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qlib/qlib/backtest/backtest.py

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3.0 KiB
Python

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from qlib.backtest.order import BaseTradeDecision
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
-------
report: Report
it records the trading report information
"""
return_value = {}
for _decision in collect_data_loop(start_time, end_time, trade_strategy, trade_executor, return_value):
pass
return return_value.get("report"), 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)
level_infra = trade_executor.get_level_infra()
trade_strategy.reset(level_infra=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)
bar.update(1)
if return_value is not None:
all_executors = trade_executor.get_all_executors()
all_reports = {
"{}{}".format(*Freq.parse(_executor.time_per_step)): _executor.get_report()
for _executor in all_executors
if _executor.generate_report
}
all_indicators = {}
for _executor in all_executors:
key = "{}{}".format(*Freq.parse(_executor.time_per_step))
all_indicators[key] = _executor.get_trade_indicator().generate_trade_indicators_dataframe()
all_indicators[key + "_obj"] = _executor.get_trade_indicator()
return_value.update({"report": all_reports, "indicator": all_indicators})