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263 lines
9.2 KiB
Python
263 lines
9.2 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import qlib
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import fire
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from qlib.config import REG_CN, HIGH_FREQ_CONFIG
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from qlib.data import D
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from qlib.utils import exists_qlib_data, init_instance_by_config, flatten_dict
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from qlib.workflow import R
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from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
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from qlib.tests.data import GetData
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from qlib.backtest import collect_data
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class NestedDecisonExecutionWorkflow:
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market = "csi300"
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benchmark = "SH000300"
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data_handler_config = {
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"start_time": "2008-01-01",
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"end_time": "2021-01-20",
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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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task = {
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"model": {
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"class": "LGBModel",
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"module_path": "qlib.contrib.model.gbdt",
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"kwargs": {
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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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},
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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", "2021-01-20"),
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},
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},
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},
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}
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port_analysis_config = {
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"executor": {
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"class": "NestedExecutor",
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"module_path": "qlib.backtest.executor",
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"kwargs": {
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"time_per_step": "week",
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"inner_executor": {
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"class": "SimulatorExecutor",
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"module_path": "qlib.backtest.executor",
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"kwargs": {
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"time_per_step": "day",
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"verbose": True,
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"generate_report": True,
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},
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},
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"inner_strategy": {
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"class": "SBBStrategyEMA",
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"module_path": "qlib.contrib.strategy.rule_strategy",
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"kwargs": {
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"freq": "day",
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"instruments": market,
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},
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},
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"generate_report": True,
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"track_data": True,
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},
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},
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"backtest": {
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"start_time": "2017-01-01",
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"end_time": "2020-08-01",
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"account": 100000000,
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"benchmark": benchmark,
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"exchange_kwargs": {
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"freq": "day",
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"limit_threshold": 0.095,
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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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},
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}
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def _init_qlib(self):
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"""initialize qlib"""
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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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GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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def _train_model(self, model, dataset):
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with R.start(experiment_name="train"):
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R.log_params(**flatten_dict(self.task))
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model.fit(dataset)
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R.save_objects(**{"params.pkl": model})
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# prediction
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recorder = R.get_recorder()
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sr = SignalRecord(model, dataset, recorder)
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sr.generate()
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def backtest(self):
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self._init_qlib()
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model = init_instance_by_config(self.task["model"])
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dataset = init_instance_by_config(self.task["dataset"])
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self._train_model(model, dataset)
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strategy_config = {
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"class": "TopkDropoutStrategy",
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"module_path": "qlib.contrib.strategy.model_strategy",
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"kwargs": {
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"model": model,
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"dataset": dataset,
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"topk": 50,
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"n_drop": 5,
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},
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}
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self.port_analysis_config["strategy"] = strategy_config
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with R.start(experiment_name="backtest"):
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recorder = R.get_recorder()
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par = PortAnaRecord(recorder, self.port_analysis_config, "day")
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par.generate()
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def collect_data(self):
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self._init_qlib()
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model = init_instance_by_config(self.task["model"])
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dataset = init_instance_by_config(self.task["dataset"])
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self._train_model(model, dataset)
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executor_config = self.port_analysis_config["executor"]
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backtest_config = self.port_analysis_config["backtest"]
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strategy_config = {
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"class": "TopkDropoutStrategy",
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"module_path": "qlib.contrib.strategy.model_strategy",
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"kwargs": {
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"model": model,
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"dataset": dataset,
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"topk": 50,
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"n_drop": 5,
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},
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}
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data_generator = collect_data(executor=executor_config, strategy=strategy_config, **backtest_config)
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for trade_decision in data_generator:
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print(trade_decision)
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def _init_qlib_with_backend(self):
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provider_uri_1min = HIGH_FREQ_CONFIG.get("provider_uri")
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if not exists_qlib_data(provider_uri_1min):
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print(f"Qlib data is not found in {provider_uri_1min}")
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GetData().qlib_data(target_dir=provider_uri_1min, interval="1min", region=REG_CN)
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# TODO: update latest data
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provider_uri_day = "~/.qlib/qlib_data/cn_data" # target_dir
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if not exists_qlib_data(provider_uri_day):
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print(f"Qlib data is not found in {provider_uri_day}")
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GetData().qlib_data(target_dir=provider_uri_day, region=REG_CN)
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provider_uri_map = {"1min": provider_uri_1min, "day": provider_uri_day}
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client_config = {
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"calendar_provider": {
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"class": "LocalCalendarProvider",
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"module_path": "qlib.data.data",
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"kwargs": {
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"backend": {
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"class": "FileCalendarStorage",
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"module_path": "qlib.data.storage.file_storage",
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"kwargs": {"provider_uri_map": provider_uri_map},
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}
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},
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},
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"feature_provider": {
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"class": "LocalFeatureProvider",
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"module_path": "qlib.data.data",
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"kwargs": {
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"backend": {
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"class": "FileFeatureStorage",
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"module_path": "qlib.data.storage.file_storage",
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"kwargs": {"provider_uri_map": provider_uri_map},
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}
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},
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},
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}
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qlib.init(provider_uri=provider_uri_day, **client_config)
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def _get_highfreq_config(self, model, dataset):
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executor_config = self.port_analysis_config["executor"]
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# update executor with hierarchical decison freq ["day", "1min"]
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executor_config["kwargs"]["time_per_step"] = "day"
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executor_config["kwargs"]["inner_executor"]["kwargs"]["time_per_step"] = "15min"
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backtest_config = self.port_analysis_config["backtest"]
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# yahoo highfreq data time
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backtest_config["start_time"] = "2020-09-20"
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backtest_config["end_time"] = "2021-01-20"
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# update benchmark, yahoo data don't have SH000300
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instruments = D.instruments(market="csi300")
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instrument_list = D.list_instruments(instruments=instruments, as_list=True)
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backtest_config["benchmark"] = instrument_list
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# update exchange config
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backtest_config["exchange_kwargs"]["freq"] = "1min"
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# set strategy
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strategy_config = {
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"class": "TopkDropoutStrategy",
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"module_path": "qlib.contrib.strategy.model_strategy",
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"kwargs": {
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"model": model,
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"dataset": dataset,
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"topk": 50,
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"n_drop": 5,
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},
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}
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return executor_config, strategy_config, backtest_config
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def backtest_highfreq(self):
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self._init_qlib_with_backend()
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model = init_instance_by_config(self.task["model"])
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dataset = init_instance_by_config(self.task["dataset"])
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self._train_model(model, dataset)
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executor_config, strategy_config, backtest_config = self._get_highfreq_config(model, dataset)
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highfreq_port_analysis_config = {
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"executor": executor_config,
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"strategy": strategy_config,
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"backtest": backtest_config,
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}
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with R.start(experiment_name="backtest_highfreq"):
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recorder = R.get_recorder()
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par = PortAnaRecord(recorder, highfreq_port_analysis_config, "day")
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par.generate()
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if __name__ == "__main__":
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fire.Fire(NestedDecisonExecutionWorkflow)
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