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1d65d28b28
* Use dict-like configuration * Rename from_neutrader to integration * SAOE strategy * Optimize file structure * Optimize code * Format code * create_state_maintainer_recursive * Remove explicit time_per_step * CI test passed * Resolve PR comments * Pass all CI * Minor test issue * Refine SAOE adapter logic * Minor bugfix * Cherry pick updates * Resolve PR comments * CI issues * Refine adapter & saoe_data logic * Resolve PR comments * Resolve PR comments * Rename ONE_SEC to EPS_T; complete backtest loop * CI issue * Resolve Yuge's PR comments
103 lines
3.8 KiB
Python
103 lines
3.8 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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from __future__ import annotations
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from typing import TYPE_CHECKING, Generator, Optional, Tuple, Union, cast
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import pandas as pd
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from qlib.backtest.decision import BaseTradeDecision
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from qlib.backtest.report import Indicator, PortfolioMetrics
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if TYPE_CHECKING:
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from qlib.strategy.base import BaseStrategy
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from qlib.backtest.executor import BaseExecutor
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from tqdm.auto import tqdm
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from ..utils.time import Freq
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def backtest_loop(
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start_time: Union[pd.Timestamp, str],
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end_time: Union[pd.Timestamp, str],
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trade_strategy: BaseStrategy,
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trade_executor: BaseExecutor,
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) -> Tuple[PortfolioMetrics, Indicator]:
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"""backtest function for the interaction of the outermost strategy and executor in the nested decision execution
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please refer to the docs of `collect_data_loop`
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Returns
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-------
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portfolio_metrics: PortfolioMetrics
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it records the trading portfolio_metrics information
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indicator: Indicator
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it computes the trading indicator
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"""
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return_value: dict = {}
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for _decision in collect_data_loop(start_time, end_time, trade_strategy, trade_executor, return_value):
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pass
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portfolio_metrics = cast(PortfolioMetrics, return_value.get("portfolio_metrics"))
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indicator = cast(Indicator, return_value.get("indicator"))
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return portfolio_metrics, indicator
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def collect_data_loop(
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start_time: Union[pd.Timestamp, str],
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end_time: Union[pd.Timestamp, str],
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trade_strategy: BaseStrategy,
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trade_executor: BaseExecutor,
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return_value: dict = None,
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) -> Generator[BaseTradeDecision, Optional[BaseTradeDecision], None]:
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"""Generator for collecting the trade decision data for rl training
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Parameters
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----------
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start_time : Union[pd.Timestamp, str]
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closed start time for backtest
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**NOTE**: This will be applied to the outmost executor's calendar.
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end_time : Union[pd.Timestamp, str]
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closed end time for backtest
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**NOTE**: This will be applied to the outmost executor's calendar.
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E.g. Executor[day](Executor[1min]), setting `end_time == 20XX0301` will include all the minutes on 20XX0301
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trade_strategy : BaseStrategy
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the outermost portfolio strategy
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trade_executor : BaseExecutor
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the outermost executor
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return_value : dict
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used for backtest_loop
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Yields
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-------
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object
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trade decision
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"""
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trade_executor.reset(start_time=start_time, end_time=end_time)
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trade_strategy.reset(level_infra=trade_executor.get_level_infra())
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with tqdm(total=trade_executor.trade_calendar.get_trade_len(), desc="backtest loop") as bar:
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_execute_result = None
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while not trade_executor.finished():
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_trade_decision: BaseTradeDecision = trade_strategy.generate_trade_decision(_execute_result)
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_execute_result = yield from trade_executor.collect_data(_trade_decision, level=0)
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trade_strategy.post_exe_step(_execute_result)
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bar.update(1)
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trade_strategy.post_upper_level_exe_step()
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if return_value is not None:
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all_executors = trade_executor.get_all_executors()
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all_portfolio_metrics = {
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"{}{}".format(*Freq.parse(_executor.time_per_step)): _executor.trade_account.get_portfolio_metrics()
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for _executor in all_executors
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if _executor.trade_account.is_port_metr_enabled()
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}
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all_indicators = {}
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for _executor in all_executors:
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key = "{}{}".format(*Freq.parse(_executor.time_per_step))
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all_indicators[key] = _executor.trade_account.get_trade_indicator().generate_trade_indicators_dataframe()
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all_indicators[key + "_obj"] = _executor.trade_account.get_trade_indicator()
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return_value.update({"portfolio_metrics": all_portfolio_metrics, "indicator": all_indicators})
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