Refine backtest codes

qlib/backtest/__init__.py

@@ -2,24 +2,29 @@
 # Licensed under the MIT License.
 
 from __future__ import annotations
+
 import copy
-from typing import List, Tuple, Union, TYPE_CHECKING
+from pathlib import Path
+from typing import TYPE_CHECKING, Generator, List, Optional, Tuple, Union
+
+import pandas as pd
 
 from .account import Account
+from .report import Indicator, PortfolioMetrics
 
 if TYPE_CHECKING:
     from ..strategy.base import BaseStrategy
     from .executor import BaseExecutor
     from .decision import BaseTradeDecision
-from .position import Position
+
+from ..config import C
+from ..log import get_module_logger
+from ..utils import init_instance_by_config
+from .backtest import backtest_loop, collect_data_loop
+from .decision import Order
 from .exchange import Exchange
-from .backtest import backtest_loop
-from .backtest import collect_data_loop
+from .position import Position
 from .utils import CommonInfrastructure
-from .decision import Order
-from ..utils import init_instance_by_config
-from ..log import get_module_logger
-from ..config import C
 
 # make import more user-friendly by adding `from qlib.backtest import STH`
 
@@ -28,26 +33,34 @@
 
 
 def get_exchange(
-    exchange=None,
-    freq="day",
-    start_time=None,
-    end_time=None,
-    codes="all",
-    subscribe_fields=[],
-    open_cost=0.0015,
-    close_cost=0.0025,
-    min_cost=5.0,
-    limit_threshold=None,
+    exchange: Union[str, dict, object, Path] = None,
+    freq: str = "day",
+    start_time: Union[pd.Timestamp, str] = None,
+    end_time: Union[pd.Timestamp, str] = None,
+    codes: Union[list, str] = "all",
+    subscribe_fields: list = [],
+    open_cost: float = 0.0015,
+    close_cost: float = 0.0025,
+    min_cost: float = 5.0,
+    limit_threshold: Union[Tuple[str, str], float, None] = None,
     deal_price: Union[str, Tuple[str], List[str]] = None,
     **kwargs,
-):
+) -> Exchange:
     """get_exchange
 
     Parameters
     ----------
 
     # exchange related arguments
-    exchange: Exchange().
+    exchange: Exchange(). It could be None or any types that are acceptable by `init_instance_by_config`.
+    freq: str
+        frequency of data.
+    start_time: Union[pd.Timestamp, str]
+        closed start time for backtest.
+    end_time: Union[pd.Timestamp, str]
+        closed end time for backtest.
+    codes: list|str
+        list stock_id list or a string of instruments (i.e. all, csi500, sse50)
     subscribe_fields: list
         subscribe fields.
     open_cost : float
@@ -57,8 +70,6 @@ def get_exchange(
     min_cost : float
         min transaction cost.  It is an absolute amount of cost instead of a ratio of your order's deal amount.
         e.g. You must pay at least 5 yuan of commission regardless of your order's deal amount.
-    trade_unit : int
-        Included in kwargs.  Please refer to the docs of `__init__` of `Exchange`
     deal_price: Union[str, Tuple[str], List[str]]
                 The `deal_price` supports following two types of input
                 - <deal_price> : str
@@ -101,10 +112,14 @@ def get_exchange(
 
 
 def create_account_instance(
-    start_time, end_time, benchmark: str, account: Union[float, int, dict], pos_type: str = "Position"
+    start_time: Union[pd.Timestamp, str],
+    end_time: Union[pd.Timestamp, str],
+    benchmark: str,
+    account: Union[float, int, dict],
+    pos_type: str = "Position",
 ) -> Account:
     """
-    # TODO: is very strange pass benchmark_config in the account(maybe for report)
+    # TODO: is very strange pass benchmark_config in the account (maybe for report)
     # There should be a post-step to process the report.
 
     Parameters
@@ -132,6 +147,8 @@ def create_account_instance(
             key "cash" means initial cash.
             key "stock1" means the information of first stock with amount and price(optional).
             ...
+    pos_type: str
+        Postion type.
     """
     if isinstance(account, (int, float)):
         pos_kwargs = {"init_cash": account}
@@ -159,15 +176,15 @@ def create_account_instance(
 
 
 def get_strategy_executor(
-    start_time,
-    end_time,
-    strategy: BaseStrategy,
-    executor: BaseExecutor,
+    start_time: Union[pd.Timestamp, str],
+    end_time: Union[pd.Timestamp, str],
+    strategy: Union[str, dict, object, Path],
+    executor: Union[str, dict, object, Path],
     benchmark: str = "SH000300",
     account: Union[float, int, Position] = 1e9,
     exchange_kwargs: dict = {},
     pos_type: str = "Position",
-):
+) -> Tuple[BaseStrategy, BaseExecutor]:
 
     # NOTE:
     # - for avoiding recursive import
@@ -176,7 +193,11 @@ def get_strategy_executor(
     from .executor import BaseExecutor  # pylint: disable=C0415
 
     trade_account = create_account_instance(
-        start_time=start_time, end_time=end_time, benchmark=benchmark, account=account, pos_type=pos_type
+        start_time=start_time,
+        end_time=end_time,
+        benchmark=benchmark,
+        account=account,
+        pos_type=pos_type,
     )
 
     exchange_kwargs = copy.copy(exchange_kwargs)
@@ -196,29 +217,31 @@ def get_strategy_executor(
 
 
 def backtest(
-    start_time,
-    end_time,
-    strategy,
-    executor,
-    benchmark="SH000300",
-    account=1e9,
-    exchange_kwargs={},
+    start_time: Union[pd.Timestamp, str],
+    end_time: Union[pd.Timestamp, str],
+    strategy_config: Union[str, dict, object, Path],
+    executor_config: Union[str, dict, object, Path],
+    benchmark: str = "SH000300",
+    account: Union[float, int, Position] = 1e9,
+    exchange_kwargs: dict = {},
     pos_type: str = "Position",
-):
-    """initialize the strategy and executor, then backtest function for the interaction of the outermost strategy and executor in the nested decision execution
+) -> Tuple[PortfolioMetrics, Indicator]:
+    """initialize the strategy and executor, then backtest function for the interaction of the outermost strategy and
+    executor in the nested decision execution
 
     Parameters
     ----------
-    start_time : pd.Timestamp|str
+    start_time : Union[pd.Timestamp, str]
         closed start time for backtest
         **NOTE**: This will be applied to the outmost executor's calendar.
-    end_time : pd.Timestamp|str
+    end_time : Union[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
-    strategy : Union[str, dict, BaseStrategy]
-        for initializing outermost portfolio strategy. Please refer to the docs of init_instance_by_config for more information.
-    executor : Union[str, dict, BaseExecutor]
+    strategy_config : Union[str, dict, object, Path]
+        for initializing outermost portfolio strategy. Please refer to the docs of init_instance_by_config for more
+        information.
+    executor_config : Union[str, dict, object, Path]
         for initializing the outermost executor.
     benchmark: str
         the benchmark for reporting.
@@ -245,8 +268,8 @@ def backtest(
     trade_strategy, trade_executor = get_strategy_executor(
         start_time,
         end_time,
-        strategy,
-        executor,
+        strategy_config,
+        executor_config,
         benchmark,
         account,
         exchange_kwargs,
@@ -257,16 +280,16 @@ def backtest(
 
 
 def collect_data(
-    start_time,
-    end_time,
-    strategy,
-    executor,
-    benchmark="SH000300",
-    account=1e9,
-    exchange_kwargs={},
+    start_time: Union[pd.Timestamp, str],
+    end_time: Union[pd.Timestamp, str],
+    strategy: Union[str, dict, object, Path],
+    executor: Union[str, dict, object, Path],
+    benchmark: str = "SH000300",
+    account: Union[float, int, Position] = 1e9,
+    exchange_kwargs: dict = {},
     pos_type: str = "Position",
     return_value: dict = None,
-):
+) -> Generator[object, None, None]:
     """initialize the strategy and executor, then collect the trade decision data for rl training
 
     please refer to the docs of the backtest for the explanation of the parameters
@@ -291,7 +314,7 @@ def collect_data(
 
 def format_decisions(
     decisions: List[BaseTradeDecision],
-) -> Tuple[str, List[Tuple[BaseTradeDecision, Union[Tuple, None]]]]:
+) -> Optional[Tuple[str, List[Tuple[BaseTradeDecision, Union[Tuple, None]]]]]:
     """
     format the decisions collected by `qlib.backtest.collect_data`
     The decisions will be organized into a tree-like structure.
@@ -326,4 +349,4 @@ def format_decisions(
     return res
 
 
-__all__ = ["Order"]
+__all__ = ["Order", "backtest"]