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131 lines
3.9 KiB
Python
131 lines
3.9 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.pytorch_lstm import LSTM
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from qlib.contrib.data.handler import ALPHA360_Denoise
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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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# from qlib.model.learner import train_model
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from qlib.utils import init_instance_by_config
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import pickle
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from tft import TFTModel
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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(target_dir=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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"start_time": "2008-01-01",
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"end_time": "2020-08-01",
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"fit_start_time": "2008-01-01",
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"fit_end_time": "2014-12-31",
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"instruments": MARKET,
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}
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TRAINER_CONFIG = {
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"train_start_time": "2008-01-01",
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"train_end_time": "2014-12-31",
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"validate_start_time": "2015-01-01",
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"validate_end_time": "2016-12-31",
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"test_start_time": "2017-01-01",
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"test_end_time": "2020-08-01",
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}
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task = {
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"dataset": {
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"class": "DatasetH",
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"module_path": "qlib.data.dataset",
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"kwargs": {
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'handler': {
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"class": "Alpha158",
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"module_path": "qlib.contrib.data.handler",
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"kwargs": DATA_HANDLER_CONFIG
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},
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'segments': {
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'train': ("2008-01-01", "2014-12-31"),
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'valid': ("2015-01-01", "2016-12-31",),
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'test': ("2017-01-01", "2020-08-01",),
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}
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}
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}
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# You shoud record the data in specific sequence
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# "record": ['SignalRecord', 'SigAnaRecord', 'PortAnaRecord'],
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}
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model = TFTModel()
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dataset = init_instance_by_config(task["dataset"])
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model.fit(dataset)
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pred_score, label_score = model.predict(dataset)
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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["excess_return_without_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"])
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analysis["excess_return_with_cost"] = risk_analysis(
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report_normal["return"] - report_normal["bench"] - report_normal["cost"]
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)
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analysis_df = pd.concat(analysis) # type: pd.DataFrame
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print(analysis_df)
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