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https://github.com/microsoft/qlib.git
synced 2026-07-14 16:26:55 +08:00
update backtest
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bxdd
@@ -5,7 +5,6 @@
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import numpy as np
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import pandas as pd
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from ...utils import get_date_by_shift, get_date_range
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from ..online.executor import SimulatorExecutor
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from ...data import D
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from .account import Account
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from ...config import C
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@@ -15,7 +14,7 @@ from ...data.dataset.utils import get_level_index
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LOG = get_module_logger("backtest")
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def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark, return_order):
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def backtest(pred, strategy, executor, trade_exchange, shift, verbose, account, benchmark, return_order):
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"""Parameters
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----------
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pred : pandas.DataFrame
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@@ -70,8 +69,8 @@ def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark,
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bench = _temp_result.groupby(level="datetime")[_temp_result.columns.tolist()[0]].mean()
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trade_dates = np.append(predict_dates[shift:], get_date_range(predict_dates[-1], left_shift=1, right_shift=shift))
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executor = SimulatorExecutor(trade_exchange, verbose=verbose)
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order_set = []
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if return_order:
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multi_order_list = []
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# trading apart
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for pred_date, trade_date in zip(predict_dates, trade_dates):
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# for loop predict date and trading date
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@@ -103,8 +102,8 @@ def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark,
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)
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else:
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order_list = []
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order_set.append((trade_account, order_list, trade_date))
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if return_order:
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multi_order_list.append((trade_account, order_list, trade_date))
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# 4. Get result after executing order list
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# NOTE: The following operation will modify order.amount.
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# NOTE: If it is buy and the cash is insufficient, the tradable amount will be recalculated
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@@ -113,53 +112,16 @@ def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark,
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# 5. Update account information according to transaction
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update_account(trade_account, trade_info, trade_exchange, trade_date)
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if return_order:
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return order_set
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else:
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# generate backtest report
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report_df = trade_account.report.generate_report_dataframe()
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report_df["bench"] = bench
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positions = trade_account.get_positions()
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return report_df, positions
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def backtest_highfreq(pred, executor, trade_exchange, shift, order_set, verbose, account, benchmark):
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trade_account_highfreq = Account(init_cash=account)
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_pred_dates = pred.index.get_level_values(level="datetime")
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predict_dates = D.calendar(start_time=_pred_dates.min(), end_time=_pred_dates.max())
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if isinstance(benchmark, pd.Series):
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bench = benchmark
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else:
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_codes = benchmark if isinstance(benchmark, list) else [benchmark]
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_temp_result = D.features(
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_codes,
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["$close/Ref($close,1)-1"],
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predict_dates[0],
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get_date_by_shift(predict_dates[-1], shift=shift),
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disk_cache=1,
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)
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if len(_temp_result) == 0:
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raise ValueError(f"The benchmark {_codes} does not exist. Please provide the right benchmark")
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bench = _temp_result.groupby(level="datetime")[_temp_result.columns.tolist()[0]].mean()
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for trade_account, order_list, trade_date in order_set:
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if verbose:
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LOG.info("[I {:%Y-%m-%d}]: highfreq trade begin.".format(trade_date))
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## TODO: kanren group need to merge code here
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print(trade_account, order_list, trade_date)
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executor.execute(trade_account, order_list, trade_date)
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for trade_account, order_list, trade_date in order_set:
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trade_info = executor.get_res()
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print(trade_info)
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update_account(trade_account_highfreq, trade_info, trade_exchange, trade_date)
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if verbose:
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LOG.info("[I {:%Y-%m-%d}]: highfreq trade end.".format(trade_date))
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executor.close()
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report_df = trade_account_highfreq.report.generate_report_dataframe()
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# generate backtest report
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report_df = trade_account.report.generate_report_dataframe()
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report_df["bench"] = bench
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positions = trade_account_highfreq.get_positions()
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return report_df, positions
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positions = trade_account.get_positions()
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report_dict = {"report_df": report_df, "positions": positions}
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if return_order:
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report_dict.update({"order_list": multi_order_list})
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return report_dict
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def update_account(trade_account, trade_info, trade_exchange, trade_date):
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"""Update the account and strategy
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@@ -11,7 +11,8 @@ from ..log import get_module_logger
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from . import strategy as strategy_pool
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from .strategy.strategy import BaseStrategy
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from .backtest.exchange import Exchange
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from .backtest.backtest import backtest as backtest_func, get_date_range, backtest_highfreq as backtest_highfreq_func
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from .backtest.backtest import backtest as backtest_func, get_date_range
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from .online.executor import BaseExecutor, SimulatorExecutor
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from ..data import D
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from ..config import C
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@@ -100,7 +101,7 @@ def get_strategy(
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"weight": "TopkWeightStrategy",
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"dropout": "TopkDropoutStrategy",
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}
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logger.info("Create new streategy ")
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logger.info("Create new strategy ")
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str_cls = getattr(strategy_pool, str_cls_dict.get(str_type))
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strategy = str_cls(
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topk=topk,
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@@ -111,6 +112,7 @@ def get_strategy(
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)
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elif isinstance(strategy, (dict, str)):
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# 2) create strategy with init_instance_by_config
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logger.info("Create new strategy ")
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strategy = init_instance_by_config(strategy)
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# else: nothing happens. 3) Use the strategy directly
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@@ -196,8 +198,48 @@ def get_exchange(
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return exchange
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def get_executor(
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executor=None,
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trade_exchange=None,
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verbose=True,
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):
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"""get_executor
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There will be 3 ways to return a executor. Please follow the code.
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Parameters
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----------
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executor : BaseExecutor
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executor used in backtest.
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trade_exchange : Exchange
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exchange used in executor
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verbose : bool
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whether to print log.
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Returns
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-------
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:class: BaseExecutor
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an initialized BaseExecutor object
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"""
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# There will be 3 ways to return a executor.
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if executor is None:
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# 1) create executor with param `executor`
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logger.info("Create new executor ")
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executor = SimulatorExecutor(trade_exchange=trade_exchange, verbose=verbose)
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elif isinstance(executor, (dict, str)):
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# 2) create executor with config
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logger.info("Create new executor ")
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executor = init_instance_by_config(executor)
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# 3) Use the executor directly
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if not isinstance(executor, BaseExecutor):
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raise TypeError("Executor not supported")
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return executor
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# This is the API for compatibility for legacy code
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def backtest(pred, account=1e9, shift=1, benchmark="SH000905", verbose=True, **kwargs):
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def backtest(pred, account=1e9, shift=1, benchmark="SH000905", verbose=True, return_order=False, **kwargs):
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"""This function will help you set a reasonable Exchange and provide default value for strategy
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Parameters
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----------
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@@ -214,6 +256,8 @@ def backtest(pred, account=1e9, shift=1, benchmark="SH000905", verbose=True, **k
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benchmark code, default is SH000905 CSI 500.
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verbose : bool
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whether to print log.
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return_order : bool
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whther to return order list
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- **strategy related arguments**
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@@ -261,6 +305,14 @@ def backtest(pred, account=1e9, shift=1, benchmark="SH000905", verbose=True, **k
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will we pass the codes extracted from the pred to the exchange.
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.. note:: This will be faster with offline qlib.
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- **executor related arguments**
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executor : BaseExecutor()
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executor used in backtest.
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verbose : bool
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whether to print log.
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"""
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# check strategy:
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spec = inspect.getfullargspec(get_strategy)
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@@ -271,45 +323,27 @@ def backtest(pred, account=1e9, shift=1, benchmark="SH000905", verbose=True, **k
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spec = inspect.getfullargspec(get_exchange)
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ex_args = {k: v for k, v in kwargs.items() if k in spec.args}
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trade_exchange = get_exchange(pred, **ex_args)
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if kwargs.get('highfreq_executor', False):
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order_set = backtest_func(
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pred=pred,
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strategy=strategy,
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trade_exchange=trade_exchange,
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shift=shift,
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verbose=verbose,
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account=account,
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benchmark=benchmark,
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return_order=True,
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)
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executor = init_instance_by_config(kwargs.get('highfreq_executor'))
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report_df, positions = backtest_highfreq_func(
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pred=pred,
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executor=executor,
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trade_exchange=trade_exchange,
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shift=shift,
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order_set=order_set,
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verbose=verbose,
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account=account,
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benchmark=benchmark
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)
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positions = {k: p.position for k, p in positions.items()}
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return report_df, positions
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else:
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# run backtest
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report_df, positions = backtest_func(
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pred=pred,
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strategy=strategy,
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trade_exchange=trade_exchange,
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shift=shift,
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verbose=verbose,
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account=account,
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benchmark=benchmark,
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return_order=False,
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)
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# for compatibility of the old API. return the dict positions
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positions = {k: p.position for k, p in positions.items()}
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return report_df, positions
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# init executor:
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executor = get_executor(executor=kwargs.get("executor"), trade_exchange=trade_exchange, verbose=verbose)
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# run backtest
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report_dict = backtest_func(
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pred=pred,
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strategy=strategy,
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executor=executor,
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trade_exchange=trade_exchange,
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shift=shift,
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verbose=verbose,
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account=account,
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benchmark=benchmark,
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return_order=return_order,
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)
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# for compatibility of the old API. return the dict positions
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positions = report_dict.get("positions")
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report_dict.update({"positions": {k: p.position for k, p in positions.items()}})
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return report_dict
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def long_short_backtest(
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@@ -241,9 +241,14 @@ class PortAnaRecord(SignalRecord):
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# custom strategy and get backtest
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pred_score = super().load()
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report_normal, positions_normal = normal_backtest(pred_score, strategy=self.strategy, **self.backtest_config)
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report_dict = normal_backtest(pred_score, strategy=self.strategy, **self.backtest_config)
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report_normal = report_dict.get("report_df")
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positions_normal = report_dict.get("positions")
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self.recorder.save_objects(**{"report_normal.pkl": report_normal}, artifact_path=PortAnaRecord.get_path())
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self.recorder.save_objects(**{"positions_normal.pkl": positions_normal}, artifact_path=PortAnaRecord.get_path())
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order_normal = report_dict.get("order_list")
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if order_normal:
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self.recorder.save_objects(**{"order_normal.pkl": order_normal}, artifact_path=PortAnaRecord.get_path())
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# analysis
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analysis = dict()
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