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基准先行（scripts/bench_engine.py）：ma_cross 800 根全流程基线 93.6ms。 profile 打破预期：85% 墙钟在 OrderSimulator._find_bar_index（每信号全列 strftime），而非计划认为的逐 bar 信号循环。三处优化（行为逐位一致）： - Strategy 新增 entry_exit_masks() 显式钩子，19 个内置策略全实现；引擎按 掩码 + 候选事件 bar 状态机一次产出信号，持仓估算逐行复刻 _update_strategy_position（含买不足 1 手退化路径）；约束检测 _vectorize_eligibility 显式可测，signal_path=auto/vector/loop 可指定； 不满足约束（无钩子/缠论注入/掩码形状错）自动回退逐 bar - OrderSimulator._build_dt_lookup：O(信号×bar) 全列扫描 → 每 simulate 一次 O(bar) 查找表（重复日期取首个、未命中 None、object 恒不匹配语义对齐） - _datetime_to_int 去 strftime：year*10000+month*100+day 整数算术 （NaT→NaN 行为一致），_bind_data 与查找表共用 实测：ma_cross 全流程 93.6→7.4ms（×12.6），信号层 ×1.39~1.72（四策略）； 32 点网格寻优 ~3.0s→231ms。对拍 39 例：19 策略×参数变体×warmup/低资金/ 费率/缓存，performance/trades/equity/positions 逐位一致。 顺带记录：wr_reversal 默认阈值与 MyTT WR 刻度不匹配（策略恒不交易，既有问题未改）
545 lines
19 KiB
Python
545 lines
19 KiB
Python
"""订单撮合模拟器。
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将策略信号转换为成交记录，支持多种执行模式、仓位管理和拒绝策略。
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import TYPE_CHECKING
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import numpy as np
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import pandas as pd
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from easy_tdx.backtest.types import Signal, Trade
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if TYPE_CHECKING:
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from easy_tdx.backtest.slippage import SlippageModel
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@dataclass
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class OrderSimulator:
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"""订单撮合模拟器。
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将策略信号（Signal）转换为成交记录（Trade），支持多种执行模式、
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仓位管理和拒绝策略。
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Attributes:
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df: K线数据 DataFrame
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execution: 成交价规则 (next_open/next_close)
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position_mode: 仓位模式 (full/fixed/percent)
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reject_policy: 拒绝策略 (reduce/skip)
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commission: 佣金费率
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min_commission: 最低佣金
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stamp_tax: 印花税率（仅卖出）
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slippage: 滑点（每股）
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"""
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df: pd.DataFrame
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execution: str = "next_open"
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position_mode: str = "full"
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reject_policy: str = "reduce"
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commission: float = 0.0003
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min_commission: float = 5.0
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stamp_tax: float = 0.001
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slippage: float = 0.0
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slippage_model: SlippageModel | None = None
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def __post_init__(self) -> None:
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# datetime → 行号查找表（惰性构建一次）。v1.28 之前每个信号都对整列
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# datetime 做 strftime 扫描（O(信号数×bar 数)，占全流程 ~85% 墙钟），
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# 网格寻优每点都跑一遍 simulate，是实测最大瓶颈。
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self._dt_lookup: dict[int, int] | None = None
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def simulate(
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self,
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signals: list[Signal],
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cash: float,
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position: float,
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position_mode: str | None = None,
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) -> list[Trade]:
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"""模拟订单撮合过程。
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Args:
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signals: 交易信号列表
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cash: 初始现金
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position: 初始持仓（股数）
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position_mode: 仓位模式（覆盖初始化参数）
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Returns:
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成交记录列表
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"""
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if position_mode is None:
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position_mode = self.position_mode
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trades: list[Trade] = []
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current_cash = cash
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current_position = position
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for signal in signals:
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# 找到信号对应的 K 线
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bar_idx = self._find_bar_index(signal.datetime)
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if bar_idx is None:
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continue
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# 信号成交时点分三类：
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# - source="stop"（止损/止盈触发）：延迟到下一根开盘成交，消除"用当根
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# intrabar 触发价精确成交"的前视偏差；若下一根跳空，取对持仓者更不利的价。
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# - price is not None 且非 stop（限价单）：在信号 bar 当根以信号价成交。
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# - 其他（市价策略信号）：按 execution 配置（默认 next_open）在下一根成交。
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if signal.source == "stop":
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exec_idx_raw = self._resolve_exec_index(bar_idx)
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# 下一根不可用时（信号在最后一根 bar 触发），回退到当根收盘成交，
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# 避免止损信号被静默丢弃（审计 #4：不能因延迟成交而漏平仓）。
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next_price: float
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if exec_idx_raw is None or exec_idx_raw >= len(self.df):
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exec_idx = bar_idx
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row = self.df.iloc[bar_idx] if bar_idx < len(self.df) else None
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if row is None:
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continue
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next_price = float(row["close"])
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else:
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exec_idx = exec_idx_raw
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price_raw = self._get_price(exec_idx, signal.direction)
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if price_raw is None:
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continue
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next_price = price_raw
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# 跳空保护：对 SELL（平仓），若下一根开盘比触发价更不利（更低），
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# 取实际开盘价；否则按触发价（止损已生效）。
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trigger = signal.price if signal.price is not None else next_price
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if signal.direction == "SELL":
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price: float = min(next_price, trigger)
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else:
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price = max(next_price, trigger)
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elif signal.price is not None:
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# 限价单：在信号所在 bar 以信号价格成交
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exec_idx = bar_idx
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price = signal.price
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else:
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# 确定成交的 K 线索引
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exec_idx_raw = self._resolve_exec_index(bar_idx)
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if exec_idx_raw is None or exec_idx_raw >= len(self.df):
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continue
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exec_idx = exec_idx_raw
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# 获取成交价
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price_raw = self._get_price(exec_idx, signal.direction)
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if price_raw is None:
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continue
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price = price_raw
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# 执行交易
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if signal.direction == "BUY":
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trade = self._execute_buy(
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signal=signal,
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bar_idx=bar_idx,
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exec_idx=exec_idx,
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price=price,
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cash=current_cash,
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position=current_position,
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position_mode=position_mode,
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)
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if trade is not None:
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trades.append(trade)
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if not trade.rejected:
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current_cash -= trade.size * trade.price + trade.commission + trade.slippage
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current_position += trade.size
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elif signal.direction == "SELL":
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trade = self._execute_sell(
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signal=signal,
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bar_idx=bar_idx,
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exec_idx=exec_idx,
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price=price,
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cash=current_cash,
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position=current_position,
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position_mode=position_mode,
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)
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if trade is not None:
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trades.append(trade)
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if not trade.rejected:
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current_cash += trade.size * trade.price - trade.commission - trade.slippage
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current_position -= trade.size
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return trades
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def _find_bar_index(self, datetime_val: int) -> int | None:
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"""查找 datetime 对应的 K 线索引。
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Args:
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datetime_val: 信号时间（int 格式 YYYYMMDD）
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Returns:
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K 线索引，未找到返回 None
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性能（v1.28）：查找表在首次调用时构建一次（O(bar 数)），此后每个
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信号 O(1) 查询；语义与逐信号全列扫描完全一致——重复日期取首个匹配
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（等价于原来的 ``mask.argmax()``），未命中返回 None。
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"""
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if self._dt_lookup is None:
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self._dt_lookup = self._build_dt_lookup()
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return self._dt_lookup.get(int(datetime_val))
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def _build_dt_lookup(self) -> dict[int, int]:
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"""构建 datetime → 行号查找表（重复日期保留首个出现）。
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与历史行为的对应关系：
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- datetime64 列：向量化转 YYYYMMDD int 后建表（原来每个信号都
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``strftime`` 全列扫描一遍，是 O(信号数×bar 数) 的热点）；
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- 数值列（int/float）：整数值等价于原 ``dt_col == datetime_val`` 的
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直接比较（非整数 float 不会命中，与原来一致）；
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- 其他列（object 等）：原来直接比较恒不命中、返回 None——这里同样
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产出空表（保持行为不变，包括 object-Timestamp 列不匹配的既有行为）。
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"""
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from easy_tdx.backtest.strategy import _datetime_to_int
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dt_col = self.df["datetime"]
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lookup: dict[int, int] = {}
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if pd.api.types.is_datetime64_any_dtype(dt_col):
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ints = _datetime_to_int(dt_col.to_numpy())
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for i, v in enumerate(ints):
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if v == v: # NaT → NaN，跳过（原 strftime 同样不产出该行）
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lookup.setdefault(int(v), i)
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return lookup
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arr = dt_col.to_numpy()
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if arr.dtype.kind in "iuf":
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for i, v in enumerate(arr):
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# 浮点列只有整数值（20240104.0）才可能与 int 信号相等，
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# 与原 == 比较语义一致
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if float(v).is_integer():
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lookup.setdefault(int(v), i)
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return lookup
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# object / 字符串等：原实现 (dt_col == int) 恒为 False → 恒 None
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return lookup
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def _resolve_exec_index(self, bar_idx: int) -> int | None:
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"""根据执行模式确定成交的 K 线索引。
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开盘价 / 收盘价模式均在信号后一根 K 线成交（next_open 取次根开盘价，
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next_close 取次根收盘价），避免使用信号当根的未完成/未来数据。
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Args:
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bar_idx: 信号对应的 K 线索引
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Returns:
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成交 K 线索引
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"""
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return bar_idx + 1
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def _get_price(self, exec_idx: int, direction: str) -> float | None:
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"""根据执行模式获取成交价。
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Args:
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exec_idx: 成交 K 线索引
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direction: 交易方向（仅作保留，当前两种模式均不依赖方向）
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Returns:
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成交价格
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"""
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if exec_idx >= len(self.df):
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return None
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row = self.df.iloc[exec_idx]
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if self.execution == "next_open":
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return float(row["open"])
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elif self.execution == "next_close":
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return float(row["close"])
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else:
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return None
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def _calculate_buy_size(
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self,
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signal_size: float,
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price: float,
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cash: float,
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position_mode: str,
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) -> float:
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"""计算买入数量。
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Args:
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signal_size: 信号指定的数量
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price: 成交价格
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cash: 可用现金
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position_mode: 仓位模式
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Returns:
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买入数量（股）
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"""
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if position_mode == "full" or signal_size == 0:
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# 全仓：计算可用现金能买多少（100股整手）
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# 先计算最大股数，然后向下取整到100的倍数
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max_cost_per_share = price * (1 + self.commission) + self.slippage
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max_shares_raw = cash / max_cost_per_share
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max_shares = int(max_shares_raw / 100) * 100
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return float(max_shares)
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elif position_mode == "fixed":
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# 固定股数
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return signal_size
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elif position_mode == "percent":
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# 总资产的百分比
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total_value = cash # 简化：假设现金=总资产
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target_value = total_value * signal_size
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max_shares = int(target_value / price / 100) * 100
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return float(max_shares)
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else:
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return signal_size
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def _calculate_sell_size(
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self,
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signal_size: float,
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position: float,
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position_mode: str,
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) -> float:
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"""计算卖出数量。
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Args:
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signal_size: 信号指定的数量
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position: 当前持仓
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position_mode: 仓位模式
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Returns:
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卖出数量（股）
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"""
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if position_mode == "full" or signal_size == 0:
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# 全部卖出
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return position
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elif position_mode == "fixed":
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# 固定股数
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return signal_size
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elif position_mode == "percent":
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# 持仓的百分比
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return position * signal_size
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else:
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return signal_size
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def _calculate_commission(self, size: float, price: float, is_sell: bool = False) -> float:
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"""计算手续费。
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Args:
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size: 成交数量
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price: 成交价格
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is_sell: 是否为卖出
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Returns:
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手续费总额
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"""
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# 佣金
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commission = max(size * price * self.commission, self.min_commission)
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# 印花税（仅卖出）
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if is_sell:
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stamp = size * price * self.stamp_tax
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commission += stamp
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return commission
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def _compute_slippage(self, size: float, price: float, is_sell: bool) -> float:
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"""计算滑点成本。"""
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if self.slippage_model is not None:
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volume = self._get_current_volume()
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volatility = self._estimate_volatility()
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return self.slippage_model.compute(
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price=price,
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size=size,
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volume=volume,
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volatility=volatility,
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direction="SELL" if is_sell else "BUY",
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)
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return size * self.slippage
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def _get_current_volume(self) -> float:
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"""获取最后一根K线的成交量，兼容 vol/volume 列名。"""
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if len(self.df) == 0:
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return 0.0
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for col in ("vol", "volume"):
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if col in self.df.columns:
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return float(self.df[col].iloc[-1])
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return 0.0
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def _estimate_volatility(self) -> float:
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"""从收盘价估计年化波动率。"""
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if "close" not in self.df.columns or len(self.df) < 2:
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return 0.0
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close = self.df["close"].to_numpy()
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returns = np.diff(close) / close[:-1]
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if len(returns) < 2:
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return 0.0
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daily_vol = float(np.std(returns))
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return float(daily_vol * np.sqrt(252))
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def _execute_buy(
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self,
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signal: Signal,
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bar_idx: int,
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exec_idx: int,
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price: float,
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cash: float,
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position: float,
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position_mode: str,
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) -> Trade | None:
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"""执行买入。
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Args:
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signal: 交易信号
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bar_idx: 信号 K 线索引
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exec_idx: 成交 K 线索引
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price: 成交价格
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cash: 可用现金
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position: 当前持仓
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position_mode: 仓位模式
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Returns:
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成交记录
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"""
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# 保存原始信号数量
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original_size = signal.size
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# 计算买入数量
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size = self._calculate_buy_size(signal.size, price, cash, position_mode)
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if size <= 0:
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# 资金不足或计算结果为0
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if self.reject_policy == "skip":
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# 对于 percent 模式，original_size 是百分比（如 0.5），不是股数
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# 对于 fixed/full 模式，original_size 就是股数
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display_size = original_size if position_mode == "fixed" else 100
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return Trade(
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datetime=self.df.iloc[exec_idx]["datetime"],
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direction="BUY",
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size=display_size,
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price=price,
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commission=0.0,
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slippage=0.0,
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pnl=0.0,
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rejected=True,
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)
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return None
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# 计算费用
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commission = self._calculate_commission(size, price, is_sell=False)
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slippage = self._compute_slippage(size, price, is_sell=False)
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# 检查资金是否足够
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total_cost = size * price + commission + slippage
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if total_cost > cash:
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if self.reject_policy == "skip":
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return Trade(
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datetime=self.df.iloc[exec_idx]["datetime"],
|
||
direction="BUY",
|
||
size=original_size,
|
||
price=price,
|
||
commission=commission,
|
||
slippage=slippage,
|
||
pnl=0.0,
|
||
rejected=True,
|
||
)
|
||
elif self.reject_policy == "reduce":
|
||
# reduce 模式：重新计算可买数量
|
||
available_cash = cash - self.min_commission - slippage
|
||
if available_cash > price:
|
||
reduced_size = int(available_cash / price / 100) * 100
|
||
if reduced_size > 0:
|
||
commission = self._calculate_commission(reduced_size, price, is_sell=False)
|
||
slippage = self._compute_slippage(reduced_size, price, is_sell=False)
|
||
return Trade(
|
||
datetime=self.df.iloc[exec_idx]["datetime"],
|
||
direction="BUY",
|
||
size=reduced_size,
|
||
price=price,
|
||
commission=commission,
|
||
slippage=slippage,
|
||
pnl=0.0,
|
||
rejected=False,
|
||
)
|
||
# 无法买任何数量
|
||
return None
|
||
|
||
return Trade(
|
||
datetime=self.df.iloc[exec_idx]["datetime"],
|
||
direction="BUY",
|
||
size=size,
|
||
price=price,
|
||
commission=commission,
|
||
slippage=slippage,
|
||
pnl=0.0,
|
||
rejected=False,
|
||
)
|
||
|
||
def _execute_sell(
|
||
self,
|
||
signal: Signal,
|
||
bar_idx: int,
|
||
exec_idx: int,
|
||
price: float,
|
||
cash: float,
|
||
position: float,
|
||
position_mode: str,
|
||
) -> Trade | None:
|
||
"""执行卖出。
|
||
|
||
Args:
|
||
signal: 交易信号
|
||
bar_idx: 信号 K 线索引
|
||
exec_idx: 成交 K 线索引
|
||
price: 成交价格
|
||
cash: 可用现金
|
||
position: 当前持仓
|
||
position_mode: 仓位模式
|
||
|
||
Returns:
|
||
成交记录
|
||
"""
|
||
# 计算卖出数量
|
||
size = self._calculate_sell_size(signal.size, position, position_mode)
|
||
|
||
if size <= 0:
|
||
# 无持仓
|
||
if self.reject_policy == "skip":
|
||
return Trade(
|
||
datetime=self.df.iloc[exec_idx]["datetime"],
|
||
direction="SELL",
|
||
size=0,
|
||
price=price,
|
||
commission=0.0,
|
||
slippage=0.0,
|
||
pnl=0.0,
|
||
rejected=True,
|
||
)
|
||
return None
|
||
|
||
# 检查持仓是否足够
|
||
if size > position:
|
||
if self.reject_policy == "skip":
|
||
return Trade(
|
||
datetime=self.df.iloc[exec_idx]["datetime"],
|
||
direction="SELL",
|
||
size=size,
|
||
price=price,
|
||
commission=0.0,
|
||
slippage=0.0,
|
||
pnl=0.0,
|
||
rejected=True,
|
||
)
|
||
# reduce 模式：减少到实际持仓
|
||
size = position
|
||
|
||
# 计算费用
|
||
commission = self._calculate_commission(size, price, is_sell=True)
|
||
slippage = self._compute_slippage(size, price, is_sell=True)
|
||
|
||
return Trade(
|
||
datetime=self.df.iloc[exec_idx]["datetime"],
|
||
direction="SELL",
|
||
size=size,
|
||
price=price,
|
||
commission=commission,
|
||
slippage=slippage,
|
||
pnl=0.0,
|
||
rejected=False,
|
||
)
|