit_nums, + ).collector_data() + + def normalize_data(self, date_field_name: str = "date", symbol_field_name: str = "symbol"): + """normalize data + + Parameters + ---------- + date_field_name: str + date field name, default date + symbol_field_name: str + symbol field name, default symbol + + Examples + --------- + $ python collector.py normalize_data --source_dir ~/.qlib/stock_data/source --normalize_dir ~/.qlib/stock_data/normalize --region CN --interval 1d + """ + _class = getattr(self._cur_module, self.normalize_class_name) + yc = Normalize( + source_dir=self.source_dir, + target_dir=self.normalize_dir, + normalize_class=_class, + max_workers=self.max_workers, + date_field_name=date_field_name, + symbol_field_name=symbol_field_name, + ) + yc.normalize() diff --git a/scripts/data_collector/yahoo/collector.py b/scripts/data_collector/yahoo/collector.py index 743f89462..eadc381ec 100644 --- a/scripts/data_collector/yahoo/collector.py +++ b/scripts/data_collector/yahoo/collector.py @@ -10,158 +10,26 @@ import importlib from abc import ABC from pathlib import Path from typing import Iterable, Type -from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor import fire import requests import numpy as np import pandas as pd -from tqdm import tqdm from loguru import logger from yahooquery import Ticker from dateutil.tz import tzlocal from qlib.utils import code_to_fname, fname_to_code +from qlib.config import REG_CN as REGION_CN CUR_DIR = Path(__file__).resolve().parent sys.path.append(str(CUR_DIR.parent.parent)) +from data_collector.base import BaseCollector, BaseNormalize, BaseRun from data_collector.utils import get_calendar_list, get_hs_stock_symbols, get_us_stock_symbols INDEX_BENCH_URL = "http://push2his.eastmoney.com/api/qt/stock/kline/get?secid=1.{index_code}&fields1=f1%2Cf2%2Cf3%2Cf4%2Cf5&fields2=f51%2Cf52%2Cf53%2Cf54%2Cf55%2Cf56%2Cf57%2Cf58&klt=101&fqt=0&beg={begin}&end={end}" -REGION_CN = "CN" -REGION_US = "US" -class YahooData: - START_DATETIME = pd.Timestamp("2000-01-01") - HIGH_FREQ_START_DATETIME = pd.Timestamp(datetime.datetime.now() - pd.Timedelta(days=5 * 6)) - END_DATETIME = pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1)) - INTERVAL_1min = "1min" - INTERVAL_1d = "1d" - - def __init__( - self, - timezone: str = None, - start=None, - end=None, - interval="1d", - delay=0, - show_1min_logging: bool = False, - ): - """ - - Parameters - ---------- - timezone: str - The timezone where the data is located - delay: float - time.sleep(delay), default 0 - interval: str - freq, value from [1min, 1d], default 1min - start: str - start datetime, default None - end: str - end datetime, default None - show_1min_logging: bool - show 1min logging, by default False; if True, there may be many warning logs - """ - self._timezone = tzlocal() if timezone is None else timezone - self._delay = delay - self._interval = interval - self._show_1min_logging = show_1min_logging - self.start_datetime = pd.Timestamp(str(start)) if start else self.START_DATETIME - self.end_datetime = min(pd.Timestamp(str(end)) if end else self.END_DATETIME, self.END_DATETIME) - if self._interval == self.INTERVAL_1min: - self.start_datetime = max(self.start_datetime, self.HIGH_FREQ_START_DATETIME) - elif self._interval == self.INTERVAL_1d: - pass - else: - raise ValueError(f"interval error: {self._interval}") - - # using for 1min - self._next_datetime = self.convert_datetime(self.start_datetime.date() + pd.Timedelta(days=1), self._timezone) - self._latest_datetime = self.convert_datetime(self.end_datetime.date(), self._timezone) - - self.start_datetime = self.convert_datetime(self.start_datetime, self._timezone) - self.end_datetime = self.convert_datetime(self.end_datetime, self._timezone) - - @staticmethod - def convert_datetime(dt: [pd.Timestamp, datetime.date, str], timezone): - try: - dt = pd.Timestamp(dt, tz=timezone).timestamp() - dt = pd.Timestamp(dt, tz=tzlocal(), unit="s") - except ValueError as e: - pass - return dt - - def _sleep(self): - time.sleep(self._delay) - - @staticmethod - def get_data_from_remote(symbol, interval, start, end, show_1min_logging: bool = False): - error_msg = f"{symbol}-{interval}-{start}-{end}" - - def _show_logging_func(): - if interval == YahooData.INTERVAL_1min and show_1min_logging: - logger.warning(f"{error_msg}:{_resp}") - - interval = "1m" if interval in ["1m", "1min"] else interval - try: - _resp = Ticker(symbol, asynchronous=False).history(interval=interval, start=start, end=end) - if isinstance(_resp, pd.DataFrame): - return _resp.reset_index() - elif isinstance(_resp, dict): - _temp_data = _resp.get(symbol, {}) - if isinstance(_temp_data, str) or ( - isinstance(_resp, dict) and _temp_data.get("indicators", {}).get("quote", None) is None - ): - _show_logging_func() - else: - _show_logging_func() - except Exception as e: - logger.warning(f"{error_msg}:{e}") - - def get_data(self, symbol: str) -> [pd.DataFrame]: - def _get_simple(start_, end_): - self._sleep() - _remote_interval = "1m" if self._interval == self.INTERVAL_1min else self._interval - return self.get_data_from_remote( - symbol, - interval=_remote_interval, - start=start_, - end=end_, - show_1min_logging=self._show_1min_logging, - ) - - _result = None - if self._interval == self.INTERVAL_1d: - _result = _get_simple(self.start_datetime, self.end_datetime) - elif self._interval == self.INTERVAL_1min: - if self._next_datetime >= self._latest_datetime: - _result = _get_simple(self.start_datetime, self.end_datetime) - else: - _res = [] - - def _get_multi(start_, end_): - _resp = _get_simple(start_, end_) - if _resp is not None and not _resp.empty: - _res.append(_resp) - - for _s, _e in ( - (self.start_datetime, self._next_datetime), - (self._latest_datetime, self.end_datetime), - ): - _get_multi(_s, _e) - for _start in pd.date_range(self._next_datetime, self._latest_datetime, closed="left"): - _end = _start + pd.Timedelta(days=1) - _get_multi(_start, _end) - if _res: - _result = pd.concat(_res, sort=False).sort_values(["symbol", "date"]) - else: - raise ValueError(f"cannot support {self._interval}") - return _result - - -class YahooCollector: +class YahooCollector(BaseCollector): def __init__( self, save_dir: [str, Path], @@ -173,7 +41,6 @@ class YahooCollector: delay=0, check_data_length: bool = False, limit_nums: int = None, - show_1min_logging: bool = False, ): """ @@ -197,131 +64,118 @@ class YahooCollector: check data length, by default False limit_nums: int using for debug, by default None - show_1min_logging: bool - show 1m logging, by default False; if True, there may be many warning logs """ - self.save_dir = Path(save_dir).expanduser().resolve() - self.save_dir.mkdir(parents=True, exist_ok=True) - - self._delay = delay - self.max_workers = max_workers - self._max_collector_count = max_collector_count - self._mini_symbol_map = {} - self._interval = interval - self._check_small_data = check_data_length - - self.stock_list = sorted(set(self.get_stock_list())) - if limit_nums is not None: - try: - self.stock_list = self.stock_list[: int(limit_nums)] - except Exception as e: - logger.warning(f"Cannot use limit_nums={limit_nums}, the parameter will be ignored") - - self.yahoo_data = YahooData( - timezone=self._timezone, + super(YahooCollector, self).__init__( + save_dir=save_dir, start=start, end=end, interval=interval, + max_workers=max_workers, + max_collector_count=max_collector_count, delay=delay, - show_1min_logging=show_1min_logging, + check_data_length=check_data_length, + limit_nums=limit_nums, ) - @property - @abc.abstractmethod - def min_numbers_trading(self): - # daily, one year: 252 / 4 - # us 1min, a week: 6.5 * 60 * 5 - # cn 1min, a week: 4 * 60 * 5 - raise NotImplementedError("rewrite min_numbers_trading") + self.init_datetime() - @abc.abstractmethod - def get_stock_list(self): - raise NotImplementedError("rewrite get_stock_list") + def init_datetime(self): + if self.interval == self.INTERVAL_1min: + self.start_datetime = max(self.start_datetime, self.DEFAULT_START_DATETIME_1MIN) + elif self.interval == self.INTERVAL_1d: + pass + else: + raise ValueError(f"interval error: {self.interval}") + + # using for 1min + self._next_datetime = self.convert_datetime(self.start_datetime.date() + pd.Timedelta(days=1), self._timezone) + self._latest_datetime = self.convert_datetime(self.end_datetime.date(), self._timezone) + + self.start_datetime = self.convert_datetime(self.start_datetime, self._timezone) + self.end_datetime = self.convert_datetime(self.end_datetime, self._timezone) + + @staticmethod + def convert_datetime(dt: [pd.Timestamp, datetime.date, str], timezone): + try: + dt = pd.Timestamp(dt, tz=timezone).timestamp() + dt = pd.Timestamp(dt, tz=tzlocal(), unit="s") + except ValueError as e: + pass + return dt @property @abc.abstractmethod def _timezone(self): raise NotImplementedError("rewrite get_timezone") - def save_stock(self, symbol, df: pd.DataFrame): - """save stock data to file + @staticmethod + def get_data_from_remote(symbol, interval, start, end, show_1min_logging: bool = False): + error_msg = f"{symbol}-{interval}-{start}-{end}" - Parameters - ---------- - symbol: str - stock code - df : pd.DataFrame - df.columns must contain "symbol" and "datetime" - """ - if df.empty: - logger.warning(f"{symbol} is empty") - return + def _show_logging_func(): + if interval == YahooCollector.INTERVAL_1min and show_1min_logging: + logger.warning(f"{error_msg}:{_resp}") - symbol = self.normalize_symbol(symbol) - symbol = code_to_fname(symbol) - stock_path = self.save_dir.joinpath(f"{symbol}.csv") - df["symbol"] = symbol - if stock_path.exists(): - _old_df = pd.read_csv(stock_path) - df = _old_df.append(df, sort=False) - df.to_csv(stock_path, index=False) + interval = "1m" if interval in ["1m", "1min"] else interval + try: + _resp = Ticker(symbol, asynchronous=False).history(interval=interval, start=start, end=end) + if isinstance(_resp, pd.DataFrame): + return _resp.reset_index() + elif isinstance(_resp, dict): + _temp_data = _resp.get(symbol, {}) + if isinstance(_temp_data, str) or ( + isinstance(_resp, dict) and _temp_data.get("indicators", {}).get("quote", None) is None + ): + _show_logging_func() + else: + _show_logging_func() + except Exception as e: + logger.warning(f"{error_msg}:{e}") - def _save_small_data(self, symbol, df): - if len(df) <= self.min_numbers_trading: - logger.warning(f"the number of trading days of {symbol} is less than {self.min_numbers_trading}!") - _temp = self._mini_symbol_map.setdefault(symbol, []) - _temp.append(df.copy()) - return None - else: - if symbol in self._mini_symbol_map: - self._mini_symbol_map.pop(symbol) - return symbol + def get_data( + self, symbol: str, interval: str, start_datetime: pd.Timestamp, end_datetime: pd.Timestamp + ) -> pd.DataFrame: + def _get_simple(start_, end_): + self.sleep() + _remote_interval = "1m" if interval == self.INTERVAL_1min else interval + return self.get_data_from_remote( + symbol, + interval=_remote_interval, + start=start_, + end=end_, + ) - def _get_data(self, symbol): _result = None - df = self.yahoo_data.get_data(symbol) - if isinstance(df, pd.DataFrame): - if not df.empty: - if self._check_small_data: - if self._save_small_data(symbol, df) is not None: - _result = symbol - self.save_stock(symbol, df) - else: - _result = symbol - self.save_stock(symbol, df) - return _result + if interval == self.INTERVAL_1d: + _result = _get_simple(start_datetime, end_datetime) + elif interval == self.INTERVAL_1min: + if self._next_datetime >= self._latest_datetime: + _result = _get_simple(start_datetime, end_datetime) + else: + _res = [] - def _collector(self, stock_list): + def _get_multi(start_, end_): + _resp = _get_simple(start_, end_) + if _resp is not None and not _resp.empty: + _res.append(_resp) - error_symbol = [] - with ThreadPoolExecutor(max_workers=self.max_workers) as executor: - with tqdm(total=len(stock_list)) as p_bar: - for _symbol, _result in zip(stock_list, executor.map(self._get_data, stock_list)): - if _result is None: - error_symbol.append(_symbol) - p_bar.update() - print(error_symbol) - logger.info(f"error symbol nums: {len(error_symbol)}") - logger.info(f"current get symbol nums: {len(stock_list)}") - error_symbol.extend(self._mini_symbol_map.keys()) - return sorted(set(error_symbol)) + for _s, _e in ( + (self.start_datetime, self._next_datetime), + (self._latest_datetime, self.end_datetime), + ): + _get_multi(_s, _e) + for _start in pd.date_range(self._next_datetime, self._latest_datetime, closed="left"): + _end = _start + pd.Timedelta(days=1) + _get_multi(_start, _end) + if _res: + _result = pd.concat(_res, sort=False).sort_values(["symbol", "date"]) + else: + raise ValueError(f"cannot support {self.interval}") + return pd.DataFrame() if _result is None else _result def collector_data(self): """collector data""" - logger.info("start collector yahoo data......") - stock_list = self.stock_list - for i in range(self._max_collector_count): - if not stock_list: - break - logger.info(f"getting data: {i+1}") - stock_list = self._collector(stock_list) - logger.info(f"{i+1} finish.") - for _symbol, _df_list in self._mini_symbol_map.items(): - self.save_stock(_symbol, pd.concat(_df_list, sort=False).drop_duplicates(["date"]).sort_values(["date"])) - if self._mini_symbol_map: - logger.warning(f"less than {self.min_numbers_trading} stock list: {list(self._mini_symbol_map.keys())}") - logger.info(f"total {len(self.stock_list)}, error: {len(set(stock_list))}") - + super(YahooCollector, self).collector_data() self.download_index_data() @abc.abstractmethod @@ -329,11 +183,6 @@ class YahooCollector: """download index data""" raise NotImplementedError("rewrite download_index_data") - @abc.abstractmethod - def normalize_symbol(self, symbol: str): - """normalize symbol""" - raise NotImplementedError("rewrite normalize_symbol") - class YahooCollectorCN(YahooCollector, ABC): def get_stock_list(self): @@ -360,8 +209,8 @@ class YahooCollectorCN1d(YahooCollectorCN): def download_index_data(self): # TODO: from MSN _format = "%Y%m%d" - _begin = self.yahoo_data.start_datetime.strftime(_format) - _end = (self.yahoo_data.end_datetime + pd.Timedelta(days=-1)).strftime(_format) + _begin = self.start_datetime.strftime(_format) + _end = (self.end_datetime + pd.Timedelta(days=-1)).strftime(_format) for _index_name, _index_code in {"csi300": "000300", "csi100": "000903"}.items(): logger.info(f"get bench data: {_index_name}({_index_code})......") try: @@ -396,7 +245,7 @@ class YahooCollectorCN1min(YahooCollectorCN): def download_index_data(self): # TODO: 1m - logger.warning(f"{self.__class__.__name__} {self._interval} does not support: download_index_data") + logger.warning(f"{self.__class__.__name__} {self.interval} does not support: download_index_data") class YahooCollectorUS(YahooCollector, ABC): @@ -433,29 +282,10 @@ class YahooCollectorUS1min(YahooCollectorUS): return 60 * 6.5 * 5 -class YahooNormalize: +class YahooNormalize(BaseNormalize): COLUMNS = ["open", "close", "high", "low", "volume"] DAILY_FORMAT = "%Y-%m-%d" - def __init__( - self, - date_field_name: str = "date", - symbol_field_name: str = "symbol", - ): - """ - - Parameters - ---------- - date_field_name: str - date field name, default is date - symbol_field_name: str - symbol field name, default is symbol - """ - self._date_field_name = date_field_name - self._symbol_field_name = symbol_field_name - - self._calendar_list = self._get_calendar_list() - @staticmethod def normalize_yahoo( df: pd.DataFrame, @@ -498,11 +328,6 @@ class YahooNormalize: df = self.adjusted_price(df) return df - @abc.abstractmethod - def _get_calendar_list(self): - """Get benchmark calendar""" - raise NotImplementedError("") - @abc.abstractmethod def adjusted_price(self, df: pd.DataFrame) -> pd.DataFrame: """adjusted price""" @@ -618,7 +443,9 @@ class YahooNormalize1min(YahooNormalize, ABC): # get 1d data from yahoo _start = pd.Timestamp(df[self._date_field_name].min()).strftime(self.DAILY_FORMAT) _end = (pd.Timestamp(df[self._date_field_name].max()) + pd.Timedelta(days=1)).strftime(self.DAILY_FORMAT) - data_1d = YahooData.get_data_from_remote(self.symbol_to_yahoo(symbol), interval="1d", start=_start, end=_end) + data_1d = YahooCollector.get_data_from_remote( + self.symbol_to_yahoo(symbol), interval="1d", start=_start, end=_end + ) if data_1d is None or data_1d.empty: df["factor"] = 1 # TODO: np.nan or 1 or 0 @@ -723,62 +550,8 @@ class YahooNormalizeCN1min(YahooNormalizeCN, YahooNormalize1min): return get_calendar_list("ALL") -class Normalize: - def __init__( - self, - source_dir: [str, Path], - target_dir: [str, Path], - normalize_class: Type[YahooNormalize], - max_workers: int = 16, - date_field_name: str = "date", - symbol_field_name: str = "symbol", - ): - """ - - Parameters - ---------- - source_dir: str or Path - The directory where the raw data collected from the Internet is saved - target_dir: str or Path - Directory for normalize data - normalize_class: Type[YahooNormalize] - normalize class - max_workers: int - Concurrent number, default is 16 - date_field_name: str - date field name, default is date - symbol_field_name: str - symbol field name, default is symbol - """ - if not (source_dir and target_dir): - raise ValueError("source_dir and target_dir cannot be None") - self._source_dir = Path(source_dir).expanduser() - self._target_dir = Path(target_dir).expanduser() - self._target_dir.mkdir(parents=True, exist_ok=True) - - self._max_workers = max_workers - - self._normalize_obj = normalize_class(date_field_name=date_field_name, symbol_field_name=symbol_field_name) - - def _executor(self, file_path: Path): - file_path = Path(file_path) - df = pd.read_csv(file_path) - df = self._normalize_obj.normalize(df) - if not df.empty: - df.to_csv(self._target_dir.joinpath(file_path.name), index=False) - - def normalize(self): - logger.info("normalize data......") - - with ProcessPoolExecutor(max_workers=self._max_workers) as worker: - file_list = list(self._source_dir.glob("*.csv")) - with tqdm(total=len(file_list)) as p_bar: - for _ in worker.map(self._executor, file_list): - p_bar.update() - - -class Run: - def __init__(self, source_dir=None, normalize_dir=None, max_workers=4, region=REGION_CN): +class Run(BaseRun): + def __init__(self, source_dir=None, normalize_dir=None, max_workers=4, interval="1d", region=REGION_CN): """ Parameters @@ -789,23 +562,26 @@ class Run: Directory for normalize data, default "Path(__file__).parent/normalize" max_workers: int Concurrent number, default is 4 + interval: str + freq, value from [1min, 1d], default 1d region: str region, value from ["CN", "US"], default "CN" """ - if source_dir is None: - source_dir = CUR_DIR.joinpath("source") - self.source_dir = Path(source_dir).expanduser().resolve() - self.source_dir.mkdir(parents=True, exist_ok=True) - - if normalize_dir is None: - normalize_dir = CUR_DIR.joinpath("normalize") - self.normalize_dir = Path(normalize_dir).expanduser().resolve() - self.normalize_dir.mkdir(parents=True, exist_ok=True) - - self._cur_module = importlib.import_module("collector") - self.max_workers = max_workers + super().__init__(source_dir, normalize_dir, max_workers, interval) self.region = region + @property + def collector_class_name(self): + return f"YahooCollector{self.region.upper()}{self.interval}" + + @property + def normalize_class_name(self): + return f"YahooNormalize{self.region.upper()}{self.interval}" + + @property + def default_base_dir(self) -> [Path, str]: + return CUR_DIR + def download_data( self, max_collector_count=2, @@ -815,7 +591,6 @@ class Run: interval="1d", check_data_length=False, limit_nums=None, - show_1min_logging=False, ): """download data from Internet @@ -835,8 +610,6 @@ class Run: check data length, by default False limit_nums: int using for debug, by default None - show_1min_logging: bool - show 1m logging, by default False; if True, there may be many warning logs Examples --------- @@ -846,29 +619,13 @@ class Run: $ python collector.py download_data --source_dir ~/.qlib/stock_data/source --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1m """ - _class = getattr( - self._cur_module, f"YahooCollector{self.region.upper()}{interval}" - ) # type: Type[YahooCollector] - _class( - self.source_dir, - max_workers=self.max_workers, - max_collector_count=max_collector_count, - delay=delay, - start=start, - end=end, - interval=interval, - check_data_length=check_data_length, - limit_nums=limit_nums, - show_1min_logging=show_1min_logging, - ).collector_data() + super(Run, self).download_data(max_collector_count, delay, start, end, interval, check_data_length, limit_nums) - def normalize_data(self, interval: str = "1d", date_field_name: str = "date", symbol_field_name: str = "symbol"): + def normalize_data(self, date_field_name: str = "date", symbol_field_name: str = "symbol"): """normalize data Parameters ---------- - interval: str - freq, value from [1min, 1d], default 1d date_field_name: str date field name, default date symbol_field_name: str @@ -878,16 +635,7 @@ class Run: --------- $ python collector.py normalize_data --source_dir ~/.qlib/stock_data/source --normalize_dir ~/.qlib/stock_data/normalize --region CN --interval 1d """ - _class = getattr(self._cur_module, f"YahooNormalize{self.region.upper()}{interval}") - yc = Normalize( - source_dir=self.source_dir, - target_dir=self.normalize_dir, - normalize_class=_class, - max_workers=self.max_workers, - date_field_name=date_field_name, - symbol_field_name=symbol_field_name, - ) - yc.normalize() + super(Run, self).normalize_data(date_field_name, symbol_field_name) if __name__ == "__main__": From 67fbdafe7656aae9d9b51b72391df4cba72e5f97 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Fri, 12 Mar 2021 02:42:25 +0000 Subject: [PATCH 093/134] Fix many bugs in TabNet and use_gpu --- .gitignore | 2 ++ examples/benchmarks/README.md | 2 +- .../benchmarks/TabNet/pretrain/best.model | Bin 1955014 -> 0 bytes .../workflow_config_TabNet_Alpha158.yaml | 2 +- qlib/contrib/model/pytorch_alstm.py | 5 ++- qlib/contrib/model/pytorch_alstm_ts.py | 10 +++--- qlib/contrib/model/pytorch_gats.py | 9 ++--- qlib/contrib/model/pytorch_gats_ts.py | 10 +++--- qlib/contrib/model/pytorch_gru.py | 10 +++--- qlib/contrib/model/pytorch_gru_ts.py | 5 ++- qlib/contrib/model/pytorch_lstm.py | 5 ++- qlib/contrib/model/pytorch_lstm_ts.py | 10 +++--- qlib/contrib/model/pytorch_nn.py | 16 ++++----- qlib/contrib/model/pytorch_tabnet.py | 33 +++++++++++------- 14 files changed, 70 insertions(+), 49 deletions(-) delete mode 100644 examples/benchmarks/TabNet/pretrain/best.model diff --git a/.gitignore b/.gitignore index 0ddd5d21f..33a2a2530 100644 --- a/.gitignore +++ b/.gitignore @@ -36,3 +36,5 @@ tags .vscode/ *.swp + +./pretrain diff --git a/examples/benchmarks/README.md b/examples/benchmarks/README.md index c5bfced6d..f1e7437fa 100644 --- a/examples/benchmarks/README.md +++ b/examples/benchmarks/README.md @@ -17,6 +17,7 @@ The numbers shown below demonstrate the performance of the entire `workflow` of | ALSTM (Yao Qin, et al.) | Alpha360 | 0.0493±0.01 | 0.3778±0.06| 0.0585±0.00 | 0.4606±0.04 | 0.0513±0.03 | 0.6727±0.38| -0.1085±0.02 | | GATs (Petar Velickovic, et al.) | Alpha360 | 0.0475±0.00 | 0.3515±0.02| 0.0592±0.00 | 0.4585±0.01 | 0.0876±0.02 | 1.1513±0.27| -0.0795±0.02 | | DoubleEnsemble (Chuheng Zhang, et al.) | Alpha360 | 0.0407±0.00| 0.3053±0.00 | 0.0490±0.00 | 0.3840±0.00 | 0.0380±0.02 | 0.5000±0.21 | -0.0984±0.02 | + ## Alpha158 dataset | Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown | |---|---|---|---|---|---|---|---|---| @@ -25,7 +26,6 @@ The numbers shown below demonstrate the performance of the entire `workflow` of | XGBoost (Tianqi Chen, et al.) | Alpha158 | 0.0481±0.00 | 0.3659±0.00| 0.0495±0.00 | 0.4033±0.00 | 0.1111±0.00 | 1.2915±0.00| -0.0893±0.00 | | LightGBM (Guolin Ke, et al.) | Alpha158 | 0.0475±0.00 | 0.3979±0.00| 0.0485±0.00 | 0.4123±0.00 | 0.1143±0.00 | 1.2744±0.00| -0.0800±0.00 | | MLP | Alpha158 | 0.0358±0.00 | 0.2738±0.03| 0.0425±0.00 | 0.3221±0.01 | 0.0836±0.02 | 1.0323±0.25| -0.1127±0.02 | -| TabNet with pretrain (Sercan O. Arikm et al) | Alpha158 | 0.0344±0.00|0.205±0.11|0.0398±0.00 |0.3479±0.01|0.0827±0.02|1.1141±0.32 |-0.0925±0.02 | | TFT (Bryan Lim, et al.) | Alpha158 (with selected 20 features) | 0.0343±0.00 | 0.2071±0.02| 0.0107±0.00 | 0.0660±0.02 | 0.0623±0.02 | 0.5818±0.20| -0.1762±0.01 | | GRU (Kyunghyun Cho, et al.) | Alpha158 (with selected 20 features) | 0.0311±0.00 | 0.2418±0.04| 0.0425±0.00 | 0.3434±0.02 | 0.0330±0.02 | 0.4805±0.30| -0.1021±0.02 | | LSTM (Sepp Hochreiter, et al.) | Alpha158 (with selected 20 features) | 0.0312±0.00 | 0.2394±0.04| 0.0418±0.00 | 0.3324±0.03 | 0.0298±0.02 | 0.4198±0.33| -0.1348±0.03 | diff --git a/examples/benchmarks/TabNet/pretrain/best.model b/examples/benchmarks/TabNet/pretrain/best.model deleted file mode 100644 index a85cbe392a4789d84331692bde397a4e129eb0b8..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1955014 zcmb511zg-r6Njk-b?PqE6_;K%b@$Y$EA`UelDbi&?n>R=y<9eRcXxLe>iYdRArM~f 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