qlib/qlib/backtest/backtest.py

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

from __future__ import annotations
from qlib.backtest.decision import BaseTradeDecision
from typing import TYPE_CHECKING

if TYPE_CHECKING:
    from qlib.strategy.base import BaseStrategy
    from qlib.backtest.executor import BaseExecutor
from ..utils.time import Freq
from tqdm.auto import tqdm


def backtest_loop(start_time, end_time, trade_strategy: BaseStrategy, trade_executor: BaseExecutor):
    """backtest funciton for the interaction of the outermost strategy and executor in the nested decision execution

    please refer to the docs of `collect_data_loop`

    Returns
    -------
    portfolio_metrics: PortfolioMetrics
        it records the trading portfolio_metrics information
    indicator: Indicator
        it computes the trading indicator
    """
    return_value = {}
    for _decision in collect_data_loop(start_time, end_time, trade_strategy, trade_executor, return_value):
        pass
    return return_value.get("portfolio_metrics"), return_value.get("indicator")


def collect_data_loop(
    start_time, end_time, trade_strategy: BaseStrategy, trade_executor: BaseExecutor, return_value: dict = None
):
    """Generator for collecting the trade decision data for rl training

    Parameters
    ----------
    start_time : pd.Timestamp|str
        closed start time for backtest
        **NOTE**: This will be applied to the outmost executor's calendar.
    end_time : pd.Timestamp|str
        closed end time for backtest
        **NOTE**: This will be applied to the outmost executor's calendar.
        E.g. Executor[day](Executor[1min]),   setting `end_time == 20XX0301` will include all the minutes on 20XX0301
    trade_strategy : BaseStrategy
        the outermost portfolio strategy
    trade_executor : BaseExecutor
        the outermost executor
    return_value : dict
        used for backtest_loop

    Yields
    -------
    object
        trade decision
    """
    trade_executor.reset(start_time=start_time, end_time=end_time)
    trade_strategy.reset(level_infra=trade_executor.get_level_infra())

    with tqdm(total=trade_executor.trade_calendar.get_trade_len(), desc="backtest loop") as bar:
        _execute_result = None
        while not trade_executor.finished():
            _trade_decision: BaseTradeDecision = trade_strategy.generate_trade_decision(_execute_result)
            _execute_result = yield from trade_executor.collect_data(_trade_decision, level=0)
            bar.update(1)

    if return_value is not None:
        all_executors = trade_executor.get_all_executors()
        all_portfolio_metrics = {
            "{}{}".format(*Freq.parse(_executor.time_per_step)): _executor.trade_account.get_portfolio_metrics()
            for _executor in all_executors
            if _executor.trade_account.is_port_metr_enabled()
        }
        all_indicators = {}
        for _executor in all_executors:
            key = "{}{}".format(*Freq.parse(_executor.time_per_step))
            all_indicators[key] = _executor.trade_account.get_trade_indicator().generate_trade_indicators_dataframe()
            all_indicators[key + "_obj"] = _executor.trade_account.get_trade_indicator()
        return_value.update({"portfolio_metrics": all_portfolio_metrics, "indicator": all_indicators})