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498 lines
16 KiB
Python
498 lines
16 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/this_close/worst/best)
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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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future_leak_warning: 是否使用了未来数据（this_close 模式）
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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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future_leak_warning: bool = False
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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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# 当信号指定了价格（止损/止盈/限价单），
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# 直接在信号所在 bar 以信号价格成交
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if signal.price is not None:
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exec_idx: int = bar_idx
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price: float = 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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"""
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# 检查 df 中的 datetime 列类型
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dt_col = self.df["datetime"]
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# 尝试直接比较（如果是 int 类型）
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try:
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idx = (dt_col == datetime_val).idxmax() if (dt_col == datetime_val).any() else None
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if idx is not None:
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return int(idx)
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except (TypeError, ValueError):
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pass
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# 如果是 datetime 对象，转为 int 比较
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if pd.api.types.is_datetime64_any_dtype(dt_col):
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dt_ints = dt_col.dt.strftime("%Y%m%d").astype(int)
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mask = dt_ints == datetime_val
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if mask.any():
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return int(mask.idxmax())
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return None
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return None
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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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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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if self.execution == "this_close":
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# 当信号 K 线收盘时成交
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self.future_leak_warning = True
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return bar_idx
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else:
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# 其他模式在下一根 K 线成交
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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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"""根据执行模式和方向获取成交价。
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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
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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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elif self.execution == "this_close":
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return float(row["close"])
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elif self.execution == "worst":
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# 买入取最高价，卖出取最低价
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return float(row["high"]) if direction == "BUY" else float(row["low"])
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elif self.execution == "best":
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# 买入取最低价，卖出取最高价
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return float(row["low"]) if direction == "BUY" else float(row["high"])
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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。"""
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if "volume" in self.df.columns and len(self.df) > 0:
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return float(self.df["volume"].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 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"],
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direction="BUY",
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size=original_size,
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price=price,
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commission=commission,
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slippage=slippage,
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pnl=0.0,
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rejected=True,
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)
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elif self.reject_policy == "reduce":
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# reduce 模式：重新计算可买数量
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available_cash = cash - self.min_commission - slippage
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if available_cash > price:
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reduced_size = int(available_cash / price / 100) * 100
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if reduced_size > 0:
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commission = self._calculate_commission(reduced_size, price, is_sell=False)
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slippage = self._compute_slippage(reduced_size, price, is_sell=False)
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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=reduced_size,
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price=price,
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commission=commission,
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slippage=slippage,
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pnl=0.0,
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rejected=False,
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)
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# 无法买任何数量
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return None
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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=size,
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price=price,
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commission=commission,
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slippage=slippage,
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pnl=0.0,
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rejected=False,
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)
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def _execute_sell(
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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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"""执行卖出。
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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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# 计算卖出数量
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size = self._calculate_sell_size(signal.size, position, position_mode)
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if size <= 0:
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# 无持仓
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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"],
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direction="SELL",
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size=0,
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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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# 检查持仓是否足够
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if size > position:
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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"],
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direction="SELL",
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size=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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# reduce 模式：减少到实际持仓
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size = position
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# 计算费用
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commission = self._calculate_commission(size, price, is_sell=True)
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slippage = self._compute_slippage(size, price, is_sell=True)
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return Trade(
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datetime=self.df.iloc[exec_idx]["datetime"],
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direction="SELL",
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size=size,
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price=price,
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commission=commission,
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slippage=slippage,
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pnl=0.0,
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rejected=False,
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)
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