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https://github.com/microsoft/qlib.git
synced 2026-07-03 19:10:58 +08:00
Refactor file structure & renaming functions
This commit is contained in:
Huoran Li
@@ -1,65 +0,0 @@
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from typing import List, Optional, Tuple
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from qlib.backtest.decision import BaseTradeDecision, Order, OrderHelper, TradeDecisionWO, TradeRange
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from qlib.backtest.utils import CommonInfrastructure
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from qlib.strategy.base import BaseStrategy
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class DecomposedStrategy(BaseStrategy):
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def __init__(self) -> None:
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super(DecomposedStrategy, self).__init__()
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self.execute_order: Optional[Order] = None
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self.execute_result: List[Tuple[Order, float, float, float]] = []
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def generate_trade_decision(self, execute_result: list = None) -> BaseTradeDecision:
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exec_vol = yield self
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oh = self.trade_exchange.get_order_helper()
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order = oh.create(self._order.stock_id, exec_vol, self._order.direction)
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self.execute_order = order
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return TradeDecisionWO([order], self)
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def alter_outer_trade_decision(self, outer_trade_decision: BaseTradeDecision) -> BaseTradeDecision:
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return outer_trade_decision
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def receive_execute_result(self, execute_result: list) -> None:
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self.execute_result = execute_result
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def reset(self, outer_trade_decision: TradeDecisionWO = None, **kwargs) -> None:
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super().reset(outer_trade_decision=outer_trade_decision, **kwargs)
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if outer_trade_decision is not None:
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order_list = outer_trade_decision.order_list
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assert len(order_list) == 1
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self._order = order_list[0]
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class SingleOrderStrategy(BaseStrategy):
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# this logic is copied from FileOrderStrategy
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def __init__(
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self,
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common_infra: CommonInfrastructure,
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order: Order,
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trade_range: TradeRange,
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instrument: str,
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) -> None:
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super().__init__(common_infra=common_infra)
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self._order = order
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self._trade_range = trade_range
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self._instrument = instrument
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def alter_outer_trade_decision(self, outer_trade_decision: BaseTradeDecision) -> BaseTradeDecision:
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return outer_trade_decision
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def generate_trade_decision(self, execute_result: list = None) -> TradeDecisionWO:
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oh: OrderHelper = self.common_infra.get("trade_exchange").get_order_helper()
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order_list = [
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oh.create(
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code=self._instrument,
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amount=self._order.amount,
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direction=Order.parse_dir(self._order.direction),
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)
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]
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return TradeDecisionWO(order_list, self, self._trade_range)
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@@ -4,116 +4,83 @@
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"""Placeholder for qlib-based simulator."""
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from __future__ import annotations
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from typing import Any, Callable, Generator, List, Optional, cast
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from typing import Callable, Generator, List, Optional, Tuple, cast
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import numpy as np
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import pandas as pd
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from qlib.rl.order_execution.from_neutrader.feature import init_qlib
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from qlib.backtest import get_exchange
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from qlib.backtest.account import Account
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from qlib.backtest.decision import Order, OrderDir, TradeRangeByTime
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from qlib.backtest.decision import BaseTradeDecision, Order, OrderHelper, TradeDecisionWO, TradeRange, TradeRangeByTime
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from qlib.backtest.executor import BaseExecutor, NestedExecutor
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from qlib.backtest.utils import CommonInfrastructure
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from qlib.config import QlibConfig
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from qlib.constant import EPS
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from qlib.rl.order_execution.from_neutrader.config import ExchangeConfig
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from qlib.rl.order_execution.from_neutrader.strategy import DecomposedStrategy, SingleOrderStrategy
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from qlib.rl.order_execution.simulator_simple import ONE_SEC, SAOEMetrics, SAOEState, _float_or_ndarray
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from qlib.rl.order_execution.from_neutrader.feature import init_qlib
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from qlib.rl.order_execution.simulator_simple import SAOEMetrics, SAOEState
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from qlib.rl.order_execution.utils import (_convert_tick_str_to_int, _dataframe_append, _get_common_infra, _get_minutes,
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_get_ticks_slice, _price_advantage)
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from qlib.rl.simulator import Simulator
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from qlib.strategy.base import BaseStrategy
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def get_common_infra(
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config: ExchangeConfig,
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trade_date: pd.Timestamp,
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codes: List[str],
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cash_limit: Optional[float] = None,
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) -> CommonInfrastructure:
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# need to specify a range here for acceleration
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if cash_limit is None:
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trade_account = Account(init_cash=int(1e12), benchmark_config={}, pos_type="InfPosition")
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else:
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trade_account = Account(
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init_cash=cash_limit,
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benchmark_config={},
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pos_type="Position",
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position_dict={code: {"amount": 1e12, "price": 1.0} for code in codes},
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)
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class DecomposedStrategy(BaseStrategy):
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def __init__(self) -> None:
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super(DecomposedStrategy, self).__init__()
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exchange = get_exchange(
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codes=codes,
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freq="1min",
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limit_threshold=config.limit_threshold,
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deal_price=config.deal_price,
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open_cost=config.open_cost,
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close_cost=config.close_cost,
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min_cost=config.min_cost if config.trade_unit is not None else 0,
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start_time=trade_date,
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end_time=trade_date + pd.DateOffset(1),
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trade_unit=config.trade_unit,
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volume_threshold=config.volume_threshold,
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)
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self.execute_order: Optional[Order] = None
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self.execute_result: List[Tuple[Order, float, float, float]] = []
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return CommonInfrastructure(trade_account=trade_account, trade_exchange=exchange)
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def generate_trade_decision(self, execute_result: list = None) -> BaseTradeDecision:
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exec_vol = yield self
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oh = self.trade_exchange.get_order_helper()
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order = oh.create(self._order.stock_id, exec_vol, self._order.direction)
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self.execute_order = order
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return TradeDecisionWO([order], self)
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def alter_outer_trade_decision(self, outer_trade_decision: BaseTradeDecision) -> BaseTradeDecision:
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return outer_trade_decision
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def receive_execute_result(self, execute_result: list) -> None:
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self.execute_result = execute_result
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def reset(self, outer_trade_decision: TradeDecisionWO = None, **kwargs) -> None:
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super().reset(outer_trade_decision=outer_trade_decision, **kwargs)
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if outer_trade_decision is not None:
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order_list = outer_trade_decision.order_list
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assert len(order_list) == 1
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self._order = order_list[0]
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def _convert_tick_str_to_int(time_per_step: str) -> int:
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d = {
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"30min": 30,
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}
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return d[time_per_step]
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class SingleOrderStrategy(BaseStrategy):
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# this logic is copied from FileOrderStrategy
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def __init__(
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self,
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common_infra: CommonInfrastructure,
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order: Order,
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trade_range: TradeRange,
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instrument: str,
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) -> None:
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super().__init__(common_infra=common_infra)
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self._order = order
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self._trade_range = trade_range
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self._instrument = instrument
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def alter_outer_trade_decision(self, outer_trade_decision: BaseTradeDecision) -> BaseTradeDecision:
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return outer_trade_decision
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def _get_ticks_slice(
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ticks_index: pd.DatetimeIndex,
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start: pd.Timestamp,
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end: pd.Timestamp,
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include_end: bool = False,
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) -> pd.DatetimeIndex:
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if not include_end:
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end = end - ONE_SEC
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return ticks_index[ticks_index.slice_indexer(start, end)]
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def _get_minutes(start_time: pd.Timestamp, end_time: pd.Timestamp) -> List[pd.Timestamp]:
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minutes = []
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t = start_time
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while t <= end_time:
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minutes.append(t)
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t += pd.Timedelta("1min")
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return minutes
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def _dataframe_append(df: pd.DataFrame, other: Any) -> pd.DataFrame:
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# dataframe.append is deprecated
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other_df = pd.DataFrame(other).set_index("datetime")
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other_df.index.name = "datetime"
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res = pd.concat([df, other_df], axis=0)
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return res
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def _price_advantage(
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exec_price: _float_or_ndarray,
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baseline_price: float,
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direction: OrderDir | int,
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) -> _float_or_ndarray:
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if baseline_price == 0: # something is wrong with data. Should be nan here
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if isinstance(exec_price, float):
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return 0.0
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else:
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return np.zeros_like(exec_price)
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if direction == OrderDir.BUY:
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res = (1 - exec_price / baseline_price) * 10000
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elif direction == OrderDir.SELL:
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res = (exec_price / baseline_price - 1) * 10000
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else:
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raise ValueError(f"Unexpected order direction: {direction}")
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res_wo_nan: np.ndarray = np.nan_to_num(res, nan=0.0)
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if res_wo_nan.size == 1:
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return res_wo_nan.item()
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else:
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return cast(_float_or_ndarray, res_wo_nan)
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def generate_trade_decision(self, execute_result: list = None) -> TradeDecisionWO:
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oh: OrderHelper = self.common_infra.get("trade_exchange").get_order_helper()
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order_list = [
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oh.create(
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code=self._instrument,
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amount=self._order.amount,
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direction=Order.parse_dir(self._order.direction),
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)
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]
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return TradeDecisionWO(order_list, self, self._trade_range)
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class StateMaintainer:
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@@ -264,7 +231,7 @@ class QlibSimulator(Simulator[Order, SAOEState, float]):
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init_qlib(self._qlib_config, instrument)
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common_infra = get_common_infra(
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common_infra = _get_common_infra(
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self._exchange_config,
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trade_date=pd.Timestamp(self._order_date),
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codes=[instrument],
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104
qlib/rl/order_execution/utils.py
Normal file
104
qlib/rl/order_execution/utils.py
Normal file
@@ -0,0 +1,104 @@
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from __future__ import annotations
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from typing import Any, List, Optional, cast
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import numpy as np
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import pandas as pd
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from qlib.backtest import Account, CommonInfrastructure, get_exchange
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from qlib.backtest.decision import OrderDir
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from qlib.rl.order_execution.from_neutrader.config import ExchangeConfig
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from qlib.rl.order_execution.simulator_simple import ONE_SEC, _float_or_ndarray
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def _get_common_infra(
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config: ExchangeConfig,
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trade_date: pd.Timestamp,
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codes: List[str],
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cash_limit: Optional[float] = None,
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) -> CommonInfrastructure:
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# need to specify a range here for acceleration
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if cash_limit is None:
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trade_account = Account(init_cash=int(1e12), benchmark_config={}, pos_type="InfPosition")
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else:
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trade_account = Account(
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init_cash=cash_limit,
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benchmark_config={},
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pos_type="Position",
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position_dict={code: {"amount": 1e12, "price": 1.0} for code in codes},
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)
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exchange = get_exchange(
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codes=codes,
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freq="1min",
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limit_threshold=config.limit_threshold,
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deal_price=config.deal_price,
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open_cost=config.open_cost,
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close_cost=config.close_cost,
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min_cost=config.min_cost if config.trade_unit is not None else 0,
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start_time=trade_date,
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end_time=trade_date + pd.DateOffset(1),
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trade_unit=config.trade_unit,
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volume_threshold=config.volume_threshold,
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)
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return CommonInfrastructure(trade_account=trade_account, trade_exchange=exchange)
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def _convert_tick_str_to_int(time_per_step: str) -> int:
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d = {
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"30min": 30,
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}
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return d[time_per_step]
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def _get_ticks_slice(
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ticks_index: pd.DatetimeIndex,
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start: pd.Timestamp,
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end: pd.Timestamp,
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include_end: bool = False,
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) -> pd.DatetimeIndex:
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if not include_end:
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end = end - ONE_SEC
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return ticks_index[ticks_index.slice_indexer(start, end)]
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def _get_minutes(start_time: pd.Timestamp, end_time: pd.Timestamp) -> List[pd.Timestamp]:
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minutes = []
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t = start_time
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while t <= end_time:
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minutes.append(t)
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t += pd.Timedelta("1min")
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return minutes
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def _dataframe_append(df: pd.DataFrame, other: Any) -> pd.DataFrame:
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# dataframe.append is deprecated
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other_df = pd.DataFrame(other).set_index("datetime")
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other_df.index.name = "datetime"
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res = pd.concat([df, other_df], axis=0)
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return res
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def _price_advantage(
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exec_price: _float_or_ndarray,
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baseline_price: float,
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direction: OrderDir | int,
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) -> _float_or_ndarray:
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if baseline_price == 0: # something is wrong with data. Should be nan here
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if isinstance(exec_price, float):
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return 0.0
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else:
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return np.zeros_like(exec_price)
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if direction == OrderDir.BUY:
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res = (1 - exec_price / baseline_price) * 10000
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elif direction == OrderDir.SELL:
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res = (exec_price / baseline_price - 1) * 10000
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else:
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raise ValueError(f"Unexpected order direction: {direction}")
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res_wo_nan: np.ndarray = np.nan_to_num(res, nan=0.0)
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if res_wo_nan.size == 1:
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return res_wo_nan.item()
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else:
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return cast(_float_or_ndarray, res_wo_nan)
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