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synced 2026-07-15 08:46:56 +08:00
Fix backtest (#719)
* modify FileStorage to support multiple freqs * modify backtest's sample documentation * change the logging level of read data exception from error to debug * fix the backtest exception when volume is 0 or np.nan * fix test_storage.py * add backtest_daily * modify backtest_daily's docstring * add __repr__/__str__ to Position * fix the bug of nested_decision_execution example Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
This commit is contained in:
Pengrong Zhu
@@ -3,15 +3,18 @@
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from __future__ import division
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from __future__ import print_function
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from logging import warn
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import numpy as np
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import pandas as pd
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import warnings
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from typing import Union
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from ..log import get_module_logger
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from ..backtest import get_exchange, backtest as backtest_func
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from ..utils import get_date_range
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from ..utils.resam import Freq
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from ..strategy.base import BaseStrategy
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from ..backtest import get_exchange, position, backtest as backtest_func, executor as _executor
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from ..data import D
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from ..config import C
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@@ -117,84 +120,129 @@ def indicator_analysis(df, method="mean"):
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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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"""This function will help you set a reasonable Exchange and provide default value for strategy
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def backtest_daily(
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start_time: Union[str, pd.Timestamp],
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end_time: Union[str, pd.Timestamp],
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strategy: Union[str, dict, BaseStrategy],
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executor: Union[str, dict, _executor.BaseExecutor] = None,
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account: Union[float, int, position.Position] = 1e8,
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benchmark: str = "SH000300",
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exchange_kwargs: dict = None,
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pos_type: str = "Position",
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):
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"""initialize the strategy and executor, then executor the backtest of daily frequency
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Parameters
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----------
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start_time : Union[str, pd.Timestamp]
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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[str, pd.Timestamp]
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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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strategy : Union[str, dict, BaseStrategy]
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for initializing outermost portfolio strategy. Please refer to the docs of init_instance_by_config for more information.
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- **backtest workflow related or commmon arguments**
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E.g.
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pred : pandas.DataFrame
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predict should has <datetime, instrument> index and one `score` column.
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account : float
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init account value.
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shift : int
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whether to shift prediction by one day.
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benchmark : str
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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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.. code-block:: python
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# dict
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strategy = {
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"class": "TopkDropoutStrategy",
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"module_path": "qlib.contrib.strategy.signal_strategy",
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"kwargs": {
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"signal": (model, dataset),
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"topk": 50,
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"n_drop": 5,
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},
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}
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# BaseStrategy
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pred_score = pd.read_pickle("score.pkl")["score"]
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STRATEGY_CONFIG = {
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"topk": 50,
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"n_drop": 5,
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"signal": pred_score,
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}
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strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
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# str example.
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# 1) specify a pickle object
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# - path like 'file:///<path to pickle file>/obj.pkl'
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# 2) specify a class name
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# - "ClassName": getattr(module, "ClassName")() will be used.
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# 3) specify module path with class name
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# - "a.b.c.ClassName" getattr(<a.b.c.module>, "ClassName")() will be used.
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- **strategy related arguments**
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strategy : Strategy()
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strategy used in backtest.
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topk : int (Default value: 50)
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top-N stocks to buy.
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margin : int or float(Default value: 0.5)
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- if isinstance(margin, int):
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executor : Union[str, dict, BaseExecutor]
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for initializing the outermost executor.
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benchmark: str
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the benchmark for reporting.
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account : Union[float, int, Position]
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information for describing how to creating the account
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For `float` or `int`:
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Using Account with only initial cash
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For `Position`:
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Using Account with a Position
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exchange_kwargs : dict
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the kwargs for initializing Exchange
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E.g.
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sell_limit = margin
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.. code-block:: python
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- else:
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exchange_kwargs = {
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"freq": freq,
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"limit_threshold": None, # limit_threshold is None, using C.limit_threshold
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"deal_price": None, # deal_price is None, using C.deal_price
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"open_cost": 0.0005,
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"close_cost": 0.0015,
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"min_cost": 5,
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}
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sell_limit = pred_in_a_day.count() * margin
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pos_type : str
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the type of Position.
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buffer margin, in single score_mode, continue holding stock if it is in nlargest(sell_limit).
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sell_limit should be no less than topk.
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n_drop : int
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number of stocks to be replaced in each trading date.
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risk_degree: float
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0-1, 0.95 for example, use 95% money to trade.
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str_type: 'amount', 'weight' or 'dropout'
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strategy type: TopkAmountStrategy ,TopkWeightStrategy or TopkDropoutStrategy.
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- **exchange related arguments**
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exchange: Exchange()
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pass the exchange for speeding up.
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subscribe_fields: list
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subscribe fields.
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open_cost : float
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open transaction cost. The default value is 0.002(0.2%).
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close_cost : float
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close transaction cost. The default value is 0.002(0.2%).
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min_cost : float
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min transaction cost.
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trade_unit : int
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100 for China A.
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deal_price: str
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dealing price type: 'close', 'open', 'vwap'.
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limit_threshold : float
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limit move 0.1 (10%) for example, long and short with same limit.
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extract_codes: bool
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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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Returns
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-------
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report_normal: pd.DataFrame
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backtest report
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positions_normal: pd.DataFrame
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backtest positions
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"""
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warnings.warn("this function is deprecated, please use backtest function in qlib.backtest", DeprecationWarning)
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report_dict = backtest_func(
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pred=pred, account=account, shift=shift, benchmark=benchmark, verbose=verbose, return_order=False, **kwargs
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freq = "day"
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if executor is None:
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executor_config = {
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"time_per_step": freq,
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"generate_portfolio_metrics": True,
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}
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executor = _executor.SimulatorExecutor(**executor_config)
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_exchange_kwargs = {
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"freq": freq,
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"limit_threshold": None,
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"deal_price": None,
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"open_cost": 0.0005,
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"close_cost": 0.0015,
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"min_cost": 5,
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}
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if exchange_kwargs is not None:
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_exchange_kwargs.update(exchange_kwargs)
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portfolio_metric_dict, indicator_dict = backtest_func(
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start_time=start_time,
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end_time=end_time,
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strategy=strategy,
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executor=executor,
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account=account,
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benchmark=benchmark,
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exchange_kwargs=_exchange_kwargs,
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pos_type=pos_type,
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)
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return report_dict.get("report_df"), report_dict.get("positions")
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analysis_freq = "{0}{1}".format(*Freq.parse(freq))
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report_normal, positions_normal = portfolio_metric_dict.get(analysis_freq)
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return report_normal, positions_normal
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def long_short_backtest(
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@@ -327,7 +375,12 @@ def t_run():
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pred["datetime"] = pd.to_datetime(pred["datetime"])
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pred = pred.set_index([pred.columns[0], pred.columns[1]])
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pred = pred.iloc[:9000]
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report_df, positions = backtest(pred=pred)
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strategy_config = {
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"topk": 50,
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"n_drop": 5,
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"signal": pred,
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}
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report_df, positions = backtest_daily(start_time="2017-01-01", end_time="2020-08-01", strategy=strategy_config)
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print(report_df.head())
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print(positions.keys())
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print(positions[list(positions.keys())[0]])
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@@ -171,20 +171,55 @@ def report_graph(report_df: pd.DataFrame, show_notebook: bool = True) -> [list,
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.. code-block:: python
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from qlib.contrib.evaluate import backtest
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import qlib
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import pandas as pd
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from qlib.utils.time import Freq
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from qlib.utils import flatten_dict
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from qlib.backtest import backtest, executor
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from qlib.contrib.evaluate import risk_analysis
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from qlib.contrib.strategy import TopkDropoutStrategy
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# backtest parameters
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bparas = {}
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bparas['limit_threshold'] = 0.095
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bparas['account'] = 1000000000
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# init qlib
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qlib.init(provider_uri=<qlib data dir>)
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sparas = {}
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sparas['topk'] = 50
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sparas['n_drop'] = 230
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strategy = TopkDropoutStrategy(**sparas)
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CSI300_BENCH = "SH000300"
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FREQ = "day"
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STRATEGY_CONFIG = {
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"topk": 50,
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"n_drop": 5,
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# pred_score, pd.Series
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"signal": pred_score,
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}
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report_normal_df, _ = backtest(pred_df, strategy, **bparas)
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EXECUTOR_CONFIG = {
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"time_per_step": "day",
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"generate_portfolio_metrics": True,
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}
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backtest_config = {
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"start_time": "2017-01-01",
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"end_time": "2020-08-01",
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"account": 100000000,
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"benchmark": CSI300_BENCH,
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"exchange_kwargs": {
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"freq": FREQ,
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"limit_threshold": 0.095,
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"deal_price": "close",
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"open_cost": 0.0005,
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"close_cost": 0.0015,
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"min_cost": 5,
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},
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}
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# strategy object
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strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
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# executor object
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executor_obj = executor.SimulatorExecutor(**EXECUTOR_CONFIG)
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# backtest
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portfolio_metric_dict, indicator_dict = backtest(executor=executor_obj, strategy=strategy_obj, **backtest_config)
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analysis_freq = "{0}{1}".format(*Freq.parse(FREQ))
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# backtest info
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report_normal_df, positions_normal = portfolio_metric_dict.get(analysis_freq)
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qcr.analysis_position.report_graph(report_normal_df)
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@@ -170,32 +170,64 @@ def risk_analysis_graph(
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.. code-block:: python
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from qlib.contrib.evaluate import risk_analysis, backtest, long_short_backtest
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import qlib
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import pandas as pd
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from qlib.utils.time import Freq
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from qlib.utils import flatten_dict
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from qlib.backtest import backtest, executor
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from qlib.contrib.evaluate import risk_analysis
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from qlib.contrib.strategy import TopkDropoutStrategy
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from qlib.contrib.report import analysis_position
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# backtest parameters
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bparas = {}
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bparas['limit_threshold'] = 0.095
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bparas['account'] = 1000000000
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# init qlib
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qlib.init(provider_uri=<qlib data dir>)
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sparas = {}
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sparas['topk'] = 50
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sparas['n_drop'] = 230
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strategy = TopkDropoutStrategy(**sparas)
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CSI300_BENCH = "SH000300"
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FREQ = "day"
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STRATEGY_CONFIG = {
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"topk": 50,
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"n_drop": 5,
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# pred_score, pd.Series
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"signal": pred_score,
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}
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report_normal_df, positions = backtest(pred_df, strategy, **bparas)
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# long_short_map = long_short_backtest(pred_df)
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# report_long_short_df = pd.DataFrame(long_short_map)
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EXECUTOR_CONFIG = {
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"time_per_step": "day",
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"generate_portfolio_metrics": True,
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}
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backtest_config = {
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"start_time": "2017-01-01",
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"end_time": "2020-08-01",
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"account": 100000000,
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"benchmark": CSI300_BENCH,
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"exchange_kwargs": {
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"freq": FREQ,
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"limit_threshold": 0.095,
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"deal_price": "close",
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"open_cost": 0.0005,
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"close_cost": 0.0015,
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"min_cost": 5,
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},
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}
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# strategy object
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strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
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# executor object
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executor_obj = executor.SimulatorExecutor(**EXECUTOR_CONFIG)
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# backtest
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portfolio_metric_dict, indicator_dict = backtest(executor=executor_obj, strategy=strategy_obj, **backtest_config)
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analysis_freq = "{0}{1}".format(*Freq.parse(FREQ))
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# backtest info
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report_normal_df, positions_normal = portfolio_metric_dict.get(analysis_freq)
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analysis = dict()
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# analysis['pred_long'] = risk_analysis(report_long_short_df['long'])
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# analysis['pred_short'] = risk_analysis(report_long_short_df['short'])
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# analysis['pred_long_short'] = risk_analysis(report_long_short_df['long_short'])
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analysis['excess_return_without_cost'] = risk_analysis(report_normal_df['return'] - report_normal_df['bench'])
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analysis['excess_return_with_cost'] = risk_analysis(report_normal_df['return'] - report_normal_df['bench'] - report_normal_df['cost'])
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analysis_df = pd.concat(analysis)
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analysis["excess_return_without_cost"] = risk_analysis(
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report_normal_df["return"] - report_normal_df["bench"], freq=analysis_freq
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)
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analysis["excess_return_with_cost"] = risk_analysis(
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report_normal_df["return"] - report_normal_df["bench"] - report_normal_df["cost"], freq=analysis_freq
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)
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analysis_df = pd.concat(analysis) # type: pd.DataFrame
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analysis_position.risk_analysis_graph(analysis_df, report_normal_df)
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