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update high freq demo
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Alex Wang
157
qlib/contrib/model/highfreq_gdbt_model.py
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157
qlib/contrib/model/highfreq_gdbt_model.py
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import numpy as np
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import pandas as pd
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import lightgbm as lgb
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from qlib.model.base import ModelFT
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from qlib.data.dataset import DatasetH
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from qlib.data.dataset.handler import DataHandlerLP
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import warnings
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class HF_LGBModel(ModelFT):
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"""LightGBM Model"""
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def __init__(self, loss="mse", **kwargs):
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if loss not in {"mse", "binary"}:
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raise NotImplementedError
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self.params = {"objective": loss, "verbosity": -1}
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self.params.update(kwargs)
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self.model = None
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def _cal_signal_metrics(self, y_test, l_cut, r_cut):
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"""
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Calcaute the signal metrics by daily level
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"""
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up_pre, down_pre = [], []
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up_alpha_ll, down_alpha_ll = [], []
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for date in y_test.index.get_level_values(0).unique():
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df_res = y_test.loc[date].sort_values("pred")
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if int(l_cut * len(df_res)) < 10:
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warnings.warn("Warning: threhold is too low or instruments number is not enough")
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continue
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top = df_res.iloc[: int(l_cut * len(df_res))]
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bottom = df_res.iloc[int(r_cut * len(df_res)) :]
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down_precision = len(top[top[top.columns[0]] < 0]) / (len(top))
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up_precision = len(bottom[bottom[top.columns[0]] > 0]) / (len(bottom))
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down_alpha = top[top.columns[0]].mean()
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up_alpha = bottom[bottom.columns[0]].mean()
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up_pre.append(up_precision)
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down_pre.append(down_precision)
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up_alpha_ll.append(up_alpha)
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down_alpha_ll.append(down_alpha)
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return (
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np.array(up_pre).mean(),
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np.array(down_pre).mean(),
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np.array(up_alpha_ll).mean(),
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np.array(down_alpha_ll).mean(),
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)
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def hf_signal_test(self, dataset: DatasetH, threhold=0.2):
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"""
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Test the sigal in high frequency test set
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"""
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if self.model == None:
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raise ValueError("Model hasn't been trained yet")
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df_test = dataset.prepare("test", col_set=["feature", "label"], data_key=DataHandlerLP.DK_I)
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df_test.dropna(inplace=True)
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x_test, y_test = df_test["feature"], df_test["label"]
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# Convert label into alpha
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y_test[y_test.columns[0]] = y_test[y_test.columns[0]] - y_test[y_test.columns[0]].mean(level=0)
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res = pd.Series(self.model.predict(x_test.values), index=x_test.index)
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y_test["pred"] = res
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up_p, down_p, up_a, down_a = self._cal_signal_metrics(y_test, threhold, 1 - threhold)
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print("===============================")
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print("High frequency signal test")
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print("===============================")
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print("Test set precision: ")
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print("Positive precision: {}, Negative precision: {}".format(up_p, down_p))
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print("Test Alpha Average in test set: ")
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print("Positive average alpha: {}, Negative average alpha: {}".format(up_a, down_a))
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def _prepare_data(self, dataset: DatasetH):
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df_train, df_valid = dataset.prepare(
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["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
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)
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x_train, y_train = df_train["feature"], df_train["label"]
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x_valid, y_valid = df_train["feature"], df_valid["label"]
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if y_train.values.ndim == 2 and y_train.values.shape[1] == 1:
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l_name = df_train["label"].columns[0]
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# Convert label into alpha
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df_train["label"][l_name] = df_train["label"][l_name] - df_train["label"][l_name].mean(level=0)
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df_valid["label"][l_name] = df_valid["label"][l_name] - df_valid["label"][l_name].mean(level=0)
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mapping_fn = lambda x: 0 if x < 0 else 1
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df_train["label_c"] = df_train["label"][l_name].apply(mapping_fn)
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df_valid["label_c"] = df_valid["label"][l_name].apply(mapping_fn)
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x_train, y_train = df_train["feature"], df_train["label_c"].values
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x_valid, y_valid = df_valid["feature"], df_valid["label_c"].values
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else:
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raise ValueError("LightGBM doesn't support multi-label training")
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dtrain = lgb.Dataset(x_train.values, label=y_train)
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dvalid = lgb.Dataset(x_valid.values, label=y_valid)
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return dtrain, dvalid
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def fit(
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self,
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dataset: DatasetH,
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num_boost_round=1000,
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early_stopping_rounds=50,
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verbose_eval=20,
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evals_result=dict(),
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**kwargs
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):
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dtrain, dvalid = self._prepare_data(dataset)
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self.model = lgb.train(
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self.params,
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dtrain,
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num_boost_round=num_boost_round,
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valid_sets=[dtrain, dvalid],
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valid_names=["train", "valid"],
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early_stopping_rounds=early_stopping_rounds,
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verbose_eval=verbose_eval,
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evals_result=evals_result,
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**kwargs
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)
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evals_result["train"] = list(evals_result["train"].values())[0]
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evals_result["valid"] = list(evals_result["valid"].values())[0]
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def predict(self, dataset):
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if self.model is None:
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raise ValueError("model is not fitted yet!")
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x_test = dataset.prepare("test", col_set="feature", data_key=DataHandlerLP.DK_I)
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return pd.Series(self.model.predict(x_test.values), index=x_test.index)
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def finetune(self, dataset: DatasetH, num_boost_round=10, verbose_eval=20):
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"""
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finetune model
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Parameters
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----------
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dataset : DatasetH
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dataset for finetuning
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num_boost_round : int
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number of round to finetune model
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verbose_eval : int
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verbose level
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"""
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# Based on existing model and finetune by train more rounds
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dtrain, _ = self._prepare_data(dataset)
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self.model = lgb.train(
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self.params,
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dtrain,
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num_boost_round=num_boost_round,
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init_model=self.model,
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valid_sets=[dtrain],
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valid_names=["train"],
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verbose_eval=verbose_eval,
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)
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