| |
| |
| """ |
| The expect result of `backtest` is following in current version |
| |
| 'The following are analysis results of benchmark return(1day).' |
| risk |
| mean 0.000651 |
| std 0.012472 |
| annualized_return 0.154967 |
| information_ratio 0.805422 |
| max_drawdown -0.160445 |
| 'The following are analysis results of the excess return without cost(1day).' |
| risk |
| mean 0.001258 |
| std 0.007575 |
| annualized_return 0.299303 |
| information_ratio 2.561219 |
| max_drawdown -0.068386 |
| 'The following are analysis results of the excess return with cost(1day).' |
| risk |
| mean 0.001110 |
| std 0.007575 |
| annualized_return 0.264280 |
| information_ratio 2.261392 |
| max_drawdown -0.071842 |
| [1706497:MainThread](2021-12-07 14:08:30,263) INFO - qlib.workflow - [record_temp.py:441] - Portfolio analysis record 'port_analysis_30minute. |
| pkl' has been saved as the artifact of the Experiment 2 |
| 'The following are analysis results of benchmark return(30minute).' |
| risk |
| mean 0.000078 |
| std 0.003646 |
| annualized_return 0.148787 |
| information_ratio 0.935252 |
| max_drawdown -0.142830 |
| ('The following are analysis results of the excess return without ' |
| 'cost(30minute).') |
| risk |
| mean 0.000174 |
| std 0.003343 |
| annualized_return 0.331867 |
| information_ratio 2.275019 |
| max_drawdown -0.074752 |
| 'The following are analysis results of the excess return with cost(30minute).' |
| risk |
| mean 0.000155 |
| std 0.003343 |
| annualized_return 0.294536 |
| information_ratio 2.018860 |
| max_drawdown -0.075579 |
| [1706497:MainThread](2021-12-07 14:08:30,277) INFO - qlib.workflow - [record_temp.py:441] - Portfolio analysis record 'port_analysis_5minute.p |
| kl' has been saved as the artifact of the Experiment 2 |
| 'The following are analysis results of benchmark return(5minute).' |
| risk |
| mean 0.000015 |
| std 0.001460 |
| annualized_return 0.172170 |
| information_ratio 1.103439 |
| max_drawdown -0.144807 |
| 'The following are analysis results of the excess return without cost(5minute).' |
| risk |
| mean 0.000028 |
| std 0.001412 |
| annualized_return 0.319771 |
| information_ratio 2.119563 |
| max_drawdown -0.077426 |
| 'The following are analysis results of the excess return with cost(5minute).' |
| risk |
| mean 0.000025 |
| std 0.001412 |
| annualized_return 0.281536 |
| information_ratio 1.866091 |
| max_drawdown -0.078194 |
| [1706497:MainThread](2021-12-07 14:08:30,287) INFO - qlib.workflow - [record_temp.py:466] - Indicator analysis record 'indicator_analysis_1day |
| .pkl' has been saved as the artifact of the Experiment 2 |
| 'The following are analysis results of indicators(1day).' |
| value |
| ffr 0.945821 |
| pa 0.000324 |
| pos 0.542882 |
| [1706497:MainThread](2021-12-07 14:08:30,293) INFO - qlib.workflow - [record_temp.py:466] - Indicator analysis record 'indicator_analysis_30mi |
| nute.pkl' has been saved as the artifact of the Experiment 2 |
| 'The following are analysis results of indicators(30minute).' |
| value |
| ffr 0.982910 |
| pa 0.000037 |
| pos 0.500806 |
| [1706497:MainThread](2021-12-07 14:08:30,302) INFO - qlib.workflow - [record_temp.py:466] - Indicator analysis record 'indicator_analysis_5min |
| ute.pkl' has been saved as the artifact of the Experiment 2 |
| 'The following are analysis results of indicators(5minute).' |
| value |
| ffr 0.991017 |
| pa 0.000000 |
| pos 0.000000 |
| [1706497:MainThread](2021-12-07 14:08:30,627) INFO - qlib.timer - [log.py:113] - Time cost: 0.014s | waiting `async_log` Done |
| """ |
|
|
| from copy import deepcopy |
| import qlib |
| import fire |
| import pandas as pd |
| from qlib.constant import REG_CN |
| from qlib.config import HIGH_FREQ_CONFIG |
| from qlib.data import D |
| from qlib.utils import exists_qlib_data, init_instance_by_config, flatten_dict |
| from qlib.workflow import R |
| from qlib.workflow.record_temp import SignalRecord, PortAnaRecord |
| from qlib.tests.data import GetData |
| from qlib.backtest import collect_data |
|
|
|
|
| class NestedDecisionExecutionWorkflow: |
| market = "csi300" |
| benchmark = "SH000300" |
| data_handler_config = { |
| "start_time": "2008-01-01", |
| "end_time": "2021-05-31", |
| "fit_start_time": "2008-01-01", |
| "fit_end_time": "2014-12-31", |
| "instruments": market, |
| } |
|
|
| task = { |
| "model": { |
| "class": "LGBModel", |
| "module_path": "qlib.contrib.model.gbdt", |
| "kwargs": { |
| "loss": "mse", |
| "colsample_bytree": 0.8879, |
| "learning_rate": 0.0421, |
| "subsample": 0.8789, |
| "lambda_l1": 205.6999, |
| "lambda_l2": 580.9768, |
| "max_depth": 8, |
| "num_leaves": 210, |
| "num_threads": 20, |
| }, |
| }, |
| "dataset": { |
| "class": "DatasetH", |
| "module_path": "qlib.data.dataset", |
| "kwargs": { |
| "handler": { |
| "class": "Alpha158", |
| "module_path": "qlib.contrib.data.handler", |
| "kwargs": data_handler_config, |
| }, |
| "segments": { |
| "train": ("2007-01-01", "2014-12-31"), |
| "valid": ("2015-01-01", "2016-12-31"), |
| "test": ("2020-01-01", "2021-05-31"), |
| }, |
| }, |
| }, |
| } |
|
|
| exp_name = "nested" |
|
|
| port_analysis_config = { |
| "executor": { |
| "class": "NestedExecutor", |
| "module_path": "qlib.backtest.executor", |
| "kwargs": { |
| "time_per_step": "day", |
| "inner_executor": { |
| "class": "NestedExecutor", |
| "module_path": "qlib.backtest.executor", |
| "kwargs": { |
| "time_per_step": "30min", |
| "inner_executor": { |
| "class": "SimulatorExecutor", |
| "module_path": "qlib.backtest.executor", |
| "kwargs": { |
| "time_per_step": "5min", |
| "generate_portfolio_metrics": True, |
| "verbose": True, |
| "indicator_config": { |
| "show_indicator": True, |
| }, |
| }, |
| }, |
| "inner_strategy": { |
| "class": "TWAPStrategy", |
| "module_path": "qlib.contrib.strategy.rule_strategy", |
| }, |
| "generate_portfolio_metrics": True, |
| "indicator_config": { |
| "show_indicator": True, |
| }, |
| }, |
| }, |
| "inner_strategy": { |
| "class": "SBBStrategyEMA", |
| "module_path": "qlib.contrib.strategy.rule_strategy", |
| "kwargs": { |
| "instruments": market, |
| "freq": "1min", |
| }, |
| }, |
| "track_data": True, |
| "generate_portfolio_metrics": True, |
| "indicator_config": { |
| "show_indicator": True, |
| }, |
| }, |
| }, |
| "backtest": { |
| "start_time": "2020-09-20", |
| "end_time": "2021-05-20", |
| "account": 100000000, |
| "exchange_kwargs": { |
| "freq": "1min", |
| "limit_threshold": 0.095, |
| "deal_price": "close", |
| "open_cost": 0.0005, |
| "close_cost": 0.0015, |
| "min_cost": 5, |
| }, |
| }, |
| } |
|
|
| def _init_qlib(self): |
| """initialize qlib""" |
| provider_uri_day = "~/.qlib/qlib_data/cn_data" |
| GetData().qlib_data(target_dir=provider_uri_day, region=REG_CN, version="v2", exists_skip=True) |
| provider_uri_1min = HIGH_FREQ_CONFIG.get("provider_uri") |
| GetData().qlib_data( |
| target_dir=provider_uri_1min, interval="1min", region=REG_CN, version="v2", exists_skip=True |
| ) |
| provider_uri_map = {"1min": provider_uri_1min, "day": provider_uri_day} |
| qlib.init(provider_uri=provider_uri_map, dataset_cache=None, expression_cache=None) |
|
|
| def _train_model(self, model, dataset): |
| with R.start(experiment_name=self.exp_name): |
| R.log_params(**flatten_dict(self.task)) |
| model.fit(dataset) |
| R.save_objects(**{"params.pkl": model}) |
|
|
| |
| recorder = R.get_recorder() |
| sr = SignalRecord(model, dataset, recorder) |
| sr.generate() |
|
|
| def backtest(self): |
| self._init_qlib() |
| model = init_instance_by_config(self.task["model"]) |
| dataset = init_instance_by_config(self.task["dataset"]) |
| self._train_model(model, dataset) |
| strategy_config = { |
| "class": "TopkDropoutStrategy", |
| "module_path": "qlib.contrib.strategy.signal_strategy", |
| "kwargs": { |
| "signal": (model, dataset), |
| "topk": 50, |
| "n_drop": 5, |
| }, |
| } |
| self.port_analysis_config["strategy"] = strategy_config |
| self.port_analysis_config["backtest"]["benchmark"] = self.benchmark |
|
|
| with R.start(experiment_name=self.exp_name, resume=True): |
| recorder = R.get_recorder() |
| par = PortAnaRecord( |
| recorder, |
| self.port_analysis_config, |
| indicator_analysis_method="value_weighted", |
| ) |
| par.generate() |
|
|
| |
| |
| |
| |
|
|
| def collect_data(self): |
| self._init_qlib() |
| model = init_instance_by_config(self.task["model"]) |
| dataset = init_instance_by_config(self.task["dataset"]) |
| self._train_model(model, dataset) |
| executor_config = self.port_analysis_config["executor"] |
| backtest_config = self.port_analysis_config["backtest"] |
| backtest_config["benchmark"] = self.benchmark |
| strategy_config = { |
| "class": "TopkDropoutStrategy", |
| "module_path": "qlib.contrib.strategy.signal_strategy", |
| "kwargs": { |
| "signal": (model, dataset), |
| "topk": 50, |
| "n_drop": 5, |
| }, |
| } |
| data_generator = collect_data(executor=executor_config, strategy=strategy_config, **backtest_config) |
| for trade_decision in data_generator: |
| print(trade_decision) |
|
|
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| def check_diff_freq(self): |
| self._init_qlib() |
| exp = R.get_exp(experiment_name="backtest") |
| rec = next(iter(exp.list_recorders().values())) |
| for check_key in "account", "total_turnover", "total_cost": |
| check_key = "total_cost" |
|
|
| acc_dict = {} |
| for freq in ["30minute", "5minute", "1day"]: |
| acc_dict[freq] = rec.load_object(f"portfolio_analysis/report_normal_{freq}.pkl")[check_key] |
| acc_df = pd.DataFrame(acc_dict) |
| acc_resam = acc_df.resample("1d").last().dropna() |
| assert (acc_resam["30minute"] == acc_resam["1day"]).all() |
|
|
| def backtest_only_daily(self): |
| """ |
| This backtest is used for comparing the nested execution and single layer execution |
| Due to the low quality daily-level and miniute-level data, they are hardly comparable. |
| So it is used for detecting serious bugs which make the results different greatly. |
| |
| .. code-block:: shell |
| |
| [1724971:MainThread](2021-12-07 16:24:31,156) INFO - qlib.workflow - [record_temp.py:441] - Portfolio analysis record 'port_analysis_1day.pkl' |
| has been saved as the artifact of the Experiment 2 |
| 'The following are analysis results of benchmark return(1day).' |
| risk |
| mean 0.000651 |
| std 0.012472 |
| annualized_return 0.154967 |
| information_ratio 0.805422 |
| max_drawdown -0.160445 |
| 'The following are analysis results of the excess return without cost(1day).' |
| risk |
| mean 0.001375 |
| std 0.006103 |
| annualized_return 0.327204 |
| information_ratio 3.475016 |
| max_drawdown -0.024927 |
| 'The following are analysis results of the excess return with cost(1day).' |
| risk |
| mean 0.001184 |
| std 0.006091 |
| annualized_return 0.281801 |
| information_ratio 2.998749 |
| max_drawdown -0.029568 |
| [1724971:MainThread](2021-12-07 16:24:31,170) INFO - qlib.workflow - [record_temp.py:466] - Indicator analysis record 'indicator_analysis_1day. |
| pkl' has been saved as the artifact of the Experiment 2 |
| 'The following are analysis results of indicators(1day).' |
| value |
| ffr 1.0 |
| pa 0.0 |
| pos 0.0 |
| [1724971:MainThread](2021-12-07 16:24:31,188) INFO - qlib.timer - [log.py:113] - Time cost: 0.007s | waiting `async_log` Done |
| |
| """ |
| self._init_qlib() |
| model = init_instance_by_config(self.task["model"]) |
| dataset = init_instance_by_config(self.task["dataset"]) |
| self._train_model(model, dataset) |
| strategy_config = { |
| "class": "TopkDropoutStrategy", |
| "module_path": "qlib.contrib.strategy.signal_strategy", |
| "kwargs": { |
| "signal": (model, dataset), |
| "topk": 50, |
| "n_drop": 5, |
| }, |
| } |
| pa_conf = deepcopy(self.port_analysis_config) |
| pa_conf["strategy"] = strategy_config |
| pa_conf["executor"] = { |
| "class": "SimulatorExecutor", |
| "module_path": "qlib.backtest.executor", |
| "kwargs": { |
| "time_per_step": "day", |
| "generate_portfolio_metrics": True, |
| "verbose": True, |
| }, |
| } |
| pa_conf["backtest"]["benchmark"] = self.benchmark |
|
|
| with R.start(experiment_name=self.exp_name, resume=True): |
| recorder = R.get_recorder() |
| par = PortAnaRecord(recorder, pa_conf) |
| par.generate() |
|
|
|
|
| if __name__ == "__main__": |
| fire.Fire(NestedDecisionExecutionWorkflow) |
|
|
