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120 lines
3.7 KiB
Python
120 lines
3.7 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import sys
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from pathlib import Path
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import qlib
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import pandas as pd
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from qlib.config import REG_CN
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from qlib.contrib.model.gbdt import LGBModel
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from qlib.contrib.estimator.handler import QLibDataHandlerClose
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from qlib.contrib.strategy.strategy import TopkDropoutStrategy
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from qlib.contrib.evaluate import (
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backtest as normal_backtest,
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risk_analysis,
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)
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from qlib.utils import exists_qlib_data
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if __name__ == "__main__":
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# use default data
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provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
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if not exists_qlib_data(provider_uri):
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print(f"Qlib data is not found in {provider_uri}")
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sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts")))
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from get_data import GetData
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GetData().qlib_data_cn(provider_uri)
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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MARKET = "CSI300"
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BENCHMARK = "SH000300"
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###################################
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# train model
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###################################
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DATA_HANDLER_CONFIG = {
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"dropna_label": True,
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"start_date": "2008-01-01",
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"end_date": "2020-08-01",
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"market": MARKET,
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}
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TRAINER_CONFIG = {
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"train_start_date": "2008-01-01",
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"train_end_date": "2014-12-31",
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"validate_start_date": "2015-01-01",
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"validate_end_date": "2016-12-31",
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"test_start_date": "2017-01-01",
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"test_end_date": "2020-08-01",
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}
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# use default DataHandler
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# custom DataHandler, refer to: TODO: DataHandler API url
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x_train, y_train, x_validate, y_validate, x_test, y_test = QLibDataHandlerClose(
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**DATA_HANDLER_CONFIG
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).get_split_data(**TRAINER_CONFIG)
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MODEL_CONFIG = {
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"loss": "mse",
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"colsample_bytree": 0.8879,
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"learning_rate": 0.0421,
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"subsample": 0.8789,
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"lambda_l1": 205.6999,
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"lambda_l2": 580.9768,
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"max_depth": 8,
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"num_leaves": 210,
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"num_threads": 20,
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}
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# use default model
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# custom Model, refer to: TODO: Model API url
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model = LGBModel(**MODEL_CONFIG)
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model.fit(x_train, y_train, x_validate, y_validate)
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_pred = model.predict(x_test)
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_pred = pd.DataFrame(_pred, index=x_test.index, columns=y_test.columns)
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# backtest requires pred_score
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pred_score = pd.DataFrame(index=_pred.index)
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pred_score["score"] = _pred.iloc(axis=1)[0]
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# save pred_score to file
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pred_score_path = Path("~/tmp/qlib/pred_score.pkl").expanduser()
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pred_score_path.parent.mkdir(exist_ok=True, parents=True)
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pred_score.to_pickle(pred_score_path)
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###################################
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# backtest
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###################################
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STRATEGY_CONFIG = {
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"topk": 50,
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"n_drop": 5,
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}
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BACKTEST_CONFIG = {
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"verbose": False,
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"limit_threshold": 0.095,
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"account": 100000000,
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"benchmark": BENCHMARK,
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"deal_price": "close",
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"open_cost": 0.0005,
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"close_cost": 0.0015,
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"min_cost": 5,
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}
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# use default strategy
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# custom Strategy, refer to: TODO: Strategy API url
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strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
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report_normal, positions_normal = normal_backtest(pred_score, strategy=strategy, **BACKTEST_CONFIG)
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###################################
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# analyze
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# If need a more detailed analysis, refer to: examples/train_and_bakctest.ipynb
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###################################
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analysis = dict()
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analysis["sub_bench"] = risk_analysis(report_normal["return"] - report_normal["bench"])
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analysis["sub_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"] - report_normal["cost"])
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analysis_df = pd.concat(analysis) # type: pd.DataFrame
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print(analysis_df)
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