liuyu/qlib
1
0
Fork 0
mirror of https://github.com/microsoft/qlib.git synced 2026-07-05 20:11:08 +08:00
Code Activity

Refine backtest codes (#1120)

Browse Source 
* Refine backtest code

* Keep working

* Minor

* Resolve PR comments

* Fix import error

* Fix import error
This commit is contained in:
Huoran Li
2022-06-10 12:14:48 +08:00
committed by GitHub
parent 1ef8e61abd
commit 89972f6c6f
15 changed files with 789 additions and 489 deletions
Download Patch File Download Diff File Expand all files Collapse all files

133
qlib/backtest/__init__.py
View File

@@ -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 .exchange import Exchange
from .backtest import backtest_loop
from .backtest import collect_data_loop
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
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 .position import Position
from .utils import CommonInfrastructure
# make import more user-friendly by adding `from qlib.backtest import STH`
@@ -28,26 +33,34 @@ logger = get_module_logger("backtest caller")
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: 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",
):
"""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 : Union[str, dict, object, Path]
for initializing outermost portfolio strategy. Please refer to the docs of init_instance_by_config for more
information.
executor : Union[str, dict, object, Path]
for initializing the outermost executor.
benchmark: str
the benchmark for reporting.
@@ -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"]

90
qlib/backtest/account.py
View File

@@ -1,15 +1,18 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import annotations
import copy
from typing import Dict, List, Tuple
from qlib.utils import init_instance_by_config
import pandas as pd
from .position import BasePosition
from .report import PortfolioMetrics, Indicator
from qlib.utils import init_instance_by_config
from .decision import BaseTradeDecision, Order
from .exchange import Exchange
from .position import BasePosition
from .report import Indicator, PortfolioMetrics
"""
rtn & earning in the Account
@@ -34,40 +37,42 @@ class AccumulatedInfo:
AccumulatedInfo should be shared across different levels
"""
def __init__(self):
def __init__(self) -> None:
self.reset()
def reset(self):
self.rtn = 0 # accumulated return, do not consider cost
self.cost = 0 # accumulated cost
self.to = 0 # accumulated turnover
def reset(self) -> None:
self.rtn: float = 0.0 # accumulated return, do not consider cost
self.cost: float = 0.0 # accumulated cost
self.to: float = 0.0 # accumulated turnover
def add_return_value(self, value):
def add_return_value(self, value: float) -> None:
self.rtn += value
def add_cost(self, value):
def add_cost(self, value: float) -> None:
self.cost += value
def add_turnover(self, value):
def add_turnover(self, value: float) -> None:
self.to += value
@property
def get_return(self):
def get_return(self) -> float:
return self.rtn
@property
def get_cost(self):
def get_cost(self) -> float:
return self.cost
@property
def get_turnover(self):
def get_turnover(self) -> float:
return self.to
class Account:
"""
The correctness of the metrics of Account in nested execution depends on the shallow copy of `trade_account` in qlib/backtest/executor.py:NestedExecutor
Different level of executor has different Account object when calculating metrics. But the position object is shared cross all the Account object.
The correctness of the metrics of Account in nested execution depends on the shallow copy of `trade_account` in
qlib/backtest/executor.py:NestedExecutor
Different level of executor has different Account object when calculating metrics. But the position object is
shared cross all the Account object.
"""
def __init__(
@@ -78,7 +83,7 @@ class Account:
benchmark_config: dict = {},
pos_type: str = "Position",
port_metr_enabled: bool = True,
):
) -> None:
"""the trade account of backtest.
Parameters
@@ -102,7 +107,7 @@ class Account:
self.benchmark_config = None # avoid no attribute error
self.init_vars(init_cash, position_dict, freq, benchmark_config)
def init_vars(self, init_cash, position_dict, freq: str, benchmark_config: dict):
def init_vars(self, init_cash: float, position_dict: dict, freq: str, benchmark_config: dict) -> None:
# 1) the following variables are shared by multiple layers
# - you will see a shallow copy instead of deepcopy in the NestedExecutor;
self.init_cash = init_cash
@@ -114,7 +119,7 @@ class Account:
"position_dict": position_dict,
},
"module_path": "qlib.backtest.position",
}
},
)
self.accum_info = AccumulatedInfo()
@@ -123,13 +128,13 @@ class Account:
self.hist_positions = {}
self.reset(freq=freq, benchmark_config=benchmark_config)
def is_port_metr_enabled(self):
def is_port_metr_enabled(self) -> bool:
"""
Is portfolio-based metrics enabled.
"""
return self._port_metr_enabled and not self.current_position.skip_update()
def reset_report(self, freq, benchmark_config):
def reset_report(self, freq: str, benchmark_config: dict) -> None:
# portfolio related metrics
if self.is_port_metr_enabled():
# NOTE:
@@ -140,13 +145,13 @@ class Account:
# fill stock value
# The frequency of account may not align with the trading frequency.
# This may result in obscure bugs when data quality is low.
if isinstance(self.benchmark_config, dict) and self.benchmark_config.get("start_time") is not None:
if isinstance(self.benchmark_config, dict) and "start_time" in self.benchmark_config:
self.current_position.fill_stock_value(self.benchmark_config["start_time"], self.freq)
# trading related metrics(e.g. high-frequency trading)
self.indicator = Indicator()
def reset(self, freq=None, benchmark_config=None, port_metr_enabled: bool = None):
def reset(self, freq: str = None, benchmark_config: dict = None, port_metr_enabled: bool = None) -> None:
"""reset freq and report of account
Parameters
@@ -155,6 +160,7 @@ class Account:
frequency of account & report, by default None
benchmark_config : {}, optional
benchmark config of report, by default None
port_metr_enabled: bool
"""
if freq is not None:
self.freq = freq
@@ -165,13 +171,13 @@ class Account:
self.reset_report(self.freq, self.benchmark_config)
def get_hist_positions(self):
def get_hist_positions(self) -> dict:
return self.hist_positions
def get_cash(self):
def get_cash(self) -> float:
return self.current_position.get_cash()
def _update_state_from_order(self, order, trade_val, cost, trade_price):
def _update_state_from_order(self, order: Order, trade_val: float, cost: float, trade_price: float) -> None:
if self.is_port_metr_enabled():
# update turnover
self.accum_info.add_turnover(trade_val)
@@ -191,13 +197,14 @@ class Account:
profit = self.current_position.get_stock_price(order.stock_id) * trade_amount - trade_val
self.accum_info.add_return_value(profit) # note here do not consider cost
def update_order(self, order, trade_val, cost, trade_price):
def update_order(self, order: Order, trade_val: float, cost: float, trade_price: float) -> None:
if self.current_position.skip_update():
# TODO: supporting polymorphism for account
# updating order for infinite position is meaningless
return
# if stock is sold out, no stock price information in Position, then we should update account first, then update current position
# if stock is sold out, no stock price information in Position, then we should update account first,
# then update current position
# if stock is bought, there is no stock in current position, update current, then update account
# The cost will be subtracted from the cash at last. So the trading logic can ignore the cost calculation
if order.direction == Order.SELL:
@@ -212,8 +219,15 @@ class Account:
self.current_position.update_order(order, trade_val, cost, trade_price)
self._update_state_from_order(order, trade_val, cost, trade_price)
def update_current_position(self, trade_start_time, trade_end_time, trade_exchange):
"""update current to make rtn consistent with earning at the end of bar, and update holding bar count of stock"""
def update_current_position(
self,
trade_start_time: pd.Timestamp,
trade_end_time: pd.Timestamp,
trade_exchange: Exchange,
) -> None:
"""
Update current to make rtn consistent with earning at the end of bar, and update holding bar count of stock
"""
# update price for stock in the position and the profit from changed_price
# NOTE: updating position does not only serve portfolio metrics, it also serve the strategy
if not self.current_position.skip_update():
@@ -228,7 +242,7 @@ class Account:
# NOTE: updating bar_count does not only serve portfolio metrics, it also serve the strategy
self.current_position.add_count_all(bar=self.freq)
def update_portfolio_metrics(self, trade_start_time, trade_end_time):
def update_portfolio_metrics(self, trade_start_time: pd.Timestamp, trade_end_time: pd.Timestamp) -> None:
"""update portfolio_metrics"""
# calculate earning
# account_value - last_account_value
@@ -243,14 +257,16 @@ class Account:
last_account_value = self.portfolio_metrics.get_latest_account_value()
last_total_cost = self.portfolio_metrics.get_latest_total_cost()
last_total_turnover = self.portfolio_metrics.get_latest_total_turnover()
# get now_account_value, now_stock_value, now_earning, now_cost, now_turnover
now_account_value = self.current_position.calculate_value()
now_stock_value = self.current_position.calculate_stock_value()
now_earning = now_account_value - last_account_value
now_cost = self.accum_info.get_cost - last_total_cost
now_turnover = self.accum_info.get_turnover - last_total_turnover
# update portfolio_metrics for today
# judge whether the the trading is begin.
# judge whether the trading is begin.
# and don't add init account state into portfolio_metrics, due to we don't have excess return in those days.
self.portfolio_metrics.update_portfolio_metrics_record(
trade_start_time=trade_start_time,
@@ -267,7 +283,7 @@ class Account:
stock_value=now_stock_value,
)
def update_hist_positions(self, trade_start_time):
def update_hist_positions(self, trade_start_time: pd.Timestamp) -> None:
"""update history position"""
now_account_value = self.current_position.calculate_value()
# set now_account_value to position
@@ -287,7 +303,7 @@ class Account:
inner_order_indicators: List[Dict[str, pd.Series]] = None,
decision_list: List[Tuple[BaseTradeDecision, pd.Timestamp, pd.Timestamp]] = None,
indicator_config: dict = {},
):
) -> None:
"""update trade indicators and order indicators in each bar end"""
# TODO: will skip empty decisions make it faster? `outer_trade_decision.empty():`
@@ -323,7 +339,7 @@ class Account:
inner_order_indicators: List[Dict[str, pd.Series]] = None,
decision_list: List[Tuple[BaseTradeDecision, pd.Timestamp, pd.Timestamp]] = None,
indicator_config: dict = {},
):
) -> None:
"""update account at each trading bar step
Parameters
@@ -338,6 +354,8 @@ class Account:
whether the trading executor is atomic, which means there is no higher-frequency trading executor inside it
- if atomic is True, calculate the indicators with trade_info
- else, aggregate indicators with inner indicators
outer_trade_decision: BaseTradeDecision
external trade decision
trade_info : List[(Order, float, float, float)], optional
trading information, by default None
- necessary if atomic is True
@@ -377,7 +395,7 @@ class Account:
indicator_config=indicator_config,
)
def get_portfolio_metrics(self):
def get_portfolio_metrics(self) -> Tuple[pd.DataFrame, dict]:
"""get the history portfolio_metrics and positions instance"""
if self.is_port_metr_enabled():
_portfolio_metrics = self.portfolio_metrics.generate_portfolio_metrics_dataframe()

32
qlib/backtest/backtest.py
View File

@@ -2,17 +2,29 @@
# Licensed under the MIT License.
from __future__ import annotations
from typing import TYPE_CHECKING, Generator, Optional, Tuple, Union
import pandas as pd
from qlib.backtest.decision import BaseTradeDecision
from typing import TYPE_CHECKING
from qlib.backtest.report import Indicator, PortfolioMetrics
if TYPE_CHECKING:
from qlib.strategy.base import BaseStrategy
from qlib.backtest.executor import BaseExecutor
from ..utils.time import Freq
from tqdm.auto import tqdm
from ..utils.time import Freq
def backtest_loop(start_time, end_time, trade_strategy: BaseStrategy, trade_executor: BaseExecutor):
def backtest_loop(
start_time: Union[pd.Timestamp, str],
end_time: Union[pd.Timestamp, str],
trade_strategy: BaseStrategy,
trade_executor: BaseExecutor,
) -> Tuple[PortfolioMetrics, Indicator]:
"""backtest function for the interaction of the outermost strategy and executor in the nested decision execution
please refer to the docs of `collect_data_loop`
@@ -31,19 +43,23 @@ def backtest_loop(start_time, end_time, trade_strategy: BaseStrategy, trade_exec
def collect_data_loop(
start_time, end_time, trade_strategy: BaseStrategy, trade_executor: BaseExecutor, return_value: dict = None
):
start_time: Union[pd.Timestamp, str],
end_time: Union[pd.Timestamp, str],
trade_strategy: BaseStrategy,
trade_executor: BaseExecutor,
return_value: dict = None,
) -> Generator[BaseTradeDecision, Optional[BaseTradeDecision], None]:
"""Generator for collecting the trade decision data for rl training
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
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

109
qlib/backtest/decision.py
View File

@@ -2,23 +2,26 @@
# Licensed under the MIT License.
from __future__ import annotations
from enum import IntEnum
from qlib.data.data import Cal
from qlib.utils.time import concat_date_time, epsilon_change
from qlib.log import get_module_logger
from typing import ClassVar, Optional, Union, List, Tuple
from abc import abstractmethod
from enum import IntEnum
# try to fix circular imports when enabling type hints
from typing import TYPE_CHECKING
from typing import TYPE_CHECKING, ClassVar, List, Optional, Tuple, Union
from qlib.backtest.utils import TradeCalendarManager
from qlib.data.data import Cal
from qlib.log import get_module_logger
from qlib.utils.time import concat_date_time, epsilon_change
if TYPE_CHECKING:
from qlib.strategy.base import BaseStrategy
from qlib.backtest.exchange import Exchange
from qlib.backtest.utils import TradeCalendarManager
from dataclasses import dataclass
import numpy as np
import pandas as pd
from dataclasses import dataclass
class OrderDir(IntEnum):
@@ -46,7 +49,7 @@ class Order:
# - they are set by users and is time-invariant.
stock_id: str
amount: float # `amount` is a non-negative and adjusted value
direction: int
direction: OrderDir
# 2) time variant values:
# - Users may want to set these values when using lower level APIs
@@ -61,7 +64,7 @@ class Order:
# What the value should be about in all kinds of cases
# - not tradable: the deal_amount == 0 , factor is None
# - the stock is suspended and the entire order fails. No cost for this order
# - dealed or partially dealed: deal_amount >= 0 and factor is not None
# - dealt or partially dealt: deal_amount >= 0 and factor is not None
deal_amount: Optional[float] = None # `deal_amount` is a non-negative value
factor: Optional[float] = None
@@ -74,10 +77,10 @@ class Order:
SELL: ClassVar[OrderDir] = OrderDir.SELL
BUY: ClassVar[OrderDir] = OrderDir.BUY
def __post_init__(self):
def __post_init__(self) -> None:
if self.direction not in {Order.SELL, Order.BUY}:
raise NotImplementedError("direction not supported, `Order.SELL` for sell, `Order.BUY` for buy")
self.deal_amount = 0
self.deal_amount = 0.0
self.factor = None
@property
@@ -99,7 +102,7 @@ class Order:
return self.deal_amount * self.sign
@property
def sign(self) -> float:
def sign(self) -> int:
"""
return the sign of trading
- `+1` indicates buying
@@ -112,15 +115,12 @@ class Order:
if isinstance(direction, OrderDir):
return direction
elif isinstance(direction, (int, float, np.integer, np.floating)):
if direction > 0:
return Order.BUY
else:
return Order.SELL
return Order.BUY if direction > 0 else Order.SELL
elif isinstance(direction, str):
dl = direction.lower()
if dl.strip() == "sell":
dl = direction.lower().strip()
if dl == "sell":
return OrderDir.SELL
elif dl.strip() == "buy":
elif dl == "buy":
return OrderDir.BUY
else:
raise NotImplementedError(f"This type of input is not supported")
@@ -138,14 +138,14 @@ class OrderHelper:
Motivation
- Make generating order easier
- User may have no knowledge about the adjust-factor information about the system.
- It involves to much interaction with the exchange when generating orders.
- It involves too much interaction with the exchange when generating orders.
"""
def __init__(self, exchange: Exchange):
def __init__(self, exchange: Exchange) -> None:
self.exchange = exchange
@staticmethod
def create(
self,
code: str,
amount: float,
direction: OrderDir,
@@ -175,21 +175,18 @@ class OrderHelper:
Order:
The created order
"""
if start_time is not None:
start_time = pd.Timestamp(start_time)
if end_time is not None:
end_time = pd.Timestamp(end_time)
# NOTE: factor is a value belongs to the results section. User don't have to care about it when creating orders
return Order(
stock_id=code,
amount=amount,
start_time=start_time,
end_time=end_time,
start_time=start_time if start_time is not None else pd.Timestamp(start_time),
end_time=end_time if end_time is not None else pd.Timestamp(end_time),
direction=direction,
)
class TradeRange:
@abstractmethod
def __call__(self, trade_calendar: TradeCalendarManager) -> Tuple[int, int]:
"""
This method will be call with following way
@@ -216,6 +213,7 @@ class TradeRange:
"""
raise NotImplementedError(f"Please implement the `__call__` method")
@abstractmethod
def clip_time_range(self, start_time: pd.Timestamp, end_time: pd.Timestamp) -> Tuple[pd.Timestamp, pd.Timestamp]:
"""
Parameters
@@ -234,23 +232,26 @@ class TradeRange:
class IdxTradeRange(TradeRange):
def __init__(self, start_idx: int, end_idx: int):
def __init__(self, start_idx: int, end_idx: int) -> None:
self._start_idx = start_idx
self._end_idx = end_idx
def __call__(self, trade_calendar: TradeCalendarManager = None) -> Tuple[int, int]:
return self._start_idx, self._end_idx
def clip_time_range(self, start_time: pd.Timestamp, end_time: pd.Timestamp) -> Tuple[pd.Timestamp, pd.Timestamp]:
raise NotImplementedError
class TradeRangeByTime(TradeRange):
"""This is a helper function for make decisions"""
def __init__(self, start_time: str, end_time: str):
def __init__(self, start_time: str, end_time: str) -> None:
"""
This is a callable class.
**NOTE**:
- It is designed for minute-bar for intraday trading!!!!!
- It is designed for minute-bar for intra-day trading!!!!!
- Both start_time and end_time are **closed** in the range
Parameters
@@ -264,26 +265,25 @@ class TradeRangeByTime(TradeRange):
self.end_time = pd.Timestamp(end_time).time()
assert self.start_time < self.end_time
def __call__(self, trade_calendar: TradeCalendarManager = None) -> Tuple[int, int]:
def __call__(self, trade_calendar: TradeCalendarManager) -> Tuple[int, int]:
if trade_calendar is None:
raise NotImplementedError("trade_calendar is necessary for getting TradeRangeByTime.")
start = trade_calendar.start_time
val_start, val_end = concat_date_time(start.date(), self.start_time), concat_date_time(
start.date(), self.end_time
)
start_date = trade_calendar.start_time.date()
val_start, val_end = concat_date_time(start_date, self.start_time), concat_date_time(start_date, self.end_time)
return trade_calendar.get_range_idx(val_start, val_end)
def clip_time_range(self, start_time: pd.Timestamp, end_time: pd.Timestamp) -> Tuple[pd.Timestamp, pd.Timestamp]:
start_date = start_time.date()
val_start, val_end = concat_date_time(start_date, self.start_time), concat_date_time(start_date, self.end_time)
# NOTE: `end_date` should not be used. Because the `end_date` is for slicing. It may be in the next day
# Assumption: start_time and end_time is for intraday trading. So it is OK for only using start_date
# Assumption: start_time and end_time is for intra-day trading. So it is OK for only using start_date
return max(val_start, start_time), min(val_end, end_time)
class BaseTradeDecision:
"""
Trade decisions ara made by strategy and executed by exeuter
Trade decisions ara made by strategy and executed by executor
Motivation:
Here are several typical scenarios for `BaseTradeDecision`
@@ -297,7 +297,7 @@ class BaseTradeDecision:
2. Same as `case 1.3`
"""
def __init__(self, strategy: BaseStrategy, trade_range: Union[Tuple[int, int], TradeRange] = None):
def __init__(self, strategy: BaseStrategy, trade_range: Union[Tuple[int, int], TradeRange] = None) -> None:
"""
Parameters
----------
@@ -339,7 +339,7 @@ class BaseTradeDecision:
"""
raise NotImplementedError(f"This type of input is not supported")
def update(self, trade_calendar: TradeCalendarManager) -> Union["BaseTradeDecision", None]:
def update(self, trade_calendar: TradeCalendarManager) -> Optional[BaseTradeDecision]:
"""
Be called at the **start** of each step.
@@ -354,10 +354,8 @@ class BaseTradeDecision:
Returns
-------
None:
No update, use previous decision(or unavailable)
BaseTradeDecision:
New update, use new decision
New update, use new decision. If no updates, return None (use previous decision (or unavailable))
"""
# purpose 1)
self.total_step = trade_calendar.get_trade_len()
@@ -412,12 +410,12 @@ class BaseTradeDecision:
"""
try:
_start_idx, _end_idx = self._get_range_limit(**kwargs)
except NotImplementedError:
except NotImplementedError as e:
if "default_value" in kwargs:
return kwargs["default_value"]
else:
# Default to get full index
raise NotImplementedError(f"The decision didn't provide an index range") from NotImplementedError
raise NotImplementedError(f"The decision didn't provide an index range") from e
# clip index
if getattr(self, "total_step", None) is not None:
@@ -426,7 +424,7 @@ class BaseTradeDecision:
if _start_idx < 0 or _end_idx >= self.total_step:
logger = get_module_logger("decision")
logger.warning(
f"[{_start_idx},{_end_idx}] go beyoud the total_step({self.total_step}), it will be clipped"
f"[{_start_idx},{_end_idx}] go beyond the total_step({self.total_step}), it will be clipped.",
)
_start_idx, _end_idx = max(0, _start_idx), min(self.total_step - 1, _end_idx)
return _start_idx, _end_idx
@@ -444,7 +442,7 @@ class BaseTradeDecision:
Parameters
----------
rtype: str
- "full": return the full limitation of the deicsion in the day
- "full": return the full limitation of the decision in the day
- "step": return the limitation of current step
raise_error: bool
@@ -497,11 +495,10 @@ class BaseTradeDecision:
return True
return True
def mod_inner_decision(self, inner_trade_decision: BaseTradeDecision):
def mod_inner_decision(self, inner_trade_decision: BaseTradeDecision) -> None:
"""
This method will be called on the inner_trade_decision after it is generated.
`inner_trade_decision` will be changed **inplaced**.
`inner_trade_decision` will be changed **inplace**.
Motivation of the `mod_inner_decision`
- Leave a hook for outer decision to affect the decision generated by the inner strategy
@@ -520,6 +517,9 @@ class BaseTradeDecision:
class EmptyTradeDecision(BaseTradeDecision):
def get_decision(self) -> List[object]:
return []
def empty(self) -> bool:
return True
@@ -544,4 +544,9 @@ class TradeDecisionWO(BaseTradeDecision):
return self.order_list
def __repr__(self) -> str:
return f"class: {self.__class__.__name__}; strategy: {self.strategy}; trade_range: {self.trade_range}; order_list[{len(self.order_list)}]"
return (
f"class: {self.__class__.__name__}; "
f"strategy: {self.strategy}; "
f"trade_range: {self.trade_range}; "
f"order_list[{len(self.order_list)}]"
)

304
qlib/backtest/exchange.py
View File

@@ -1,21 +1,25 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import annotations
from collections import defaultdict
from typing import TYPE_CHECKING
from typing import List, Tuple, Union
from typing import TYPE_CHECKING, List, Optional, Tuple, Type, Union
from ..utils.index_data import IndexData
if TYPE_CHECKING:
from .account import Account
from qlib.backtest.position import BasePosition, Position
import random
import numpy as np
import pandas as pd
from ..data.data import D
from qlib.backtest.position import BasePosition
from ..config import C
from ..constant import REG_CN
from ..data.data import D
from ..log import get_module_logger
from .decision import Order, OrderDir, OrderHelper
from .high_performance_ds import BaseQuote, NumpyQuote
@@ -24,22 +28,22 @@ from .high_performance_ds import BaseQuote, NumpyQuote
class Exchange:
def __init__(
self,
freq="day",
start_time=None,
end_time=None,
codes="all",
freq: str = "day",
start_time: Union[pd.Timestamp, str] = None,
end_time: Union[pd.Timestamp, str] = None,
codes: Union[list, str] = "all",
deal_price: Union[str, Tuple[str], List[str]] = None,
subscribe_fields=[],
subscribe_fields: list = [],
limit_threshold: Union[Tuple[str, str], float, None] = None,
volume_threshold=None,
open_cost=0.0015,
close_cost=0.0025,
min_cost=5,
impact_cost=0.0,
extra_quote=None,
quote_cls=NumpyQuote,
volume_threshold: Union[tuple, dict] = None,
open_cost: float = 0.0015,
close_cost: float = 0.0025,
min_cost: float = 5.0,
impact_cost: float = 0.0,
extra_quote: pd.DataFrame = None,
quote_cls: Type[BaseQuote] = NumpyQuote,
**kwargs,
):
) -> None:
"""__init__
:param freq: frequency of data
:param start_time: closed start time for backtest
@@ -72,11 +76,12 @@ class Exchange:
]
1) ("cum" or "current", limit_str) denotes a single volume limit.
- limit_str is qlib data expression which is allowed to define your own Operator.
Please refer to qlib/contrib/ops/high_freq.py, here are any custom operator for high frequency,
such as DayCumsum. !!!NOTE: if you want you use the custom operator, you need to
register it in qlib_init.
- "cum" means that this is a cumulative value over time, such as cumulative market volume.
So when it is used as a volume limit, it is necessary to subtract the dealt amount.
Please refer to qlib/contrib/ops/high_freq.py, here are any custom operator for
high frequency, such as DayCumsum. !!!NOTE: if you want you use the custom
operator, you need to register it in qlib_init.
- "cum" means that this is a cumulative value over time, such as cumulative market
volume. So when it is used as a volume limit, it is necessary to subtract the dealt
amount.
- "current" means that this is a real-time value and will not accumulate over time,
so it can be directly used as a capacity limit.
e.g. ("cum", "0.2 * DayCumsum($volume, '9:45', '14:45')"), ("current", "$bidV1")
@@ -84,7 +89,7 @@ class Exchange:
"buy" means the volume limits of buying. "sell" means the volume limits of selling.
Different volume limits will be aggregated with min(). If volume_threshold is only
("cum" or "current", limit_str) instead of a dict, the volume limits are for
both by deault. In other words, it is same as {"all": ("cum" or "current", limit_str)}.
both by default. In other words, it is same as {"all": ("cum" or "current", limit_str)}.
3) e.g. "volume_threshold": {
"all": ("cum", "0.2 * DayCumsum($volume, '9:45', '14:45')"),
"buy": ("current", "$askV1"),
@@ -104,13 +109,14 @@ class Exchange:
Necessary fields:
$close is for calculating the total value at end of each day.
Optional fields:
$volume is only necessary when we limit the trade amount or calculate PA(vwap) indicator
$volume is only necessary when we limit the trade amount or calculate
PA(vwap) indicator
$vwap is only necessary when we use the $vwap price as the deal price
$factor is for rounding to the trading unit
limit_sell will be set to False by default(False indicates we can sell this
target on this day).
limit_buy will be set to False by default(False indicates we can buy this
target on this day).
limit_sell will be set to False by default (False indicates we can sell
this target on this day).
limit_buy will be set to False by default (False indicates we can buy
this target on this day).
index: MultipleIndex(instrument, pd.Datetime)
"""
self.freq = freq
@@ -163,7 +169,7 @@ class Exchange:
if self.limit_type == self.LT_TP_EXP:
for exp in limit_threshold:
necessary_fields.add(exp)
all_fields = necessary_fields | vol_lt_fields
all_fields = necessary_fields | set(vol_lt_fields)
all_fields = list(all_fields | set(subscribe_fields))
self.all_fields = all_fields
@@ -182,17 +188,22 @@ class Exchange:
self.quote_cls = quote_cls
self.quote: BaseQuote = self.quote_cls(self.quote_df, freq)
def get_quote_from_qlib(self):
def get_quote_from_qlib(self) -> None:
# get stock data from qlib
if len(self.codes) == 0:
self.codes = D.instruments()
self.quote_df = D.features(
self.codes, self.all_fields, self.start_time, self.end_time, freq=self.freq, disk_cache=True
self.codes,
self.all_fields,
self.start_time,
self.end_time,
freq=self.freq,
disk_cache=True,
).dropna(subset=["$close"])
self.quote_df.columns = self.all_fields
# check buy_price data and sell_price data
for attr in "buy_price", "sell_price":
for attr in ("buy_price", "sell_price"):
pstr = getattr(self, attr) # price string
if self.quote_df[pstr].isna().any():
self.logger.warning("{} field data contains nan.".format(pstr))
@@ -238,7 +249,7 @@ class Exchange:
LT_FLT = "float" # float
LT_NONE = "none" # none
def _get_limit_type(self, limit_threshold):
def _get_limit_type(self, limit_threshold: Union[Tuple, float, None]) -> str:
"""get limit type"""
if isinstance(limit_threshold, Tuple):
return self.LT_TP_EXP
@@ -249,7 +260,7 @@ class Exchange:
else:
raise NotImplementedError(f"This type of `limit_threshold` is not supported")
def _update_limit(self, limit_threshold):
def _update_limit(self, limit_threshold: Union[Tuple, float, None]) -> None:
# check limit_threshold
limit_type = self._get_limit_type(limit_threshold)
if limit_type == self.LT_NONE:
@@ -263,9 +274,10 @@ class Exchange:
self.quote_df["limit_buy"] = self.quote_df["$change"].ge(limit_threshold)
self.quote_df["limit_sell"] = self.quote_df["$change"].le(-limit_threshold) # pylint: disable=E1130
def _get_vol_limit(self, volume_threshold):
@staticmethod
def _get_vol_limit(volume_threshold: Union[tuple, dict]) -> Tuple[Optional[list], Optional[list], set]:
"""
preproccess the volume limit.
preprocess the volume limit.
get the fields need to get from qlib.
get the volume limit list of buying and selling which is composed of all limits.
Parameters
@@ -295,8 +307,7 @@ class Exchange:
volume_threshold = {"all": volume_threshold}
assert isinstance(volume_threshold, dict)
for key in volume_threshold:
vol_limit = volume_threshold[key]
for key, vol_limit in volume_threshold.items():
assert isinstance(vol_limit, tuple)
fields.add(vol_limit[1])
@@ -307,10 +318,19 @@ class Exchange:
return buy_vol_limit, sell_vol_limit, fields
def check_stock_limit(self, stock_id, start_time, end_time, direction=None):
def check_stock_limit(
self,
stock_id: str,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
direction: int = None,
) -> bool:
"""
Parameters
----------
stock_id : str
start_time: pd.Timestamp
end_time: pd.Timestamp
direction : int, optional
trade direction, by default None
- if direction is None, check if tradable for buying and selling.
@@ -328,39 +348,42 @@ class Exchange:
else:
raise ValueError(f"direction {direction} is not supported!")
def check_stock_suspended(self, stock_id, start_time, end_time):
def check_stock_suspended(
self,
stock_id: str,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
) -> bool:
# is suspended
if stock_id in self.quote.get_all_stock():
return self.quote.get_data(stock_id, start_time, end_time, "$close") is None
else:
return True
def is_stock_tradable(self, stock_id, start_time, end_time, direction=None):
def is_stock_tradable(
self,
stock_id: str,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
direction: int = None,
) -> bool:
# check if stock can be traded
# same as check in check_order
if self.check_stock_suspended(stock_id, start_time, end_time) or self.check_stock_limit(
stock_id, start_time, end_time, direction
):
return False
else:
return True
return not (
self.check_stock_suspended(stock_id, start_time, end_time)
or self.check_stock_limit(stock_id, start_time, end_time, direction)
)
def check_order(self, order):
def check_order(self, order: Order) -> bool:
# check limit and suspended
if self.check_stock_suspended(order.stock_id, order.start_time, order.end_time) or self.check_stock_limit(
order.stock_id, order.start_time, order.end_time, order.direction
):
return False
else:
return True
return self.is_stock_tradable(order.stock_id, order.start_time, order.end_time, order.direction)
def deal_order(
self,
order,
order: Order,
trade_account: Account = None,
position: BasePosition = None,
dealt_order_amount: defaultdict = defaultdict(float),
):
) -> Tuple[float, float, float]:
"""
Deal order when the actual transaction
the results section in `Order` will be changed.
@@ -371,9 +394,9 @@ class Exchange:
:return: trade_val, trade_cost, trade_price
"""
# check order first.
if self.check_order(order) is False:
if not self.check_order(order):
order.deal_amount = 0.0
# using np.nan instead of None to make it more convenient to should the value in format string
# using np.nan instead of None to make it more convenient to show the value in format string
self.logger.debug(f"Order failed due to trading limitation: {order}")
return 0.0, 0.0, np.nan
@@ -382,7 +405,9 @@ class Exchange:
# NOTE: order will be changed in this function
trade_price, trade_val, trade_cost = self._calc_trade_info_by_order(
order, trade_account.current_position if trade_account else position, dealt_order_amount
order,
trade_account.current_position if trade_account else position,
dealt_order_amount,
)
if trade_val > 1e-5:
# If the order can only be deal 0 value. Nothing to be updated
@@ -396,23 +421,49 @@ class Exchange:
return trade_val, trade_cost, trade_price
def get_quote_info(self, stock_id, start_time, end_time, method="ts_data_last"):
return self.quote.get_data(stock_id, start_time, end_time, method=method)
def get_quote_info(
self,
stock_id: str,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
method: str = "ts_data_last",
) -> Union[None, int, float, bool, IndexData]:
return self.quote.get_data(stock_id, start_time, end_time, method=method) # TODO: missing `field`?
def get_close(self, stock_id, start_time, end_time, method="ts_data_last"):
def get_close(
self,
stock_id: str,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
method: str = "ts_data_last",
) -> Union[None, int, float, bool, IndexData]:
return self.quote.get_data(stock_id, start_time, end_time, field="$close", method=method)
def get_volume(self, stock_id, start_time, end_time, method="sum"):
def get_volume(
self,
stock_id: str,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
method: str = "sum",
) -> float:
"""get the total deal volume of stock with `stock_id` between the time interval [start_time, end_time)"""
return self.quote.get_data(stock_id, start_time, end_time, field="$volume", method=method)
def get_deal_price(self, stock_id, start_time, end_time, direction: OrderDir, method="ts_data_last"):
def get_deal_price(
self,
stock_id: str,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
direction: OrderDir,
method: str = "ts_data_last",
) -> float:
if direction == OrderDir.SELL:
pstr = self.sell_price
elif direction == OrderDir.BUY:
pstr = self.buy_price
else:
raise NotImplementedError(f"This type of input is not supported")
deal_price = self.quote.get_data(stock_id, start_time, end_time, field=pstr, method=method)
if method is not None and (deal_price is None or np.isnan(deal_price) or deal_price <= 1e-08):
self.logger.warning(f"(stock_id:{stock_id}, trade_time:{(start_time, end_time)}, {pstr}): {deal_price}!!!")
@@ -420,11 +471,16 @@ class Exchange:
deal_price = self.get_close(stock_id, start_time, end_time, method)
return deal_price
def get_factor(self, stock_id, start_time, end_time) -> Union[float, None]:
def get_factor(
self,
stock_id: str,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
) -> Optional[float]:
"""
Returns
-------
Union[float, None]:
Optional[float]:
`None`: if the stock is suspended `None` may be returned
`float`: return factor if the factor exists
"""
@@ -434,11 +490,16 @@ class Exchange:
return self.quote.get_data(stock_id, start_time, end_time, field="$factor", method="ts_data_last")
def generate_amount_position_from_weight_position(
self, weight_position, cash, start_time, end_time, direction=OrderDir.BUY
):
self,
weight_position: dict,
cash: float,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
direction: OrderDir = OrderDir.BUY,
) -> dict:
"""
The generate the target position according to the weight and the cash.
NOTE: All the cash will assigned to the tadable stock.
NOTE: All the cash will assigned to the tradable stock.
Parameter:
weight_position : dict {stock_id : weight}; allocate cash by weight_position
among then, weight must be in this range: 0 < weight < 1
@@ -451,15 +512,14 @@ class Exchange:
# calculate the total weight of tradable value
tradable_weight = 0.0
for stock_id in weight_position:
for stock_id, wp in weight_position.items():
if self.is_stock_tradable(stock_id=stock_id, start_time=start_time, end_time=end_time):
# weight_position must be greater than 0 and less than 1
if weight_position[stock_id] < 0 or weight_position[stock_id] > 1:
if wp < 0 or wp > 1:
raise ValueError(
"weight_position is {}, "
"weight_position is not in the range of (0, 1).".format(weight_position[stock_id])
"weight_position is {}, " "weight_position is not in the range of (0, 1).".format(wp),
)
tradable_weight += weight_position[stock_id]
tradable_weight += wp
if tradable_weight - 1.0 >= 1e-5:
raise ValueError("tradable_weight is {}, can not greater than 1.".format(tradable_weight))
@@ -467,19 +527,24 @@ class Exchange:
amount_dict = {}
for stock_id in weight_position:
if weight_position[stock_id] > 0.0 and self.is_stock_tradable(
stock_id=stock_id, start_time=start_time, end_time=end_time
stock_id=stock_id,
start_time=start_time,
end_time=end_time,
):
amount_dict[stock_id] = (
cash
* weight_position[stock_id]
/ tradable_weight
// self.get_deal_price(
stock_id=stock_id, start_time=start_time, end_time=end_time, direction=direction
stock_id=stock_id,
start_time=start_time,
end_time=end_time,
direction=direction,
)
)
return amount_dict
def get_real_deal_amount(self, current_amount, target_amount, factor):
def get_real_deal_amount(self, current_amount: float, target_amount: float, factor: float) -> float:
"""
Calculate the real adjust deal amount when considering the trading unit
:param current_amount:
@@ -501,7 +566,13 @@ class Exchange:
deal_amount = self.round_amount_by_trade_unit(deal_amount, factor)
return -deal_amount
def generate_order_for_target_amount_position(self, target_position, current_position, start_time, end_time):
def generate_order_for_target_amount_position(
self,
target_position: dict,
current_position: dict,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
) -> list:
"""
Note: some future information is used in this function
Parameter:
@@ -517,7 +588,8 @@ class Exchange:
# three parts: kept stock_id, dropped stock_id, new stock_id
# handle kept stock_id
# because the order of the set is not fixed, the trading order of the stock is different, so that the backtest results of the same parameter are different;
# because the order of the set is not fixed, the trading order of the stock is different, so that the backtest
# results of the same parameter are different;
# so here we sort stock_id, and then randomly shuffle the order of stock_id
# because the same random seed is used, the final stock_id order is fixed
sorted_ids = sorted(set(list(current_position.keys()) + list(target_position.keys())))
@@ -546,7 +618,7 @@ class Exchange:
start_time=start_time,
end_time=end_time,
factor=factor,
)
),
)
else:
# sell stock
@@ -558,14 +630,19 @@ class Exchange:
start_time=start_time,
end_time=end_time,
factor=factor,
)
),
)
# return order_list : buy + sell
return sell_order_list + buy_order_list
def calculate_amount_position_value(
self, amount_dict, start_time, end_time, only_tradable=False, direction=OrderDir.SELL
):
self,
amount_dict: dict,
start_time: pd.Timestamp,
end_time: pd.Timestamp,
only_tradable: bool = False,
direction: OrderDir = OrderDir.SELL,
) -> float:
"""Parameter
position : Position()
amount_dict : {stock_id : amount}
@@ -576,21 +653,28 @@ class Exchange:
"""
value = 0
for stock_id in amount_dict:
if (
only_tradable is True
and self.check_stock_suspended(stock_id=stock_id, start_time=start_time, end_time=end_time) is False
and self.check_stock_limit(stock_id=stock_id, start_time=start_time, end_time=end_time) is False
or only_tradable is False
if not only_tradable or (
not self.check_stock_suspended(stock_id=stock_id, start_time=start_time, end_time=end_time)
and not self.check_stock_limit(stock_id=stock_id, start_time=start_time, end_time=end_time)
):
value += (
self.get_deal_price(
stock_id=stock_id, start_time=start_time, end_time=end_time, direction=direction
stock_id=stock_id,
start_time=start_time,
end_time=end_time,
direction=direction,
)
* amount_dict[stock_id]
)
return value
def _get_factor_or_raise_error(self, factor: float = None, stock_id: str = None, start_time=None, end_time=None):
def _get_factor_or_raise_error(
self,
factor: float = None,
stock_id: str = None,
start_time: pd.Timestamp = None,
end_time: pd.Timestamp = None,
) -> float:
"""Please refer to the docs of get_amount_of_trade_unit"""
if factor is None:
if stock_id is not None and start_time is not None and end_time is not None:
@@ -599,7 +683,13 @@ class Exchange:
raise ValueError(f"`factor` and (`stock_id`, `start_time`, `end_time`) can't both be None")
return factor
def get_amount_of_trade_unit(self, factor: float = None, stock_id: str = None, start_time=None, end_time=None):
def get_amount_of_trade_unit(
self,
factor: float = None,
stock_id: str = None,
start_time: pd.Timestamp = None,
end_time: pd.Timestamp = None,
) -> Optional[float]:
"""
get the trade unit of amount based on **factor**
the factor can be given directly or calculated in given time range and stock id.
@@ -617,14 +707,22 @@ class Exchange:
"""
if not self.trade_w_adj_price and self.trade_unit is not None:
factor = self._get_factor_or_raise_error(
factor=factor, stock_id=stock_id, start_time=start_time, end_time=end_time
factor=factor,
stock_id=stock_id,
start_time=start_time,
end_time=end_time,
)
return self.trade_unit / factor
else:
return None
def round_amount_by_trade_unit(
self, deal_amount, factor: float = None, stock_id: str = None, start_time=None, end_time=None
self,
deal_amount,
factor: float = None,
stock_id: str = None,
start_time=None,
end_time=None,
):
"""Parameter
Please refer to the docs of get_amount_of_trade_unit
@@ -635,7 +733,10 @@ class Exchange:
if not self.trade_w_adj_price and self.trade_unit is not None:
# the minimal amount is 1. Add 0.1 for solving precision problem.
factor = self._get_factor_or_raise_error(
factor=factor, stock_id=stock_id, start_time=start_time, end_time=end_time
factor=factor,
stock_id=stock_id,
start_time=start_time,
end_time=end_time,
)
return (deal_amount * factor + 0.1) // self.trade_unit * self.trade_unit / factor
return deal_amount
@@ -714,7 +815,12 @@ class Exchange:
max_trade_amount = (cash - self.min_cost) / trade_price
return max_trade_amount
def _calc_trade_info_by_order(self, order, position: Position, dealt_order_amount):
def _calc_trade_info_by_order(
self,
order: Order,
position: Optional[BasePosition],
dealt_order_amount: dict,
) -> Tuple[float, float, float]:
"""
Calculation of trade info
**NOTE**: Order will be changed in this function
@@ -753,7 +859,8 @@ class Exchange:
if not np.isclose(order.deal_amount, current_amount):
# when not selling last stock. rounding is necessary
order.deal_amount = self.round_amount_by_trade_unit(
min(current_amount, order.deal_amount), order.factor
min(current_amount, order.deal_amount),
order.factor,
)
# in case of negative value of cash
@@ -778,7 +885,8 @@ class Exchange:
# The money is not enough
max_buy_amount = self._get_buy_amount_by_cash_limit(trade_price, cash, cost_ratio)
order.deal_amount = self.round_amount_by_trade_unit(
min(max_buy_amount, order.deal_amount), order.factor
min(max_buy_amount, order.deal_amount),
order.factor,
)
self.logger.debug(f"Order clipped due to cash limitation: {order}")
else:

182
qlib/backtest/executor.py
View File

@@ -1,19 +1,28 @@
from abc import abstractmethod
from __future__ import annotations
import copy
from abc import abstractmethod
from collections import defaultdict
from types import GeneratorType
from typing import Generator, List, Optional, Tuple, Union
import pandas as pd
from qlib.backtest.account import Account
from qlib.backtest.position import BasePosition
from qlib.log import get_module_logger
from types import GeneratorType
from qlib.backtest.account import Account
import pandas as pd
from typing import List, Tuple, Union
from collections import defaultdict
from .decision import Order, BaseTradeDecision
from .exchange import Exchange
from .utils import TradeCalendarManager, CommonInfrastructure, LevelInfrastructure, get_start_end_idx
from ..utils import init_instance_by_config
from ..strategy.base import BaseStrategy
from ..utils import init_instance_by_config
from .decision import BaseTradeDecision, Order
from .exchange import Exchange
from .utils import (
BaseInfrastructure,
CommonInfrastructure,
LevelInfrastructure,
TradeCalendarManager,
get_start_end_idx,
)
class BaseExecutor:
@@ -30,9 +39,9 @@ class BaseExecutor:
track_data: bool = False,
trade_exchange: Exchange = None,
common_infra: CommonInfrastructure = None,
settle_type=BasePosition.ST_NO,
settle_type=BasePosition.ST_NO, # TODO: add typehint
**kwargs,
):
) -> None:
"""
Parameters
----------
@@ -53,15 +62,21 @@ class BaseExecutor:
- 'base_price': the based price than which the trading price is advanced, Optional, default by 'twap'
- If 'base_price' is 'twap', the based price is the time weighted average price
- If 'base_price' is 'vwap', the based price is the volume weighted average price
- 'weight_method': weighted method when calculating total trading pa by different orders' pa in each step, optional, default by 'mean'
- 'weight_method': weighted method when calculating total trading pa by different orders' pa in each
step, optional, default by 'mean'
- If 'weight_method' is 'mean', calculating mean value of different orders' pa
- If 'weight_method' is 'amount_weighted', calculating amount weighted average value of different orders' pa
- If 'weight_method' is 'value_weighted', calculating value weighted average value of different orders' pa
- If 'weight_method' is 'amount_weighted', calculating amount weighted average value of different
orders' pa
- If 'weight_method' is 'value_weighted', calculating value weighted average value of different
orders' pa
- 'ffr_config': config for calculating fulfill rate(ffr), optional
- 'weight_method': weighted method when calculating total trading ffr by different orders' ffr in each step, optional, default by 'mean'
- 'weight_method': weighted method when calculating total trading ffr by different orders' ffr in each
step, optional, default by 'mean'
- If 'weight_method' is 'mean', calculating mean value of different orders' ffr
- If 'weight_method' is 'amount_weighted', calculating amount weighted average value of different orders' ffr
- If 'weight_method' is 'value_weighted', calculating value weighted average value of different orders' ffr
- If 'weight_method' is 'amount_weighted', calculating amount weighted average value of different
orders' ffr
- If 'weight_method' is 'value_weighted', calculating value weighted average value of different
orders' ffr
Example:
{
'show_indicator': True,
@@ -79,7 +94,8 @@ class BaseExecutor:
whether to print trading info, by default False
track_data : bool, optional
whether to generate trade_decision, will be used when training rl agent
- If `self.track_data` is true, when making data for training, the input `trade_decision` of `execute` will be generated by `collect_data`
- If `self.track_data` is true, when making data for training, the input `trade_decision` of `execute` will
be generated by `collect_data`
- Else, `trade_decision` will not be generated
trade_exchange : Exchange
@@ -114,7 +130,7 @@ class BaseExecutor:
self.dealt_order_amount = defaultdict(float)
self.deal_day = None
def reset_common_infra(self, common_infra, copy_trade_account=False):
def reset_common_infra(self, common_infra: BaseInfrastructure, copy_trade_account: bool = False) -> None:
"""
reset infrastructure for trading
- reset trade_account
@@ -132,7 +148,7 @@ class BaseExecutor:
# 2. Others are not shared, so each level has it own metrics (portfolio and trading metrics)
self.trade_account: Account = copy.copy(common_infra.get("trade_account"))
else:
self.trade_account = common_infra.get("trade_account")
self.trade_account: Account = common_infra.get("trade_account")
self.trade_account.reset(freq=self.time_per_step, port_metr_enabled=self.generate_portfolio_metrics)
@property
@@ -148,7 +164,7 @@ class BaseExecutor:
"""
return self.level_infra.get("trade_calendar")
def reset(self, common_infra: CommonInfrastructure = None, **kwargs):
def reset(self, common_infra: CommonInfrastructure = None, **kwargs) -> None:
"""
- reset `start_time` and `end_time`, used in trade calendar
- reset `common_infra`, used to reset `trade_account`, `trade_exchange`, .etc
@@ -161,13 +177,13 @@ class BaseExecutor:
if common_infra is not None:
self.reset_common_infra(common_infra)
def get_level_infra(self):
def get_level_infra(self) -> LevelInfrastructure:
return self.level_infra
def finished(self):
def finished(self) -> bool:
return self.trade_calendar.finished()
def execute(self, trade_decision: BaseTradeDecision, level: int = 0):
def execute(self, trade_decision: BaseTradeDecision, level: int = 0) -> List[object]:
"""execute the trade decision and return the executed result
NOTE: this function is never used directly in the framework. Should we delete it?
@@ -189,9 +205,15 @@ class BaseExecutor:
pass
return return_value.get("execute_result")
@classmethod
@abstractmethod
def _collect_data(cls, trade_decision: BaseTradeDecision, level: int = 0) -> Tuple[List[object], dict]:
def _collect_data(
self,
trade_decision: BaseTradeDecision,
level: int = 0,
) -> Union[
Generator[BaseTradeDecision, Optional[BaseTradeDecision], Tuple[List[object], dict]],
Tuple[List[object], dict],
]:
"""
Please refer to the doc of collect_data
The only difference between `_collect_data` and `collect_data` is that some common steps are moved into
@@ -209,8 +231,11 @@ class BaseExecutor:
"""
def collect_data(
self, trade_decision: BaseTradeDecision, return_value: dict = None, level: int = 0
) -> List[object]:
self,
trade_decision: BaseTradeDecision,
return_value: dict = None,
level: int = 0,
) -> Generator[BaseTradeDecision, Optional[BaseTradeDecision], List[object]]:
"""Generator for collecting the trade decision data for rl training
his function will make a step forward
@@ -253,7 +278,9 @@ class BaseExecutor:
obj = self._collect_data(trade_decision=trade_decision, level=level)
if isinstance(obj, GeneratorType):
res, kwargs = yield from obj
yield_res = yield from obj
assert isinstance(yield_res, tuple) and len(yield_res) == 2
res, kwargs = yield_res
else:
# Some concrete executor don't have inner decisions
res, kwargs = obj
@@ -279,7 +306,7 @@ class BaseExecutor:
return_value.update({"execute_result": res})
return res
def get_all_executors(self):
def get_all_executors(self) -> List[BaseExecutor]:
"""get all executors"""
return [self]
@@ -287,7 +314,8 @@ class BaseExecutor:
class NestedExecutor(BaseExecutor):
"""
Nested Executor with inner strategy and executor
- At each time `execute` is called, it will call the inner strategy and executor to execute the `trade_decision` in a higher frequency env.
- At each time `execute` is called, it will call the inner strategy and executor to execute the `trade_decision`
in a higher frequency env.
"""
def __init__(
@@ -305,7 +333,7 @@ class NestedExecutor(BaseExecutor):
align_range_limit: bool = True,
common_infra: CommonInfrastructure = None,
**kwargs,
):
) -> None:
"""
Parameters
----------
@@ -323,10 +351,14 @@ class NestedExecutor(BaseExecutor):
It is only for nested executor, because range_limit is given by outer strategy
"""
self.inner_executor: BaseExecutor = init_instance_by_config(
inner_executor, common_infra=common_infra, accept_types=BaseExecutor
inner_executor,
common_infra=common_infra,
accept_types=BaseExecutor,
)
self.inner_strategy: BaseStrategy = init_instance_by_config(
inner_strategy, common_infra=common_infra, accept_types=BaseStrategy
inner_strategy,
common_infra=common_infra,
accept_types=BaseStrategy,
)
self._skip_empty_decision = skip_empty_decision
@@ -344,10 +376,10 @@ class NestedExecutor(BaseExecutor):
**kwargs,
)
def reset_common_infra(self, common_infra, copy_trade_account=False):
def reset_common_infra(self, common_infra: CommonInfrastructure, copy_trade_account: bool = False) -> None:
"""
reset infrastructure for trading
- reset inner_strategyand inner_executor common infra
- reset inner_strategy and inner_executor common infra
"""
# NOTE: please refer to the docs of BaseExecutor.reset_common_infra for the meaning of `copy_trade_account`
@@ -358,7 +390,7 @@ class NestedExecutor(BaseExecutor):
self.inner_executor.reset_common_infra(common_infra, copy_trade_account=True)
self.inner_strategy.reset_common_infra(common_infra)
def _init_sub_trading(self, trade_decision):
def _init_sub_trading(self, trade_decision: BaseTradeDecision) -> None:
trade_start_time, trade_end_time = self.trade_calendar.get_step_time()
self.inner_executor.reset(start_time=trade_start_time, end_time=trade_end_time)
sub_level_infra = self.inner_executor.get_level_infra()
@@ -368,14 +400,18 @@ class NestedExecutor(BaseExecutor):
def _update_trade_decision(self, trade_decision: BaseTradeDecision) -> BaseTradeDecision:
# outer strategy have chance to update decision each iterator
updated_trade_decision = trade_decision.update(self.inner_executor.trade_calendar)
if updated_trade_decision is not None:
if updated_trade_decision is not None: # TODO: always is None for now?
trade_decision = updated_trade_decision
# NEW UPDATE
# create a hook for inner strategy to update outer decision
self.inner_strategy.alter_outer_trade_decision(trade_decision)
return trade_decision
def _collect_data(self, trade_decision: BaseTradeDecision, level: int = 0):
def _collect_data(
self,
trade_decision: BaseTradeDecision,
level: int = 0,
) -> Generator[BaseTradeDecision, Optional[BaseTradeDecision], Tuple[List[object], dict]]:
execute_result = []
inner_order_indicators = []
decision_list = []
@@ -390,8 +426,8 @@ class NestedExecutor(BaseExecutor):
if trade_decision.empty() and self._skip_empty_decision:
# give one chance for outer strategy to update the strategy
# - For updating some information in the sub executor(the strategy have no knowledge of the inner
# executor when generating the decision)
# - For updating some information in the sub executor (the strategy have no knowledge of the inner
# executor when generating the decision)
break
sub_cal: TradeCalendarManager = self.inner_executor.trade_calendar
@@ -405,15 +441,19 @@ class NestedExecutor(BaseExecutor):
# NOTE: !!!!!
# the two lines below is for a special case in RL
# To solve the confliction below
# - Normally, user will create a strategy and embed it into Qlib's executor and simulator interaction loop
# For a _nested qlib example_, (Qlib Strategy) <=> (Qlib Executor[(inner Qlib Strategy) <=> (inner Qlib Executor)])
# To solve the conflicts below
# - Normally, user will create a strategy and embed it into Qlib's executor and simulator interaction
# loop For a _nested qlib example_, (Qlib Strategy) <=> (Qlib Executor[(inner Qlib Strategy) <=>
# (inner Qlib Executor)])
# - However, RL-based framework has it's own script to run the loop
# For an _RL learning example_, (RL Policy) <=> (RL Env[(inner Qlib Executor)])
# To make it possible to run _nested qlib example_ and _RL learning example_ together, the solution below is proposed
# - The entry script follow the example of _RL learning example_ to be compatible with all kinds of RL Framework
# To make it possible to run _nested qlib example_ and _RL learning example_ together, the solution
# below is proposed
# - The entry script follow the example of _RL learning example_ to be compatible with all kinds of
# RL Framework
# - Each step of (RL Env) will make (inner Qlib Executor) one step forward
# - (inner Qlib Strategy) is a proxy strategy, it will give the program control right to (RL Env) by `yield from` and wait for the action from the policy
# - (inner Qlib Strategy) is a proxy strategy, it will give the program control right to (RL Env)
# by `yield from` and wait for the action from the policy
# So the two lines below is the implementation of yielding control rights
if isinstance(res, GeneratorType):
res = yield from res
@@ -427,13 +467,15 @@ class NestedExecutor(BaseExecutor):
# NOTE: Trade Calendar will step forward in the follow line
_inner_execute_result = yield from self.inner_executor.collect_data(
trade_decision=_inner_trade_decision, level=level + 1
trade_decision=_inner_trade_decision,
level=level + 1,
)
assert isinstance(_inner_execute_result, list)
self.post_inner_exe_step(_inner_execute_result)
execute_result.extend(_inner_execute_result)
inner_order_indicators.append(
self.inner_executor.trade_account.get_trade_indicator().get_order_indicator(raw=True)
self.inner_executor.trade_account.get_trade_indicator().get_order_indicator(raw=True),
)
else:
# do nothing and just step forward
@@ -441,7 +483,7 @@ class NestedExecutor(BaseExecutor):
return execute_result, {"inner_order_indicators": inner_order_indicators, "decision_list": decision_list}
def post_inner_exe_step(self, inner_exe_res):
def post_inner_exe_step(self, inner_exe_res: List[object]) -> None:
"""
A hook for doing sth after each step of inner strategy
@@ -451,11 +493,23 @@ class NestedExecutor(BaseExecutor):
the execution result of inner task
"""
def get_all_executors(self):
def get_all_executors(self) -> List[object]:
"""get all executors, including self and inner_executor.get_all_executors()"""
return [self, *self.inner_executor.get_all_executors()]
def _retrieve_orders_from_decision(trade_decision: BaseTradeDecision) -> List[Order]:
"""
IDE-friendly helper function.
"""
decisions = trade_decision.get_decision()
orders: List[Order] = []
for decision in decisions:
assert isinstance(decision, Order)
orders.append(decision)
return orders
class SimulatorExecutor(BaseExecutor):
"""Executor that simulate the true market"""
@@ -464,10 +518,10 @@ class SimulatorExecutor(BaseExecutor):
# available trade_types
TT_SERIAL = "serial"
## The orders will be executed serially in a sequence
# The orders will be executed serially in a sequence
# In each trading step, it is possible that users sell instruments first and use the money to buy new instruments
TT_PARAL = "parallel"
## The orders will be executed parallelly
# The orders will be executed in parallel
# In each trading step, if users try to sell instruments first and buy new instruments with money, failure will
# occur
@@ -483,7 +537,7 @@ class SimulatorExecutor(BaseExecutor):
common_infra: CommonInfrastructure = None,
trade_type: str = TT_SERIAL,
**kwargs,
):
) -> None:
"""
Parameters
----------
@@ -517,7 +571,7 @@ class SimulatorExecutor(BaseExecutor):
List[Order]:
get a list orders according to `self.trade_type`
"""
orders = trade_decision.get_decision()
orders = _retrieve_orders_from_decision(trade_decision)
if self.trade_type == self.TT_SERIAL:
# Orders will be traded in a parallel way
@@ -525,15 +579,15 @@ class SimulatorExecutor(BaseExecutor):
elif self.trade_type == self.TT_PARAL:
# NOTE: !!!!!!!
# Assumption: there will not be orders in different trading direction in a single step of a strategy !!!!
# The parallel trading failure will be caused only by the confliction of money
# Therefore, make the buying go first will make sure the confliction happen.
# The parallel trading failure will be caused only by the conflicts of money
# Therefore, make the buying go first will make sure the conflicts happen.
# It equals to parallel trading after sorting the order by direction
order_it = sorted(orders, key=lambda order: -order.direction)
else:
raise NotImplementedError(f"This type of input is not supported")
return order_it
def _update_dealt_order_amount(self, order):
def _update_dealt_order_amount(self, order: Order) -> None:
"""update date and dealt order amount in the day."""
now_deal_day = self.trade_calendar.get_step_time()[0].floor(freq="D")
@@ -542,8 +596,7 @@ class SimulatorExecutor(BaseExecutor):
self.deal_day = now_deal_day
self.dealt_order_amount[order.stock_id] += order.deal_amount
def _collect_data(self, trade_decision: BaseTradeDecision, level: int = 0):
def _collect_data(self, trade_decision: BaseTradeDecision, level: int = 0) -> Tuple[List[object], dict]:
trade_start_time, _ = self.trade_calendar.get_step_time()
execute_result = []
@@ -559,7 +612,8 @@ class SimulatorExecutor(BaseExecutor):
self._update_dealt_order_amount(order)
if self.verbose:
print(
"[I {:%Y-%m-%d %H:%M:%S}]: {} {}, price {:.2f}, amount {}, deal_amount {}, factor {}, value {:.2f}, cash {:.2f}.".format(
"[I {:%Y-%m-%d %H:%M:%S}]: {} {}, price {:.2f}, amount {}, deal_amount {}, factor {}, "
"value {:.2f}, cash {:.2f}.".format(
trade_start_time,
"sell" if order.direction == Order.SELL else "buy",
order.stock_id,
@@ -569,6 +623,6 @@ class SimulatorExecutor(BaseExecutor):
order.factor,
trade_val,
self.trade_account.get_cash(),
)
),
)
return execute_result, {"trade_info": execute_result}

25
qlib/backtest/high_performance_ds.py
View File

@@ -1,20 +1,21 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from functools import lru_cache
import logging
from typing import List, Text, Union, Callable, Iterable, Dict
from collections import OrderedDict
import inspect
import pandas as pd
import numpy as np
import logging
from collections import OrderedDict
from functools import lru_cache
from typing import Callable, Dict, Iterable, List, Text, Union
import numpy as np
import pandas as pd
import qlib.utils.index_data as idd
from ..log import get_module_logger
from ..utils.index_data import IndexData, SingleData
from ..utils.resam import resam_ts_data, ts_data_last
from ..log import get_module_logger
from ..utils.time import is_single_value, Freq
import qlib.utils.index_data as idd
from ..utils.time import Freq, is_single_value
class BaseQuote:
@@ -627,7 +628,9 @@ class NumpyOrderIndicator(BaseOrderIndicator):
metrics = [metrics]
for metric in metrics:
order_indicator.data[metric] = idd.sum_by_index(
[indicator.data[metric] for indicator in indicators], stocks, fill_value
[indicator.data[metric] for indicator in indicators],
stocks,
fill_value,
)
def __repr__(self):

154
qlib/backtest/position.py
View File

@@ -2,24 +2,28 @@
# Licensed under the MIT License.
from datetime import timedelta
from typing import Dict, List, Union
import pandas as pd
from datetime import timedelta
import numpy as np
import pandas as pd
from .decision import Order
from ..data.data import D
from .decision import Order
class BasePosition:
"""
The Position want to maintain the position like a dictionary
The Position wants to maintain the position like a dictionary
Please refer to the `Position` class for the position
"""
def __init__(self, *args, cash=0.0, **kwargs):
def __init__(self, *args, cash: float = 0.0, **kwargs) -> None:
self._settle_type = self.ST_NO
self.position = {}
def fill_stock_value(self, start_time: Union[str, pd.Timestamp], freq: str, last_days: int = 30) -> None:
pass
def skip_update(self) -> bool:
"""
@@ -49,7 +53,7 @@ class BasePosition:
"""
raise NotImplementedError(f"Please implement the `check_stock` method")
def update_order(self, order: Order, trade_val: float, cost: float, trade_price: float):
def update_order(self, order: Order, trade_val: float, cost: float, trade_price: float) -> None:
"""
Parameters
----------
@@ -64,7 +68,7 @@ class BasePosition:
"""
raise NotImplementedError(f"Please implement the `update_order` method")
def update_stock_price(self, stock_id, price: float):
def update_stock_price(self, stock_id: str, price: float) -> None:
"""
Updating the latest price of the order
The useful when clearing balance at each bar end
@@ -89,6 +93,9 @@ class BasePosition:
"""
raise NotImplementedError(f"Please implement the `calculate_stock_value` method")
def calculate_value(self) -> float:
raise NotImplementedError(f"Please implement the `calculate_value` method")
def get_stock_list(self) -> List:
"""
Get the list of stocks in the position.
@@ -124,14 +131,16 @@ class BasePosition:
def get_cash(self, include_settle: bool = False) -> float:
"""
Parameters
----------
include_settle:
will the unsettled(delayed) cash included
Default: not include those unavailable cash
Returns
-------
float:
the available(tradable) cash in position
include_settle:
will the unsettled(delayed) cash included
Default: not include those unavailable cash
"""
raise NotImplementedError(f"Please implement the `get_cash` method")
@@ -165,7 +174,7 @@ class BasePosition:
"""
raise NotImplementedError(f"Please implement the `get_stock_weight_dict` method")
def add_count_all(self, bar):
def add_count_all(self, bar) -> None:
"""
Will be called at the end of each bar on each level
@@ -176,24 +185,19 @@ class BasePosition:
"""
raise NotImplementedError(f"Please implement the `add_count_all` method")
def update_weight_all(self):
def update_weight_all(self) -> None:
"""
Updating the position weight;
# TODO: this function is a little weird. The weight data in the position is in a wrong state after dealing order
# and before updating weight.
Parameters
----------
bar :
The level to be updated
"""
raise NotImplementedError(f"Please implement the `add_count_all` method")
ST_CASH = "cash"
ST_NO = None
def settle_start(self, settle_type: str):
def settle_start(self, settle_type: str) -> None:
"""
settlement start
It will act like start and commit a transaction
@@ -210,14 +214,9 @@ class BasePosition:
"""
raise NotImplementedError(f"Please implement the `settle_conf` method")
def settle_commit(self):
def settle_commit(self) -> None:
"""
settlement commit
Parameters
----------
settle_type : str
please refer to the documents of Executor
"""
raise NotImplementedError(f"Please implement the `settle_commit` method")
@@ -242,13 +241,11 @@ class Position(BasePosition):
}
"""
def __init__(self, cash: float = 0, position_dict: Dict[str, Dict[str, float]] = {}):
def __init__(self, cash: float = 0, position_dict: Dict[str, Union[Dict[str, float], float]] = {}) -> None:
"""Init position by cash and position_dict.
Parameters
----------
start_time :
the start time of backtest. It's for filling the initial value of stocks.
cash : float, optional
initial cash in account, by default 0
position_dict : Dict[
@@ -268,9 +265,9 @@ class Position(BasePosition):
# Otherwise the initial value
self.init_cash = cash
self.position = position_dict.copy()
for stock in self.position:
if isinstance(self.position[stock], int):
self.position[stock] = {"amount": self.position[stock]}
for stock, value in self.position.items():
if isinstance(value, int):
self.position[stock] = {"amount": value}
self.position["cash"] = cash
# If the stock price information is missing, the account value will not be calculated temporarily
@@ -279,21 +276,23 @@ class Position(BasePosition):
except KeyError:
pass
def fill_stock_value(self, start_time: Union[str, pd.Timestamp], freq: str, last_days: int = 30):
def fill_stock_value(self, start_time: Union[str, pd.Timestamp], freq: str, last_days: int = 30) -> None:
"""fill the stock value by the close price of latest last_days from qlib.
Parameters
----------
start_time :
the start time of backtest.
freq : str
Frequency
last_days : int, optional
the days to get the latest close price, by default 30.
"""
stock_list = []
for stock in self.position:
if not isinstance(self.position[stock], dict):
for stock, value in self.position.items():
if not isinstance(value, dict):
continue
if ("price" not in self.position[stock]) or (self.position[stock]["price"] is None):
if value.get("price", None) is None:
stock_list.append(stock)
if len(stock_list) == 0:
@@ -304,7 +303,12 @@ class Position(BasePosition):
price_end_time = start_time
price_start_time = start_time - timedelta(days=last_days)
price_df = D.features(
stock_list, ["$close"], price_start_time, price_end_time, freq=freq, disk_cache=True
stock_list,
["$close"],
price_start_time,
price_end_time,
freq=freq,
disk_cache=True,
).dropna()
price_dict = price_df.groupby(["instrument"]).tail(1).reset_index(level=1, drop=True)["$close"].to_dict()
@@ -316,7 +320,7 @@ class Position(BasePosition):
self.position[stock]["price"] = price_dict[stock]
self.position["now_account_value"] = self.calculate_value()
def _init_stock(self, stock_id, amount, price=None):
def _init_stock(self, stock_id: str, amount: float, price: float = None) -> None:
"""
initialization the stock in current position
@@ -334,7 +338,7 @@ class Position(BasePosition):
self.position[stock_id]["price"] = price
self.position[stock_id]["weight"] = 0 # update the weight in the end of the trade date
def _buy_stock(self, stock_id, trade_val, cost, trade_price):
def _buy_stock(self, stock_id: str, trade_val: float, cost: float, trade_price: float) -> None:
trade_amount = trade_val / trade_price
if stock_id not in self.position:
self._init_stock(stock_id=stock_id, amount=trade_amount, price=trade_price)
@@ -344,15 +348,16 @@ class Position(BasePosition):
self.position["cash"] -= trade_val + cost
def _sell_stock(self, stock_id, trade_val, cost, trade_price):
def _sell_stock(self, stock_id: str, trade_val: float, cost: float, trade_price: float) -> None:
trade_amount = trade_val / trade_price
if stock_id not in self.position:
raise KeyError("{} not in current position".format(stock_id))
else:
if np.isclose(self.position[stock_id]["amount"], trade_amount):
# Selling all the stocks
# we use np.isclose instead of abs(<the final amount>) <= 1e-5 because `np.isclose` consider both ralative amount and absolute amount
# Using abs(<the final amount>) <= 1e-5 will result in error when the amount is large
# we use np.isclose instead of abs(<the final amount>) <= 1e-5 because `np.isclose` consider both
# relative amount and absolute amount
# Using abs(<the final amount>) <= 1e-5 will result in error when the amount is large
self._del_stock(stock_id)
else:
# decrease the amount of stock
@@ -361,8 +366,10 @@ class Position(BasePosition):
if self.position[stock_id]["amount"] < -1e-5:
raise ValueError(
"only have {} {}, require {}".format(
self.position[stock_id]["amount"] + trade_amount, stock_id, trade_amount
)
self.position[stock_id]["amount"] + trade_amount,
stock_id,
trade_amount,
),
)
new_cash = trade_val - cost
@@ -373,13 +380,13 @@ class Position(BasePosition):
else:
raise NotImplementedError(f"This type of input is not supported")
def _del_stock(self, stock_id):
def _del_stock(self, stock_id: str) -> None:
del self.position[stock_id]
def check_stock(self, stock_id):
def check_stock(self, stock_id: str) -> bool:
return stock_id in self.position
def update_order(self, order, trade_val, cost, trade_price):
def update_order(self, order: Order, trade_val: float, cost: float, trade_price: float) -> None:
# handle order, order is a order class, defined in exchange.py
if order.direction == Order.BUY:
# BUY
@@ -390,54 +397,54 @@ class Position(BasePosition):
else:
raise NotImplementedError("do not support order direction {}".format(order.direction))
def update_stock_price(self, stock_id, price):
def update_stock_price(self, stock_id: str, price: float) -> None:
self.position[stock_id]["price"] = price
def update_stock_count(self, stock_id, bar, count):
def update_stock_count(self, stock_id: str, bar: str, count: float) -> None: # TODO: check type of `bar`
self.position[stock_id][f"count_{bar}"] = count
def update_stock_weight(self, stock_id, weight):
def update_stock_weight(self, stock_id: str, weight: float) -> None:
self.position[stock_id]["weight"] = weight
def calculate_stock_value(self):
def calculate_stock_value(self) -> float:
stock_list = self.get_stock_list()
value = 0
for stock_id in stock_list:
value += self.position[stock_id]["amount"] * self.position[stock_id]["price"]
return value
def calculate_value(self):
def calculate_value(self) -> float:
value = self.calculate_stock_value()
value += self.position["cash"] + self.position.get("cash_delay", 0.0)
return value
def get_stock_list(self):
def get_stock_list(self) -> List[str]:
stock_list = list(set(self.position.keys()) - {"cash", "now_account_value", "cash_delay"})
return stock_list
def get_stock_price(self, code):
def get_stock_price(self, code: str) -> float:
return self.position[code]["price"]
def get_stock_amount(self, code):
def get_stock_amount(self, code: str) -> float:
return self.position[code]["amount"] if code in self.position else 0
def get_stock_count(self, code, bar):
def get_stock_count(self, code: str, bar: str) -> float:
"""the days the account has been hold, it may be used in some special strategies"""
if f"count_{bar}" in self.position[code]:
return self.position[code][f"count_{bar}"]
else:
return 0
def get_stock_weight(self, code):
def get_stock_weight(self, code: str) -> float:
return self.position[code]["weight"]
def get_cash(self, include_settle=False):
def get_cash(self, include_settle: bool = False) -> float:
cash = self.position["cash"]
if include_settle:
cash += self.position.get("cash_delay", 0.0)
return cash
def get_stock_amount_dict(self):
def get_stock_amount_dict(self) -> dict:
"""generate stock amount dict {stock_id : amount of stock}"""
d = {}
stock_list = self.get_stock_list()
@@ -445,7 +452,7 @@ class Position(BasePosition):
d[stock_code] = self.get_stock_amount(code=stock_code)
return d
def get_stock_weight_dict(self, only_stock=False):
def get_stock_weight_dict(self, only_stock: bool = False) -> dict:
"""get_stock_weight_dict
generate stock weight dict {stock_id : value weight of stock in the position}
it is meaningful in the beginning or the end of each trade date
@@ -463,7 +470,7 @@ class Position(BasePosition):
d[stock_code] = self.position[stock_code]["amount"] * self.position[stock_code]["price"] / position_value
return d
def add_count_all(self, bar):
def add_count_all(self, bar: str) -> None:
stock_list = self.get_stock_list()
for code in stock_list:
if f"count_{bar}" in self.position[code]:
@@ -471,18 +478,18 @@ class Position(BasePosition):
else:
self.position[code][f"count_{bar}"] = 1
def update_weight_all(self):
def update_weight_all(self) -> None:
weight_dict = self.get_stock_weight_dict()
for stock_code, weight in weight_dict.items():
self.update_stock_weight(stock_code, weight)
def settle_start(self, settle_type):
def settle_start(self, settle_type: str) -> None:
assert self._settle_type == self.ST_NO, "Currently, settlement can't be nested!!!!!"
self._settle_type = settle_type
if settle_type == self.ST_CASH:
self.position["cash_delay"] = 0.0
def settle_commit(self):
def settle_commit(self) -> None:
if self._settle_type != self.ST_NO:
if self._settle_type == self.ST_CASH:
self.position["cash"] += self.position["cash_delay"]
@@ -507,10 +514,10 @@ class InfPosition(BasePosition):
# InfPosition always have any stocks
return True
def update_order(self, order: Order, trade_val: float, cost: float, trade_price: float):
def update_order(self, order: Order, trade_val: float, cost: float, trade_price: float) -> None:
pass
def update_stock_price(self, stock_id, price: float):
def update_stock_price(self, stock_id: str, price: float) -> None:
pass
def calculate_stock_value(self) -> float:
@@ -522,17 +529,20 @@ class InfPosition(BasePosition):
"""
return np.inf
def get_stock_list(self) -> List:
def calculate_value(self) -> float:
raise NotImplementedError(f"InfPosition doesn't support calculating value")
def get_stock_list(self) -> list:
raise NotImplementedError(f"InfPosition doesn't support stock list position")
def get_stock_price(self, code) -> float:
def get_stock_price(self, code: str) -> float:
"""the price of the inf position is meaningless"""
return np.nan
def get_stock_amount(self, code) -> float:
def get_stock_amount(self, code: str) -> float:
return np.inf
def get_cash(self, include_settle=False) -> float:
def get_cash(self, include_settle: bool = False) -> float:
return np.inf
def get_stock_amount_dict(self) -> Dict:
@@ -541,14 +551,14 @@ class InfPosition(BasePosition):
def get_stock_weight_dict(self, only_stock: bool = False) -> Dict:
raise NotImplementedError(f"InfPosition doesn't support get_stock_weight_dict")
def add_count_all(self, bar):
def add_count_all(self, bar: str) -> None:
raise NotImplementedError(f"InfPosition doesn't support add_count_all")
def update_weight_all(self):
def update_weight_all(self) -> None:
raise NotImplementedError(f"InfPosition doesn't support update_weight_all")
def settle_start(self, settle_type: str):
def settle_start(self, settle_type: str) -> None:
pass
def settle_commit(self):
def settle_commit(self) -> None:
pass

18
qlib/backtest/profit_attribution.py
View File

@@ -4,14 +4,16 @@
This module is not well maintained.
"""
import numpy as np
import pandas as pd
from .position import Position
from ..data import D
from ..config import C
import datetime
from pathlib import Path
import numpy as np
import pandas as pd
from ..config import C
from ..data import D
from .position import Position
def get_benchmark_weight(
bench,
@@ -214,7 +216,9 @@ def get_stock_group(stock_group_field_df, bench_stock_weight_df, group_method, g
for idx, row in (~bench_stock_weight_df.isna()).iterrows():
bench_values = stock_group_field_df.loc[idx, row[row].index]
new_stock_group_df.loc[idx] = get_daily_bin_group(
bench_values, stock_group_field_df.loc[idx], group_n=group_n
bench_values,
stock_group_field_df.loc[idx],
group_n=group_n,
)
return new_stock_group_df
@@ -315,7 +319,7 @@ def brinson_pa(
# The excess profit from the interaction of assets allocation and stocks selection
"RIN": Q4 - Q3 - Q2 + Q1,
"RTotal": Q4 - Q1, # The totoal excess profit
}
},
),
{
"port_group_ret": port_group_ret_df,

34
qlib/backtest/report.py
View File

@@ -2,19 +2,20 @@
# Licensed under the MIT License.
from collections import OrderedDict
import pathlib
from collections import OrderedDict
from typing import Dict, List, Tuple, Union
import numpy as np
import pandas as pd
from qlib.backtest.exchange import Exchange
import qlib.utils.index_data as idd
from qlib.backtest.decision import BaseTradeDecision, Order, OrderDir
from .high_performance_ds import BaseOrderIndicator, NumpyOrderIndicator, SingleMetric
from qlib.backtest.exchange import Exchange
from ..tests.config import CSI300_BENCH
from ..utils.resam import get_higher_eq_freq_feature, resam_ts_data
import qlib.utils.index_data as idd
from .high_performance_ds import BaseOrderIndicator, NumpyOrderIndicator, SingleMetric
class PortfolioMetrics:
@@ -161,7 +162,8 @@ class PortfolioMetrics:
stock_value,
]:
raise ValueError(
"None in [trade_start_time, account_value, cash, return_rate, total_turnover, turnover_rate, total_cost, cost_rate, stock_value]"
"None in [trade_start_time, account_value, cash, return_rate, total_turnover, turnover_rate, "
"total_cost, cost_rate, stock_value]",
)
if trade_end_time is None and bench_value is None:
@@ -335,7 +337,10 @@ class Indicator:
# sum inner order indicators with same metric.
all_metric = ["inner_amount", "deal_amount", "trade_price", "trade_value", "trade_cost", "trade_dir"]
self.order_indicator_cls.sum_all_indicators(
self.order_indicator, inner_order_indicators, all_metric, fill_value=0
self.order_indicator,
inner_order_indicators,
all_metric,
fill_value=0,
)
def func(trade_price, deal_amount):
@@ -378,12 +383,17 @@ class Indicator:
if decision.trade_range is not None:
trade_start_time, trade_end_time = decision.trade_range.clip_time_range(
start_time=trade_start_time, end_time=trade_end_time
start_time=trade_start_time,
end_time=trade_end_time,
)
if price == "deal_price":
price_s = trade_exchange.get_deal_price(
inst, trade_start_time, trade_end_time, direction=direction, method=None
inst,
trade_start_time,
trade_end_time,
direction=direction,
method=None,
)
else:
raise NotImplementedError(f"This type of input is not supported")
@@ -599,8 +609,12 @@ class Indicator:
if show_indicator:
print(
"[Indicator({}) {:%Y-%m-%d %H:%M:%S}]: FFR: {}, PA: {}, POS: {}".format(
freq, trade_start_time, fulfill_rate, price_advantage, positive_rate
)
freq,
trade_start_time,
fulfill_rate,
price_advantage,
positive_rate,
),
)
def get_order_indicator(self, raw: bool = True):

13
qlib/backtest/signal.py
View File

@@ -1,13 +1,16 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from qlib.utils import init_instance_by_config
import abc
from typing import Dict, List, Text, Tuple, Union
from ..model.base import BaseModel
import pandas as pd
from qlib.utils import init_instance_by_config
from ..data.dataset import Dataset
from ..data.dataset.utils import convert_index_format
from ..model.base import BaseModel
from ..utils.resam import resam_ts_data
import pandas as pd
import abc
class Signal(metaclass=abc.ABCMeta):
@@ -82,7 +85,7 @@ class ModelSignal(SignalWCache):
def create_signal_from(
obj: Union[Signal, Tuple[BaseModel, Dataset], List, Dict, Text, pd.Series, pd.DataFrame]
obj: Union[Signal, Tuple[BaseModel, Dataset], List, Dict, Text, pd.Series, pd.DataFrame],
) -> Signal:
"""
create signal from diverse information

107
qlib/backtest/utils.py
View File

@@ -2,16 +2,22 @@
# Licensed under the MIT License.
from __future__ import annotations
import bisect
from abc import abstractmethod
from typing import TYPE_CHECKING, Any, Set, Tuple, Union
import numpy as np
from qlib.utils.time import epsilon_change
from typing import TYPE_CHECKING, Tuple, Union
if TYPE_CHECKING:
from qlib.backtest.decision import BaseTradeDecision
import pandas as pd
import warnings
import pandas as pd
from ..data.data import Cal
@@ -26,8 +32,8 @@ class TradeCalendarManager:
freq: str,
start_time: Union[str, pd.Timestamp] = None,
end_time: Union[str, pd.Timestamp] = None,
level_infra: "LevelInfrastructure" = None,
):
level_infra: LevelInfrastructure = None,
) -> None:
"""
Parameters
----------
@@ -43,19 +49,26 @@ class TradeCalendarManager:
self.level_infra = level_infra
self.reset(freq=freq, start_time=start_time, end_time=end_time)
def reset(self, freq, start_time, end_time):
def reset(
self,
freq: str,
start_time: Union[str, pd.Timestamp] = None,
end_time: Union[str, pd.Timestamp] = None,
) -> None:
"""
Please refer to the docs of `__init__`
Reset the trade calendar
- self.trade_len : The total count for trading step
- self.trade_step : The number of trading step finished, self.trade_step can be [0, 1, 2, ..., self.trade_len - 1]
- self.trade_step : The number of trading step finished, self.trade_step can be
[0, 1, 2, ..., self.trade_len - 1]
"""
self.freq = freq
self.start_time = pd.Timestamp(start_time) if start_time else None
self.end_time = pd.Timestamp(end_time) if end_time else None
_calendar = Cal.calendar(freq=freq, future=True)
assert isinstance(_calendar, np.ndarray)
self._calendar = _calendar
_, _, _start_index, _end_index = Cal.locate_index(start_time, end_time, freq=freq, future=True)
self.start_index = _start_index
@@ -63,7 +76,7 @@ class TradeCalendarManager:
self.trade_len = _end_index - _start_index + 1
self.trade_step = 0
def finished(self):
def finished(self) -> bool:
"""
Check if the trading finished
- Should check before calling strategy.generate_decisions and executor.execute
@@ -72,29 +85,32 @@ class TradeCalendarManager:
"""
return self.trade_step >= self.trade_len
def step(self):
def step(self) -> None:
if self.finished():
raise RuntimeError(f"The calendar is finished, please reset it if you want to call it!")
self.trade_step = self.trade_step + 1
self.trade_step += 1
def get_freq(self):
def get_freq(self) -> str:
return self.freq
def get_trade_len(self):
def get_trade_len(self) -> int:
"""get the total step length"""
return self.trade_len
def get_trade_step(self):
def get_trade_step(self) -> int:
return self.trade_step
def get_step_time(self, trade_step=None, shift=0):
def get_step_time(self, trade_step: int = None, shift: int = 0) -> Tuple[pd.Timestamp, pd.Timestamp]:
"""
Get the left and right endpoints of the trade_step'th trading interval
About the endpoints:
- Qlib uses the closed interval in time-series data selection, which has the same performance as pandas.Series.loc
# - The returned right endpoints should minus 1 seconds because of the closed interval representation in Qlib.
# Note: Qlib supports up to minutely decision execution, so 1 seconds is less than any trading time interval.
- Qlib uses the closed interval in time-series data selection, which has the same performance as
pandas.Series.loc
# - The returned right endpoints should minus 1 seconds because of the closed interval representation in
# Qlib.
# Note: Qlib supports up to minutely decision execution, so 1 seconds is less than any trading time
# interval.
Parameters
----------
@@ -105,15 +121,14 @@ class TradeCalendarManager:
Returns
-------
Tuple[pd.Timestamp, pd.Timestap]
Tuple[pd.Timestamp, pd.Timestamp]
- If shift == 0, return the trading time range
- If shift > 0, return the trading time range of the earlier shift bars
- If shift < 0, return the trading time range of the later shift bar
"""
if trade_step is None:
trade_step = self.get_trade_step()
trade_step = trade_step - shift
calendar_index = self.start_index + trade_step
calendar_index = self.start_index + trade_step - shift
return self._calendar[calendar_index], epsilon_change(self._calendar[calendar_index + 1])
def get_data_cal_range(self, rtype: str = "full") -> Tuple[int, int]:
@@ -126,7 +141,7 @@ class TradeCalendarManager:
Parameters
----------
rtype: str
- "full": return the full limitation of the deicsion in the day
- "full": return the full limitation of the decision in the day
- "step": return the limitation of current step
Returns
@@ -148,7 +163,7 @@ class TradeCalendarManager:
return start_idx - day_start_idx, end_index - day_start_idx
def get_all_time(self):
def get_all_time(self) -> Tuple[pd.Timestamp, pd.Timestamp]:
"""Get the start_time and end_time for trading"""
return self.start_time, self.end_time
@@ -167,30 +182,33 @@ class TradeCalendarManager:
Tuple[int, int]:
the index of the range. **the left and right are closed**
"""
left, right = (
bisect.bisect_right(self._calendar, start_time) - 1,
bisect.bisect_right(self._calendar, end_time) - 1,
)
left = bisect.bisect_right(self._calendar, start_time) - 1
right = bisect.bisect_right(self._calendar, end_time) - 1
left -= self.start_index
right -= self.start_index
def clip(idx):
def clip(idx: int) -> int:
return min(max(0, idx), self.trade_len - 1)
return clip(left), clip(right)
def __repr__(self) -> str:
return f"class: {self.__class__.__name__}; {self.start_time}[{self.start_index}]~{self.end_time}[{self.end_index}]: [{self.trade_step}/{self.trade_len}]"
return (
f"class: {self.__class__.__name__}; "
f"{self.start_time}[{self.start_index}]~{self.end_time}[{self.end_index}]: "
f"[{self.trade_step}/{self.trade_len}]"
)
class BaseInfrastructure:
def __init__(self, **kwargs):
def __init__(self, **kwargs) -> None:
self.reset_infra(**kwargs)
def get_support_infra(self):
@abstractmethod
def get_support_infra(self) -> Set[str]:
raise NotImplementedError("`get_support_infra` is not implemented!")
def reset_infra(self, **kwargs):
def reset_infra(self, **kwargs) -> None:
support_infra = self.get_support_infra()
for k, v in kwargs.items():
if k in support_infra:
@@ -198,53 +216,58 @@ class BaseInfrastructure:
else:
warnings.warn(f"{k} is ignored in `reset_infra`!")
def get(self, infra_name):
def get(self, infra_name: str) -> Any:
if hasattr(self, infra_name):
return getattr(self, infra_name)
else:
warnings.warn(f"infra {infra_name} is not found!")
def has(self, infra_name):
def has(self, infra_name: str) -> bool:
return infra_name in self.get_support_infra() and hasattr(self, infra_name)
def update(self, other):
def update(self, other: BaseInfrastructure) -> None:
support_infra = other.get_support_infra()
infra_dict = {_infra: getattr(other, _infra) for _infra in support_infra if hasattr(other, _infra)}
self.reset_infra(**infra_dict)
class CommonInfrastructure(BaseInfrastructure):
def get_support_infra(self):
return ["trade_account", "trade_exchange"]
def get_support_infra(self) -> Set[str]:
return {"trade_account", "trade_exchange"}
class LevelInfrastructure(BaseInfrastructure):
"""level infrastructure is created by executor, and then shared to strategies on the same level"""
def get_support_infra(self):
def get_support_infra(self) -> Set[str]:
"""
Descriptions about the infrastructure
sub_level_infra:
- **NOTE**: this will only work after _init_sub_trading !!!
"""
return ["trade_calendar", "sub_level_infra", "common_infra"]
return {"trade_calendar", "sub_level_infra", "common_infra"}
def reset_cal(self, freq, start_time, end_time):
def reset_cal(
self,
freq: str,
start_time: Union[str, pd.Timestamp, None],
end_time: Union[str, pd.Timestamp, None],
) -> None:
"""reset trade calendar manager"""
if self.has("trade_calendar"):
self.get("trade_calendar").reset(freq, start_time=start_time, end_time=end_time)
else:
self.reset_infra(
trade_calendar=TradeCalendarManager(freq, start_time=start_time, end_time=end_time, level_infra=self)
trade_calendar=TradeCalendarManager(freq, start_time=start_time, end_time=end_time, level_infra=self),
)
def set_sub_level_infra(self, sub_level_infra: LevelInfrastructure):
"""this will make the calendar access easier when acrossing multi-levels"""
def set_sub_level_infra(self, sub_level_infra: LevelInfrastructure) -> None:
"""this will make the calendar access easier when crossing multi-levels"""
self.reset_infra(sub_level_infra=sub_level_infra)
def get_start_end_idx(trade_calendar: TradeCalendarManager, outer_trade_decision: BaseTradeDecision) -> Union[int, int]:
def get_start_end_idx(trade_calendar: TradeCalendarManager, outer_trade_decision: BaseTradeDecision) -> Tuple[int, int]:
"""
A helper function for getting the decision-level index range limitation for inner strategy
- NOTE: this function is not applicable to order-level

61
qlib/strategy/base.py
View File

@@ -1,17 +1,20 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import annotations
from typing import TYPE_CHECKING
from abc import abstractmethod
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from qlib.backtest.exchange import Exchange
from qlib.backtest.position import BasePosition
from typing import Tuple, Union
from ..backtest.decision import BaseTradeDecision
from ..backtest.utils import CommonInfrastructure, LevelInfrastructure, TradeCalendarManager
from ..rl.interpreter import ActionInterpreter, StateInterpreter
from ..utils import init_instance_by_config
from ..backtest.utils import CommonInfrastructure, LevelInfrastructure, TradeCalendarManager
from ..backtest.decision import BaseTradeDecision
__all__ = ["BaseStrategy", "RLStrategy", "RLIntStrategy"]
@@ -25,12 +28,13 @@ class BaseStrategy:
level_infra: LevelInfrastructure = None,
common_infra: CommonInfrastructure = None,
trade_exchange: Exchange = None,
):
) -> None:
"""
Parameters
----------
outer_trade_decision : BaseTradeDecision, optional
the trade decision of outer strategy which this strategy relies, and it will be traded in [start_time, end_time], by default None
the trade decision of outer strategy which this strategy relies, and it will be traded in
[start_time, end_time], by default None
- If the strategy is used to split trade decision, it will be used
- If the strategy is used for portfolio management, it can be ignored
level_infra : LevelInfrastructure, optional
@@ -41,9 +45,10 @@ class BaseStrategy:
trade_exchange : Exchange
exchange that provides market info, used to deal order and generate report
- If `trade_exchange` is None, self.trade_exchange will be set with common_infra
- It allowes different trade_exchanges is used in different executions.
- It allows different trade_exchanges is used in different executions.
- For example:
- In daily execution, both daily exchange and minutely are usable, but the daily exchange is recommended because it run faster.
- In daily execution, both daily exchange and minutely are usable, but the daily exchange is
recommended because it run faster.
- In minutely execution, the daily exchange is not usable, only the minutely exchange is recommended.
"""
@@ -63,13 +68,13 @@ class BaseStrategy:
"""get trade exchange in a prioritized order"""
return getattr(self, "_trade_exchange", None) or self.common_infra.get("trade_exchange")
def reset_level_infra(self, level_infra: LevelInfrastructure):
def reset_level_infra(self, level_infra: LevelInfrastructure) -> None:
if not hasattr(self, "level_infra"):
self.level_infra = level_infra
else:
self.level_infra.update(level_infra)
def reset_common_infra(self, common_infra: CommonInfrastructure):
def reset_common_infra(self, common_infra: CommonInfrastructure) -> None:
if not hasattr(self, "common_infra"):
self.common_infra: CommonInfrastructure = common_infra
else:
@@ -79,9 +84,9 @@ class BaseStrategy:
self,
level_infra: LevelInfrastructure = None,
common_infra: CommonInfrastructure = None,
outer_trade_decision=None,
**kwargs,
):
outer_trade_decision: BaseTradeDecision = None,
**kwargs, # TODO: remove this?
) -> None:
"""
- reset `level_infra`, used to reset trade calendar, .etc
- reset `common_infra`, used to reset `trade_account`, `trade_exchange`, .etc
@@ -89,18 +94,20 @@ class BaseStrategy:
**NOTE**:
split this function into `reset` and `_reset` will make following cases more convenient
1. Users want to initialize his strategy by overriding `reset`, but they don't want to affect the `_reset` called
when initialization
1. Users want to initialize his strategy by overriding `reset`, but they don't want to affect the `_reset`
called when initialization
"""
self._reset(
level_infra=level_infra, common_infra=common_infra, outer_trade_decision=outer_trade_decision, **kwargs
level_infra=level_infra,
common_infra=common_infra,
outer_trade_decision=outer_trade_decision,
)
def _reset(
self,
level_infra: LevelInfrastructure = None,
common_infra: CommonInfrastructure = None,
outer_trade_decision=None,
outer_trade_decision: BaseTradeDecision = None,
):
"""
Please refer to the docs of `reset`
@@ -114,7 +121,8 @@ class BaseStrategy:
if outer_trade_decision is not None:
self.outer_trade_decision = outer_trade_decision
def generate_trade_decision(self, execute_result=None):
@abstractmethod
def generate_trade_decision(self, execute_result: list = None) -> BaseTradeDecision:
"""Generate trade decision in each trading bar
Parameters
@@ -125,9 +133,11 @@ class BaseStrategy:
"""
raise NotImplementedError("generate_trade_decision is not implemented!")
@staticmethod
def update_trade_decision(
self, trade_decision: BaseTradeDecision, trade_calendar: TradeCalendarManager
) -> Union[BaseTradeDecision, None]:
trade_decision: BaseTradeDecision,
trade_calendar: TradeCalendarManager,
) -> Optional[BaseTradeDecision]:
"""
update trade decision in each step of inner execution, this method enable all order
@@ -145,7 +155,8 @@ class BaseStrategy:
# default to return None, which indicates that the trade decision is not changed
return None
def alter_outer_trade_decision(self, outer_trade_decision: BaseTradeDecision):
# FIXME: do not define this method as an abstract one since it is never implemented
def alter_outer_trade_decision(self, outer_trade_decision: BaseTradeDecision) -> BaseTradeDecision:
"""
A method for updating the outer_trade_decision.
The outer strategy may change its decision during updating.
@@ -154,6 +165,10 @@ class BaseStrategy:
----------
outer_trade_decision : BaseTradeDecision
the decision updated by the outer strategy
Returns
-------
BaseTradeDecision
"""
# default to reset the decision directly
# NOTE: normally, user should do something to the strategy due to the change of outer decision
@@ -200,7 +215,7 @@ class RLStrategy(BaseStrategy):
level_infra: LevelInfrastructure = None,
common_infra: CommonInfrastructure = None,
**kwargs,
):
) -> None:
"""
Parameters
----------
@@ -223,7 +238,7 @@ class RLIntStrategy(RLStrategy):
level_infra: LevelInfrastructure = None,
common_infra: CommonInfrastructure = None,
**kwargs,
):
) -> None:
"""
Parameters
----------
@@ -242,7 +257,7 @@ class RLIntStrategy(RLStrategy):
self.state_interpreter = init_instance_by_config(state_interpreter, accept_types=StateInterpreter)
self.action_interpreter = init_instance_by_config(action_interpreter, accept_types=ActionInterpreter)
def generate_trade_decision(self, execute_result=None):
def generate_trade_decision(self, execute_result: list = None) -> BaseTradeDecision:
_interpret_state = self.state_interpreter.interpret(execute_result=execute_result)
_action = self.policy.step(_interpret_state)
_trade_decision = self.action_interpreter.interpret(action=_action)

3
qlib/utils/__init__.py
View File

@@ -376,7 +376,7 @@ get_cls_kwargs = get_callable_kwargs # NOTE: this is for compatibility for the
def init_instance_by_config(
config: Union[str, dict, object, Path],
config: Union[str, dict, object, Path], # TODO: use a user-defined type to replace this Union.
default_module=None,
accept_types: Union[type, Tuple[type]] = (),
try_kwargs: Dict = {},
@@ -1063,4 +1063,5 @@ __all__ = [
"unpack_archive_with_buffer",
"get_tmp_file_with_buffer",
"set_log_with_config",
"init_instance_by_config",
]

13
tests/backtest/test_file_strategy.py
View File

@@ -2,7 +2,7 @@
# Licensed under the MIT License.
import unittest
from qlib.backtest import backtest, decision
from qlib.backtest import backtest
from qlib.tests import TestAutoData
import pandas as pd
from pathlib import Path
@@ -52,13 +52,12 @@ class FileStrTest(TestAutoData):
factor = df["$factor"].item()
price_unit = price / factor * 100
dealt_num_for_1000 = (account_money // price_unit) * (100 / factor)
print(price, factor, price_unit, dealt_num_for_1000)
# 2) generate orders
orders = self._gen_orders(dealt_num_for_1000)
print(orders)
orders.to_csv(self.EXAMPLE_FILE)
orders = pd.read_csv(self.EXAMPLE_FILE, index_col=["datetime", "instrument"])
print(orders)
# 3) run the strategy
strategy_config = {
@@ -101,7 +100,11 @@ class FileStrTest(TestAutoData):
},
},
}
report_dict, indicator_dict = backtest(executor=executor_config, strategy=strategy_config, **backtest_config)
report_dict, indicator_dict = backtest(
executor=executor_config,
strategy=strategy_config,
**backtest_config,
)
# ffr valid
ffr_dict = indicator_dict["1day"]["ffr"].to_dict()