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https://github.com/microsoft/qlib.git
synced 2026-07-09 14:00:55 +08:00
update env & strategy, add workflow
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bxdd
@@ -8,95 +8,37 @@ from .exchange import Exchange
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from .report import Report
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from .backtest import backtest as backtest_func, get_date_range
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import copy
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import numpy as np
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import inspect
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from ...utils import init_instance_by_config
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from ...log import get_module_logger
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from ...config import C
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from ..utils import init_instance_by_config
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from ..log import get_module_logger
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from ..config import C
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logger = get_module_logger("backtest caller")
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def get_strategy(
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strategy=None,
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topk=50,
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margin=0.5,
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n_drop=5,
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risk_degree=0.95,
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str_type="dropout",
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adjust_dates=None,
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):
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"""get_strategy
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There will be 3 ways to return a stratgy. Please follow the code.
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Parameters
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----------
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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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sell_limit = margin
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- else:
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sell_limit = pred_in_a_day.count() * margin
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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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Returns
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-------
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:class: Strategy
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an initialized strategy object
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"""
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# There will be 3 ways to return a strategy.
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if strategy is None:
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# 1) create strategy with param `strategy`
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str_cls_dict = {
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"amount": "TopkAmountStrategy",
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"weight": "TopkWeightStrategy",
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"dropout": "TopkDropoutStrategy",
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}
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logger.info("Create new strategy ")
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from .. import strategy as strategy_pool
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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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buffer_margin=margin,
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n_drop=n_drop,
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risk_degree=risk_degree,
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adjust_dates=adjust_dates,
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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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from ..strategy.strategy import BaseStrategy
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# else: nothing happens. 3) Use the strategy directly
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if not isinstance(strategy, BaseStrategy):
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raise TypeError("Strategy not supported")
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return strategy
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def init_env_instance_by_config(env):
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if isinstance(env, dict):
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env_config = copy.copy(env)
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if "kwargs" in env_config:
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env_kwargs = copy.copy(env_config["kwargs"]):
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if "sub_env" in env_kwargs:
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env_kwargs["sub_env"] = init_env_instance_by_config(env_kwargs["sub_env"])
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if "sub_strategy" in env_kwargs:
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env_kwargs["sub_strategy"] = init_instance_by_config(env_kwargs["sub_strategy"])
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env_config["kwargs"] = env_kwargs
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return init_instance_by_config(env_config)
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else:
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return env
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def get_exchange(
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pred,
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exchange=None,
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start_time=None,
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end_time=None,
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codes = "all",
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subscribe_fields=[],
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open_cost=0.0015,
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close_cost=0.0025,
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@@ -104,7 +46,6 @@ def get_exchange(
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trade_unit=None,
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limit_threshold=None,
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deal_price=None,
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extract_codes=False,
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shift=1,
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):
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"""get_exchange
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@@ -128,9 +69,6 @@ def get_exchange(
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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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Returns
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-------
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@@ -149,176 +87,52 @@ def get_exchange(
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# handle exception for deal_price
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if deal_price[0] != "$":
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deal_price = "$" + deal_price
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if extract_codes:
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codes = sorted(pred.index.get_level_values("instrument").unique())
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else:
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codes = "all" # TODO: We must ensure that 'all.txt' includes all the stocks
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dates = sorted(pred.index.get_level_values("datetime").unique())
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dates = np.append(dates, get_date_range(dates[-1], left_shift=1, right_shift=shift))
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exchange = Exchange(
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trade_dates=dates,
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start_time=start_time,
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end_time=end_time,
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codes=codes,
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deal_price=deal_price,
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subscribe_fields=subscribe_fields,
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limit_threshold=limit_threshold,
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open_cost=open_cost,
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close_cost=close_cost,
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min_cost=min_cost,
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trade_unit=trade_unit,
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min_cost=min_cost,
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)
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return exchange
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else:
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return init_instance_by_config(exchange, accept_types=Exchange)
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def backtest(start_time, end_time, strategy, env, account=1e9, benchmark, **kwargs):
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trade_strategy = init_instance_by_config(strategy)
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trade_env = init_env_instance_by_config(env)
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trade_account = Account(init_cash=account)
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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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from ..online.executor import SimulatorExecutor
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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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from ..online.executor import BaseExecutor
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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, 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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- **backtest workflow related or commmon arguments**
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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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return_order : bool
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whether to return order list
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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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sell_limit = margin
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- else:
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sell_limit = pred_in_a_day.count() * margin
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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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"""
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# check strategy:
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spec = inspect.getfullargspec(get_strategy)
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str_args = {k: v for k, v in kwargs.items() if k in spec.args}
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strategy = get_strategy(**str_args)
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# init exchange:
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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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# init executor:
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executor = get_executor(executor=kwargs.get("executor"), trade_exchange=trade_exchange, verbose=verbose)
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temp_env = trade_env
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while True:
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if hasattr(temp_env, "trade_exchange"):
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temp_env.reset(trade_exchange=trade_exchange)
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if hasattr(temp_env, "sub_env"):
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temp_env = temp_env.sub_env
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else:
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break
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trade_env.reset(start_time=start_time, end_time=end_time, trade_account=trade_account)
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trade_strategy.reset(start_time=start_time, end_time=end_time)
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trade_state = self.sub_env.get_first_state()
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while not trade_env.finished():
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_order_list = self.sub_strategy.generate_order(**trade_state)
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trade_state, trade_info = self.sub_env.execute(sub_order_list)
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report_df = trade_account.report.generate_report_dataframe()
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positions = trade_account.get_positions()
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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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report_dict = {"report_df": report_df, "positions": 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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return
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