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solve the conflict

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bxdd
2021-04-30 23:23:56 +08:00
parent d297a493b8 ee269b0914
commit e30df11a0b
69 changed files with 2289 additions and 317 deletions
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2
qlib/contrib/backtest/position.py
View File

@@ -130,7 +130,7 @@ class Position:
return self.position["cash"]
def get_stock_amount_dict(self):
"""generate stock amount dict {stock_id : amount of stock} """
"""generate stock amount dict {stock_id : amount of stock}"""
d = {}
stock_list = self.get_stock_list()
for stock_code in stock_list:

53
qlib/contrib/eva/alpha.py
View File

@@ -8,6 +8,59 @@ import pandas as pd
from typing import Tuple
def calc_long_short_prec(
pred: pd.Series, label: pd.Series, date_col="datetime", quantile: float = 0.2, dropna=False, is_alpha=False
) -> Tuple[pd.Series, pd.Series]:
"""
calculate the precision for long and short operation
:param pred/label: index is **pd.MultiIndex**, index name is **[datetime, instruments]**; columns names is **[score]**.
.. code-block:: python
score
datetime instrument
2020-12-01 09:30:00 SH600068 0.553634
SH600195 0.550017
SH600276 0.540321
SH600584 0.517297
SH600715 0.544674
label :
label
date_col :
date_col
Returns
-------
(pd.Series, pd.Series)
long precision and short precision in time level
"""
if is_alpha:
label = label - label.mean(level=date_col)
if int(1 / quantile) >= len(label.index.get_level_values(1).unique()):
raise ValueError("Need more instruments to calculate precision")
df = pd.DataFrame({"pred": pred, "label": label})
if dropna:
df.dropna(inplace=True)
group = df.groupby(level=date_col)
N = lambda x: int(len(x) * quantile)
# find the top/low quantile of prediction and treat them as long and short target
long = group.apply(lambda x: x.nlargest(N(x), columns="pred").label).reset_index(level=0, drop=True)
short = group.apply(lambda x: x.nsmallest(N(x), columns="pred").label).reset_index(level=0, drop=True)
groupll = long.groupby(date_col)
l_dom = groupll.apply(lambda x: x > 0)
l_c = groupll.count()
groups = short.groupby(date_col)
s_dom = groups.apply(lambda x: x < 0)
s_c = groups.count()
return (l_dom.groupby(date_col).sum() / l_c), (s_dom.groupby(date_col).sum() / s_c)
def calc_ic(pred: pd.Series, label: pd.Series, date_col="datetime", dropna=False) -> Tuple[pd.Series, pd.Series]:
"""calc_ic.

39
qlib/contrib/model/__init__.py
View File

@@ -0,0 +1,39 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
try:
from .catboost_model import CatBoostModel
except ModuleNotFoundError:
CatBoostModel = None
print("Please install necessary libs for CatBoostModel.")
try:
from .double_ensemble import DEnsembleModel
from .gbdt import LGBModel
except ModuleNotFoundError:
DEnsembleModel, LGBModel = None, None
print("Please install necessary libs for DEnsembleModel and LGBModel, such as lightgbm.")
try:
from .xgboost import XGBModel
except ModuleNotFoundError:
XGBModel = None
print("Please install necessary libs for XGBModel, such as xgboost.")
try:
from .linear import LinearModel
except ModuleNotFoundError:
LinearModel = None
print("Please install necessary libs for LinearModel, such as scipy and sklearn.")
# import pytorch models
try:
from .pytorch_alstm import ALSTM
from .pytorch_gats import GATs
from .pytorch_gru import GRU
from .pytorch_lstm import LSTM
from .pytorch_nn import DNNModelPytorch
from .pytorch_tabnet import TabnetModel
from .pytorch_sfm import SFM_Model
pytorch_classes = (ALSTM, GATs, GRU, LSTM, DNNModelPytorch, TabnetModel, SFM_Model)
except ModuleNotFoundError:
pytorch_classes = ()
print("Please install necessary libs for PyTorch models.")
all_model_classes = (CatBoostModel, DEnsembleModel, LGBModel, XGBModel, LinearModel) + pytorch_classes

5
qlib/contrib/model/catboost_model.py
View File

@@ -3,6 +3,7 @@
import numpy as np
import pandas as pd
from typing import Text, Union
from catboost import Pool, CatBoost
from catboost.utils import get_gpu_device_count
@@ -62,10 +63,10 @@ class CatBoostModel(Model):
evals_result["train"] = list(evals_result["learn"].values())[0]
evals_result["valid"] = list(evals_result["validation"].values())[0]
def predict(self, dataset):
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
if self.model is None:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature")
x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
return pd.Series(self.model.predict(x_test.values), index=x_test.index)

10
qlib/contrib/model/double_ensemble.py
View File

@@ -4,7 +4,7 @@
import lightgbm as lgb
import numpy as np
import pandas as pd
from typing import Text, Union
from ...model.base import Model
from ...data.dataset import DatasetH
from ...data.dataset.handler import DataHandlerLP
@@ -40,6 +40,10 @@ class DEnsembleModel(Model):
self.bins_sr = bins_sr
self.bins_fs = bins_fs
self.decay = decay
if sample_ratios is None: # the default values for sample_ratios
sample_ratios = [0.8, 0.7, 0.6, 0.5, 0.4]
if sub_weights is None: # the default values for sub_weights
sub_weights = [1.0, 0.2, 0.2, 0.2, 0.2, 0.2]
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
@@ -228,10 +232,10 @@ class DEnsembleModel(Model):
raise ValueError("not implemented yet")
return loss_curve
def predict(self, dataset):
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
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)
x_test = dataset.prepare(segment, 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]

6
qlib/contrib/model/gbdt.py
View File

@@ -4,7 +4,7 @@
import numpy as np
import pandas as pd
import lightgbm as lgb
from typing import Text, Union
from ...model.base import ModelFT
from ...data.dataset import DatasetH
from ...data.dataset.handler import DataHandlerLP
@@ -61,10 +61,10 @@ class LGBModel(ModelFT):
evals_result["train"] = list(evals_result["train"].values())[0]
evals_result["valid"] = list(evals_result["valid"].values())[0]
def predict(self, dataset):
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
if self.model is None:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature", data_key=DataHandlerLP.DK_I)
x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
return pd.Series(self.model.predict(x_test.values), index=x_test.index)
def finetune(self, dataset: DatasetH, num_boost_round=10, verbose_eval=20):

157
qlib/contrib/model/highfreq_gdbt_model.py Normal file
View File

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

6
qlib/contrib/model/linear.py
View File

@@ -3,7 +3,7 @@
import numpy as np
import pandas as pd
from typing import Text, Union
from scipy.optimize import nnls
from sklearn.linear_model import LinearRegression, Ridge, Lasso
@@ -84,8 +84,8 @@ class LinearModel(Model):
self.coef_ = coef
self.intercept_ = 0.0
def predict(self, dataset):
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
if self.coef_ is None:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature", data_key=DataHandlerLP.DK_I)
x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
return pd.Series(x_test.values @ self.coef_ + self.intercept_, index=x_test.index)

12
qlib/contrib/model/pytorch_alstm.py
View File

@@ -8,13 +8,9 @@ from __future__ import print_function
import os
import numpy as np
import pandas as pd
from typing import Text, Union
import copy
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
get_or_create_path,
drop_nan_by_y_index,
)
from ...utils import get_or_create_path
from ...log import get_module_logger
import torch
@@ -273,11 +269,11 @@ class ALSTM(Model):
if self.use_gpu:
torch.cuda.empty_cache()
def predict(self, dataset):
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
if not self.fitted:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature")
x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
index = x_test.index
self.ALSTM_model.eval()
x_values = x_test.values

12
qlib/contrib/model/pytorch_alstm_ts.py
View File

@@ -8,13 +8,9 @@ from __future__ import print_function
import os
import numpy as np
import pandas as pd
from typing import Text, Union
import copy
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
get_or_create_path,
drop_nan_by_y_index,
)
from ...utils import get_or_create_path
from ...log import get_module_logger
import torch
@@ -264,11 +260,11 @@ class ALSTM(Model):
if self.use_gpu:
torch.cuda.empty_cache()
def predict(self, dataset):
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
if not self.fitted:
raise ValueError("model is not fitted yet!")
dl_test = dataset.prepare("test", col_set=["feature", "label"], data_key=DataHandlerLP.DK_I)
dl_test = dataset.prepare(segment, col_set=["feature", "label"], data_key=DataHandlerLP.DK_I)
dl_test.config(fillna_type="ffill+bfill")
test_loader = DataLoader(dl_test, batch_size=self.batch_size, num_workers=self.n_jobs)
self.ALSTM_model.eval()

13
qlib/contrib/model/pytorch_gats.py
View File

@@ -8,13 +8,9 @@ from __future__ import print_function
import os
import numpy as np
import pandas as pd
from typing import Text, Union
import copy
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
get_or_create_path,
drop_nan_by_y_index,
)
from ...utils import get_or_create_path
from ...log import get_module_logger
import torch
import torch.nn as nn
@@ -83,7 +79,6 @@ class GATs(Model):
self.with_pretrain = with_pretrain
self.model_path = model_path
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.logger.info(
@@ -310,11 +305,11 @@ class GATs(Model):
if self.use_gpu:
torch.cuda.empty_cache()
def predict(self, dataset):
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
if not self.fitted:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature")
x_test = dataset.prepare(segment, col_set="feature")
index = x_test.index
self.GAT_model.eval()
x_values = x_test.values

7
qlib/contrib/model/pytorch_gats_ts.py
View File

@@ -9,12 +9,7 @@ import os
import numpy as np
import pandas as pd
import copy
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
get_or_create_path,
drop_nan_by_y_index,
)
from ...utils import get_or_create_path
from ...log import get_module_logger
import torch
import torch.nn as nn

12
qlib/contrib/model/pytorch_gru.py
View File

@@ -8,13 +8,9 @@ from __future__ import print_function
import os
import numpy as np
import pandas as pd
from typing import Text, Union
import copy
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
get_or_create_path,
drop_nan_by_y_index,
)
from ...utils import get_or_create_path
from ...log import get_module_logger
import torch
@@ -273,11 +269,11 @@ class GRU(Model):
if self.use_gpu:
torch.cuda.empty_cache()
def predict(self, dataset):
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
if not self.fitted:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature")
x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
index = x_test.index
self.gru_model.eval()
x_values = x_test.values

11
qlib/contrib/model/pytorch_gru_ts.py
View File

@@ -9,12 +9,7 @@ import os
import numpy as np
import pandas as pd
import copy
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
get_or_create_path,
drop_nan_by_y_index,
)
from ...utils import get_or_create_path
from ...log import get_module_logger
import torch
@@ -126,8 +121,8 @@ 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)))
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)

16
qlib/contrib/model/pytorch_lstm.py
View File

@@ -8,13 +8,9 @@ from __future__ import print_function
import os
import numpy as np
import pandas as pd
from typing import Text, Union
import copy
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
get_or_create_path,
drop_nan_by_y_index,
)
from ...utils import get_or_create_path
from ...log import get_module_logger
import torch
@@ -268,11 +264,11 @@ class LSTM(Model):
if self.use_gpu:
torch.cuda.empty_cache()
def predict(self, dataset):
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
if not self.fitted:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature")
x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
index = x_test.index
self.lstm_model.eval()
x_values = x_test.values
@@ -280,17 +276,13 @@ class LSTM(Model):
preds = []
for begin in range(sample_num)[:: self.batch_size]:
if sample_num - begin < self.batch_size:
end = sample_num
else:
end = begin + self.batch_size
x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
with torch.no_grad():
pred = self.lstm_model(x_batch).detach().cpu().numpy()
preds.append(pred)
return pd.Series(np.concatenate(preds), index=index)

7
qlib/contrib/model/pytorch_lstm_ts.py
View File

@@ -9,12 +9,7 @@ import os
import numpy as np
import pandas as pd
import copy
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
get_or_create_path,
drop_nan_by_y_index,
)
from ...utils import get_or_create_path
from ...log import get_module_logger
import torch

12
qlib/contrib/model/pytorch_nn.py
View File

@@ -8,6 +8,7 @@ from __future__ import print_function
import os
import numpy as np
import pandas as pd
from typing import Text, Union
from sklearn.metrics import roc_auc_score, mean_squared_error
import torch
@@ -18,7 +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, get_or_create_path, drop_nan_by_y_index
from ...utils import unpack_archive_with_buffer, save_multiple_parts_file, get_or_create_path
from ...log import get_module_logger
from ...workflow import R
@@ -48,8 +49,8 @@ class DNNModelPytorch(Model):
def __init__(
self,
input_dim,
output_dim,
input_dim=360,
output_dim=1,
layers=(256,),
lr=0.001,
max_steps=300,
@@ -271,13 +272,12 @@ class DNNModelPytorch(Model):
else:
raise NotImplementedError("loss {} is not supported!".format(loss_type))
def predict(self, dataset):
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
if not self.fitted:
raise ValueError("model is not fitted yet!")
x_test_pd = dataset.prepare("test", col_set="feature")
x_test_pd = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
x_test = torch.from_numpy(x_test_pd.values).float().to(self.device)
self.dnn_model.eval()
with torch.no_grad():
preds = self.dnn_model(x_test).detach().cpu().numpy()
return pd.Series(np.squeeze(preds), index=x_test_pd.index)

17
qlib/contrib/model/pytorch_sfm.py
View File

@@ -7,13 +7,9 @@ from __future__ import print_function
import os
import numpy as np
import pandas as pd
from typing import Text, Union
import copy
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
get_or_create_path,
drop_nan_by_y_index,
)
from ...utils import get_or_create_path
from ...log import get_module_logger
import torch
@@ -442,11 +438,11 @@ class SFM(Model):
raise ValueError("unknown metric `%s`" % self.metric)
def predict(self, dataset):
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
if not self.fitted:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature")
x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
index = x_test.index
self.sfm_model.eval()
x_values = x_test.values
@@ -459,10 +455,7 @@ class SFM(Model):
else:
end = begin + self.batch_size
x_batch = torch.from_numpy(x_values[begin:end]).float()
if self.device != "cpu":
x_batch = x_batch.to(self.device)
x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
with torch.no_grad():
pred = self.sfm_model(x_batch).detach().cpu().numpy()

12
qlib/contrib/model/pytorch_tabnet.py
View File

@@ -6,13 +6,9 @@ from __future__ import print_function
import os
import numpy as np
import pandas as pd
from typing import Text, Union
import copy
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
get_or_create_path,
drop_nan_by_y_index,
)
from ...utils import get_or_create_path
from ...log import get_module_logger
import torch
@@ -217,11 +213,11 @@ class TabnetModel(Model):
if self.use_gpu:
torch.cuda.empty_cache()
def predict(self, dataset):
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
if not self.fitted:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature", data_key=DataHandlerLP.DK_I)
x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
index = x_test.index
self.tabnet_model.eval()
x_values = torch.from_numpy(x_test.values)

6
qlib/contrib/model/xgboost.py
View File

@@ -4,7 +4,7 @@
import numpy as np
import pandas as pd
import xgboost as xgb
from typing import Text, Union
from ...model.base import Model
from ...data.dataset import DatasetH
from ...data.dataset.handler import DataHandlerLP
@@ -57,8 +57,8 @@ class XGBModel(Model):
evals_result["train"] = list(evals_result["train"].values())[0]
evals_result["valid"] = list(evals_result["valid"].values())[0]
def predict(self, dataset):
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
if self.model is None:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature")
x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
return pd.Series(self.model.predict(xgb.DMatrix(x_test.values)), index=x_test.index)

2
qlib/contrib/report/analysis_position/cumulative_return.py
View File

@@ -214,7 +214,7 @@ def cumulative_return_graph(
features_df = D.features(D.instruments('csi500'), ['Ref($close, -1)/$close - 1'], pred_df_dates.min(), pred_df_dates.max())
features_df.columns = ['label']
qcr.cumulative_return_graph(positions, report_normal_df, features_df)
qcr.analysis_position.cumulative_return_graph(positions, report_normal_df, features_df)
Graph desc:

2
qlib/contrib/report/analysis_position/rank_label.py
View File

@@ -94,7 +94,7 @@ def rank_label_graph(
features_df = D.features(D.instruments('csi500'), ['Ref($close, -1)/$close-1'], pred_df_dates.min(), pred_df_dates.max())
features_df.columns = ['label']
qcr.rank_label_graph(positions, features_df, pred_df_dates.min(), pred_df_dates.max())
qcr.analysis_position.rank_label_graph(positions, features_df, pred_df_dates.min(), pred_df_dates.max())
:param position: position data; **qlib.contrib.backtest.backtest.backtest** result.

2
qlib/contrib/report/analysis_position/report.py
View File

@@ -186,7 +186,7 @@ def report_graph(report_df: pd.DataFrame, show_notebook: bool = True) -> [list,
report_normal_df, _ = backtest(pred_df, strategy, **bparas)
qcr.report_graph(report_normal_df)
qcr.analysis_position.report_graph(report_normal_df)
:param report_df: **df.index.name** must be **date**, **df.columns** must contain **return**, **turnover**, **cost**, **bench**.

2
qlib/contrib/report/graph.py
View File

@@ -18,7 +18,7 @@ from ...utils import get_module_by_module_path
class BaseGraph:
""""""
""" """
_name = None

413
qlib/contrib/strategy/strategy.py Normal file
View File

@@ -0,0 +1,413 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import copy
import numpy as np
import pandas as pd
from ..backtest.order import Order
from .order_generator import OrderGenWInteract
# TODO: The base strategies will be moved out of contrib to core code
class BaseStrategy:
def __init__(self):
pass
def get_risk_degree(self, date):
"""get_risk_degree
Return the proportion of your total value you will used in investment.
Dynamically risk_degree will result in Market timing
"""
# It will use 95% amount of your total value by default
return 0.95
def generate_order_list(self, score_series, current, trade_exchange, pred_date, trade_date):
"""
DO NOT directly change the state of current
Parameters
-----------
score_series : pd.Series
stock_id , score.
current : Position()
current state of position.
DO NOT directly change the state of current.
trade_exchange : Exchange()
trade exchange.
pred_date : pd.Timestamp
predict date.
trade_date : pd.Timestamp
trade date.
"""
pass
def update(self, score_series, pred_date, trade_date):
"""User can use this method to update strategy state each trade date.
Parameters
-----------
score_series : pd.Series
stock_id , score.
pred_date : pd.Timestamp
oredict date.
trade_date : pd.Timestamp
trade date.
"""
pass
def init(self, **kwargs):
"""Some strategy need to be initial after been implemented,
User can use this method to init his strategy with parameters needed.
"""
pass
def get_init_args_from_model(self, model, init_date):
"""
This method only be used in 'online' module, it will generate the *args to initial the strategy.
:param
mode : model used in 'online' module.
"""
return {}
class StrategyWrapper:
"""
StrategyWrapper is a wrapper of another strategy.
By overriding some methods to make some changes on the basic strategy
Cost control and risk control will base on this class.
"""
def __init__(self, inner_strategy):
"""__init__
:param inner_strategy: set the inner strategy.
"""
self.inner_strategy = inner_strategy
def __getattr__(self, name):
"""__getattr__
:param name: If no implementation in this method. Call the method in the innter_strategy by default.
"""
return getattr(self.inner_strategy, name)
class AdjustTimer:
"""AdjustTimer
Responsible for timing of position adjusting
This is designed as multiple inheritance mechanism due to:
- the is_adjust may need access to the internel state of a strategy.
- it can be reguard as a enhancement to the existing strategy.
"""
# adjust position in each trade date
def is_adjust(self, trade_date):
"""is_adjust
Return if the strategy can adjust positions on `trade_date`
Will normally be used in strategy do trading with trade frequency
"""
return True
class ListAdjustTimer(AdjustTimer):
def __init__(self, adjust_dates=None):
"""__init__
:param adjust_dates: an iterable object, it will return a timelist for trading dates
"""
if adjust_dates is None:
# None indicates that all dates is OK for adjusting
self.adjust_dates = None
else:
self.adjust_dates = {pd.Timestamp(dt) for dt in adjust_dates}
def is_adjust(self, trade_date):
if self.adjust_dates is None:
return True
return pd.Timestamp(trade_date) in self.adjust_dates
class WeightStrategyBase(BaseStrategy, AdjustTimer):
def __init__(self, order_generator_cls_or_obj=OrderGenWInteract, *args, **kwargs):
super().__init__(*args, **kwargs)
if isinstance(order_generator_cls_or_obj, type):
self.order_generator = order_generator_cls_or_obj()
else:
self.order_generator = order_generator_cls_or_obj
def generate_target_weight_position(self, score, current, trade_date):
"""
Generate target position from score for this date and the current position.The cash is not considered in the position
Parameters
-----------
score : pd.Series
pred score for this trade date, index is stock_id, contain 'score' column.
current : Position()
current position.
trade_exchange : Exchange()
trade_date : pd.Timestamp
trade date.
"""
raise NotImplementedError()
def generate_order_list(self, score_series, current, trade_exchange, pred_date, trade_date):
"""
Parameters
-----------
score_series : pd.Seires
stock_id , score.
current : Position()
current of account.
trade_exchange : Exchange()
exchange.
trade_date : pd.Timestamp
date.
"""
# judge if to adjust
if not self.is_adjust(trade_date):
return []
# generate_order_list
# generate_target_weight_position() and generate_order_list_from_target_weight_position() to generate order_list
current_temp = copy.deepcopy(current)
target_weight_position = self.generate_target_weight_position(
score=score_series, current=current_temp, trade_date=trade_date
)
order_list = self.order_generator.generate_order_list_from_target_weight_position(
current=current_temp,
trade_exchange=trade_exchange,
risk_degree=self.get_risk_degree(trade_date),
target_weight_position=target_weight_position,
pred_date=pred_date,
trade_date=trade_date,
)
return order_list
class TopkDropoutStrategy(BaseStrategy, ListAdjustTimer):
def __init__(
self,
topk,
n_drop,
method_sell="bottom",
method_buy="top",
risk_degree=0.95,
thresh=1,
hold_thresh=1,
only_tradable=False,
**kwargs,
):
"""
Parameters
-----------
topk : int
the number of stocks in the portfolio.
n_drop : int
number of stocks to be replaced in each trading date.
method_sell : str
dropout method_sell, random/bottom.
method_buy : str
dropout method_buy, random/top.
risk_degree : float
position percentage of total value.
thresh : int
minimun holding days since last buy singal of the stock.
hold_thresh : int
minimum holding days
before sell stock , will check current.get_stock_count(order.stock_id) >= self.thresh.
only_tradable : bool
will the strategy only consider the tradable stock when buying and selling.
if only_tradable:
strategy will make buy sell decision without checking the tradable state of the stock.
else:
strategy will make decision with the tradable state of the stock info and avoid buy and sell them.
"""
super(TopkDropoutStrategy, self).__init__()
ListAdjustTimer.__init__(self, kwargs.get("adjust_dates", None))
self.topk = topk
self.n_drop = n_drop
self.method_sell = method_sell
self.method_buy = method_buy
self.risk_degree = risk_degree
self.thresh = thresh
# self.stock_count['code'] will be the days the stock has been hold
# since last buy signal. This is designed for thresh
self.stock_count = {}
self.hold_thresh = hold_thresh
self.only_tradable = only_tradable
def get_risk_degree(self, date):
"""get_risk_degree
Return the proportion of your total value you will used in investment.
Dynamically risk_degree will result in Market timing.
"""
# It will use 95% amoutn of your total value by default
return self.risk_degree
def generate_order_list(self, score_series, current, trade_exchange, pred_date, trade_date):
"""
Generate order list according to score_series at trade_date, will not change current.
Parameters
-----------
score_series : pd.Series
stock_id , score.
current : Position()
current of account.
trade_exchange : Exchange()
exchange.
pred_date : pd.Timestamp
predict date.
trade_date : pd.Timestamp
trade date.
"""
if not self.is_adjust(trade_date):
return []
if self.only_tradable:
# If The strategy only consider tradable stock when make decision
# It needs following actions to filter stocks
def get_first_n(l, n, reverse=False):
cur_n = 0
res = []
for si in reversed(l) if reverse else l:
if trade_exchange.is_stock_tradable(stock_id=si, trade_date=trade_date):
res.append(si)
cur_n += 1
if cur_n >= n:
break
return res[::-1] if reverse else res
def get_last_n(l, n):
return get_first_n(l, n, reverse=True)
def filter_stock(l):
return [si for si in l if trade_exchange.is_stock_tradable(stock_id=si, trade_date=trade_date)]
else:
# Otherwise, the stock will make decision with out the stock tradable info
def get_first_n(l, n):
return list(l)[:n]
def get_last_n(l, n):
return list(l)[-n:]
def filter_stock(l):
return l
current_temp = copy.deepcopy(current)
# generate order list for this adjust date
sell_order_list = []
buy_order_list = []
# load score
cash = current_temp.get_cash()
current_stock_list = current_temp.get_stock_list()
# last position (sorted by score)
last = score_series.reindex(current_stock_list).sort_values(ascending=False).index
# The new stocks today want to buy **at most**
if self.method_buy == "top":
today = get_first_n(
score_series[~score_series.index.isin(last)].sort_values(ascending=False).index,
self.n_drop + self.topk - len(last),
)
elif self.method_buy == "random":
topk_candi = get_first_n(score_series.sort_values(ascending=False).index, self.topk)
candi = list(filter(lambda x: x not in last, topk_candi))
n = self.n_drop + self.topk - len(last)
try:
today = np.random.choice(candi, n, replace=False)
except ValueError:
today = candi
else:
raise NotImplementedError(f"This type of input is not supported")
# combine(new stocks + last stocks), we will drop stocks from this list
# In case of dropping higher score stock and buying lower score stock.
comb = score_series.reindex(last.union(pd.Index(today))).sort_values(ascending=False).index
# Get the stock list we really want to sell (After filtering the case that we sell high and buy low)
if self.method_sell == "bottom":
sell = last[last.isin(get_last_n(comb, self.n_drop))]
elif self.method_sell == "random":
candi = filter_stock(last)
try:
sell = pd.Index(np.random.choice(candi, self.n_drop, replace=False) if len(last) else [])
except ValueError: # No enough candidates
sell = candi
else:
raise NotImplementedError(f"This type of input is not supported")
# Get the stock list we really want to buy
buy = today[: len(sell) + self.topk - len(last)]
# buy singal: if a stock falls into topk, it appear in the buy_sinal
buy_signal = score_series.sort_values(ascending=False).iloc[: self.topk].index
for code in current_stock_list:
if not trade_exchange.is_stock_tradable(stock_id=code, trade_date=trade_date):
continue
if code in sell:
# check hold limit
if self.stock_count[code] < self.thresh or current_temp.get_stock_count(code) < self.hold_thresh:
# can not sell this code
# no buy signal, but the stock is kept
self.stock_count[code] += 1
continue
# sell order
sell_amount = current_temp.get_stock_amount(code=code)
sell_order = Order(
stock_id=code,
amount=sell_amount,
trade_date=trade_date,
direction=Order.SELL, # 0 for sell, 1 for buy
factor=trade_exchange.get_factor(code, trade_date),
)
# is order executable
if trade_exchange.check_order(sell_order):
sell_order_list.append(sell_order)
trade_val, trade_cost, trade_price = trade_exchange.deal_order(sell_order, position=current_temp)
# update cash
cash += trade_val - trade_cost
# sold
del self.stock_count[code]
else:
# no buy signal, but the stock is kept
self.stock_count[code] += 1
elif code in buy_signal:
# NOTE: This is different from the original version
# get new buy signal
# Only the stock fall in to topk will produce buy signal
self.stock_count[code] = 1
else:
self.stock_count[code] += 1
# buy new stock
# note the current has been changed
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
# value = value / (1+trade_exchange.open_cost) # set open_cost limit
for code in buy:
# check is stock suspended
if not trade_exchange.is_stock_tradable(stock_id=code, trade_date=trade_date):
continue
# buy order
buy_price = trade_exchange.get_deal_price(stock_id=code, trade_date=trade_date)
buy_amount = value / buy_price
factor = trade_exchange.quote[(code, trade_date)]["$factor"]
buy_amount = trade_exchange.round_amount_by_trade_unit(buy_amount, factor)
buy_order = Order(
stock_id=code,
amount=buy_amount,
trade_date=trade_date,
direction=Order.BUY, # 1 for buy
factor=factor,
)
buy_order_list.append(buy_order)
self.stock_count[code] = 1
return sell_order_list + buy_order_list

4
qlib/contrib/workflow/__init__.py
View File

@@ -0,0 +1,4 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from .record_temp import MultiSegRecord
from .record_temp import SignalMseRecord

54
qlib/contrib/workflow/record_temp.py
View File

@@ -1,16 +1,60 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import re
import logging
import pandas as pd
from sklearn.metrics import mean_squared_error
from pprint import pprint
import numpy as np
from sklearn.metrics import mean_squared_error
from typing import Dict, Text, Any
from ...contrib.eva.alpha import calc_ic
from ...workflow.record_temp import RecordTemp
from ...workflow.record_temp import SignalRecord
from ...data import dataset as qlib_dataset
from ...log import get_module_logger
logger = get_module_logger("workflow", "INFO")
logger = get_module_logger("workflow", logging.INFO)
class MultiSegRecord(RecordTemp):
"""
This is the multiple segments signal record class that generates the signal prediction.
This class inherits the ``RecordTemp`` class.
"""
def __init__(self, model, dataset, recorder=None):
super().__init__(recorder=recorder)
if not isinstance(dataset, qlib_dataset.DatasetH):
raise ValueError("The type of dataset is not DatasetH instead of {:}".format(type(dataset)))
self.model = model
self.dataset = dataset
def generate(self, segments: Dict[Text, Any], save: bool = False):
for key, segment in segments.items():
predics = self.model.predict(self.dataset, segment)
if isinstance(predics, pd.Series):
predics = predics.to_frame("score")
labels = self.dataset.prepare(
segments=segment, col_set="label", data_key=qlib_dataset.handler.DataHandlerLP.DK_R
)
# Compute the IC and Rank IC
ic, ric = calc_ic(predics.iloc[:, 0], labels.iloc[:, 0])
results = {"all-IC": ic, "mean-IC": ic.mean(), "all-Rank-IC": ric, "mean-Rank-IC": ric.mean()}
logger.info("--- Results for {:} ({:}) ---".format(key, segment))
ic_x100, ric_x100 = ic * 100, ric * 100
logger.info("IC: {:.4f}%".format(ic_x100.mean()))
logger.info("ICIR: {:.4f}%".format(ic_x100.mean() / ic_x100.std()))
logger.info("Rank IC: {:.4f}%".format(ric_x100.mean()))
logger.info("Rank ICIR: {:.4f}%".format(ric_x100.mean() / ric_x100.std()))
if save:
save_name = "results-{:}.pkl".format(key)
self.recorder.save_objects(**{save_name: results})
logger.info(
"The record '{:}' has been saved as the artifact of the Experiment {:}".format(
save_name, self.recorder.experiment_id
)
)
class SignalMseRecord(SignalRecord):
@@ -38,7 +82,7 @@ class SignalMseRecord(SignalRecord):
objects = {"mse.pkl": mse, "rmse.pkl": np.sqrt(mse)}
self.recorder.log_metrics(**metrics)
self.recorder.save_objects(**objects, artifact_path=self.get_path())
pprint(metrics)
logger.info("The evaluation results in SignalMseRecord is {:}".format(metrics))
def list(self):
paths = [self.get_path("mse.pkl"), self.get_path("rmse.pkl")]