简单波动率策略，实现+网格搜索
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@@ -1,5 +1,5 @@
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# src/execution_simulator.py (修改部分)
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from datetime import datetime
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from typing import Dict, List, Optional
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import pandas as pd
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from .core_data import Order, Trade, Bar, PortfolioSnapshot
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@@ -36,35 +36,111 @@ class ExecutionSimulator:
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self.commission_rate = commission_rate
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self.trade_log: List[Trade] = [] # 存储所有成交记录
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self.pending_orders: Dict[str, Order] = {} # {order_id: Order_object}
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self._current_time = None
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print(
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f"模拟器初始化：初始资金={self.initial_capital:.2f}, 滑点率={self.slippage_rate}, 佣金率={self.commission_rate}")
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if self.positions:
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print(f"初始持仓：{self.positions}")
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def update_time(self, current_time: datetime):
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"""
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更新模拟器的当前时间。
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这个方法由 BacktestEngine 在遍历 K 线时调用。
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"""
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self._current_time = current_time
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# --- 新增的公共方法 ---
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def get_current_time(self) -> datetime:
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"""
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获取模拟器的当前时间。
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"""
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if self._current_time is None:
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# 可以在这里抛出错误或者返回一个默认值，取决于你对未初始化时间的处理
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# 抛出错误可以帮助你发现问题，例如在模拟器时间未设置时就尝试获取
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# raise RuntimeError("Simulator time has not been set. Ensure update_time is called.")
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return None
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return self._current_time
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def _calculate_fill_price(self, order: Order, current_bar: Bar) -> float:
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"""
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内部方法：根据订单类型和滑点计算实际成交价格。
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简化处理：市价单以当前Bar收盘价为基准，考虑滑点。
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- 市价单通常以当前K线的开盘价成交（考虑滑点）。
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- 限价单判断是否触及限价，如果触及，以限价成交（考虑滑点）。
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"""
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base_price = current_bar.close # 简化为收盘价成交
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fill_price = -1.0 # 默认未成交
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# 考虑滑点
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if order.direction in ["BUY", "CLOSE_SHORT"]: # 买入或平空，价格向上偏离
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fill_price = base_price * (1 + self.slippage_rate)
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elif order.direction in ["SELL", "CLOSE_LONG"]: # 卖出或平多，价格向下偏离
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fill_price = base_price * (1 - self.slippage_rate)
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else: # 默认情况，无滑点
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fill_price = base_price
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if order.price_type == "MARKET":
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# 市价单通常以开盘价成交，或者根据你的策略需求选择收盘价
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# 这里我们仍然使用开盘价作为市价单的基准成交价
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base_price = current_bar.open
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# 如果是限价单且成交价格不满足条件，则可能不成交
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if order.price_type == "LIMIT" and order.limit_price is not None:
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# 对于BUY和CLOSE_SHORT，成交价必须 <= 限价
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if (order.direction == "BUY" or order.direction == "CLOSE_SHORT") and fill_price > order.limit_price:
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return -1.0 # 未触及限价
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# 对于SELL和CLOSE_LONG，成交价必须 >= 限价
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elif (order.direction == "SELL" or order.direction == "CLOSE_LONG") and fill_price < order.limit_price:
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return -1.0 # 未触及限价
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if order.direction == "BUY" or order.direction == "CLOSE_SHORT": # 买入或平空，价格向上偏离
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fill_price = base_price * (1 + self.slippage_rate)
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elif order.direction == "SELL" or order.direction == "CLOSE_LONG": # 卖出或平多，价格向下偏离
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fill_price = base_price * (1 - self.slippage_rate)
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else: # 默认情况，理论上不应该到这里，因为方向应该明确
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fill_price = base_price # 不考虑滑点
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# 市价单只要有价格就会成交，无需额外价格区间判断
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elif order.price_type == "LIMIT" and order.limit_price is not None:
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limit_price = order.limit_price
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high = current_bar.high
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low = current_bar.low
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open_price = current_bar.open # 也可以在限价单成交时用开盘价作为实际成交价，或限价本身
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if order.direction == "BUY" or order.direction == "CLOSE_SHORT": # 限价买入或限价平空
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# 买入：如果K线最低价 <= 限价，则限价单可能成交
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if low <= limit_price:
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# 成交价通常是限价，但考虑滑点后可能略高（对买方不利）
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# 或者可以以 open/low 之间的一个价格成交，这里简化为限价+滑点
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fill_price_candidate = limit_price * (1 + self.slippage_rate)
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# 确保成交价不会比当前K线的最低价还低（如果不是在最低点成交）
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# 也可以简单就返回 limit_price * (1 + self.slippage_rate)
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# 如果开盘价低于或等于限价，那么通常会以开盘价成交（或者略差）
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# 否则，如果价格是从上方跌落到限价区，那么会在限价附近成交
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# 这里简化：如果K线触及限价，则以限价成交（考虑滑点）。
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# 更精细的模拟会考虑价格穿越顺序 (例如，是否开盘就跳过了限价)
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# 如果开盘价已经低于或等于限价，那么就以开盘价成交（考虑滑点）
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if open_price <= limit_price:
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fill_price = open_price * (1 + self.slippage_rate)
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else: # 价格从高处跌落到限价
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fill_price = limit_price * (1 + self.slippage_rate)
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# 确保成交价不会超过限价 (虽然加滑点可能会略超，但这是交易成本的一部分)
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# 这个检查是为了避免逻辑错误，理论上加滑点后应该可以接受比限价略高的价格
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# 如果你严格要求成交价不能高于限价，则需要移除滑点或者更复杂的逻辑
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# 这里我们接受加滑点后的价格
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if fill_price > high and fill_price > limit_price: # 如果计算出来的成交价高于K线最高价，则可能不合理
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fill_price = -1.0 # 理论上不该发生，除非滑点过大
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else:
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return -1.0 # 未触及限价
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elif order.direction == "SELL" or order.direction == "CLOSE_LONG": # 限价卖出或限价平多
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# 卖出：如果K线最高价 >= 限价，则限价单可能成交
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if high >= limit_price:
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# 成交价通常是限价，但考虑滑点后可能略低（对卖方不利）
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fill_price_candidate = limit_price * (1 - self.slippage_rate)
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# 如果开盘价已经高于或等于限价，那么就以开盘价成交（考虑滑点）
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if open_price >= limit_price:
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fill_price = open_price * (1 - self.slippage_rate)
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else: # 价格从低处上涨到限价
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fill_price = limit_price * (1 - self.slippage_rate)
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# 确保成交价不会低于限价
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if fill_price < low and fill_price < limit_price: # 如果计算出来的成交价低于K线最低价，则可能不合理
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fill_price = -1.0 # 理论上不该发生
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else:
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return -1.0 # 未触及限价
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# 最后检查成交价是否有效
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if fill_price <= 0:
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return -1.0 # 如果计算出来价格无效，返回未成交
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return fill_price
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@@ -107,6 +183,7 @@ class ExecutionSimulator:
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if fill_price <= 0: # 表示未成交或不满足限价条件
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if order.price_type == "LIMIT":
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self.pending_orders[order.id] = order
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# print(f'撮合失败,order id:{order.id},fill_price:{fill_price}')
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return None # 未成交，返回None
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# --- 以下是订单成功成交的逻辑 ---
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@@ -116,78 +193,90 @@ class ExecutionSimulator:
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current_position = self.positions.get(symbol, 0)
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current_average_cost = self.average_costs.get(symbol, 0.0)
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if order.direction == "BUY":
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actual_direction = order.direction
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if order.direction == "CLOSE_SHORT":
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actual_direction = "BUY"
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elif order.direction == "CLOSE_LONG":
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actual_direction = "SELL"
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is_close_order_intent = (order.direction == "CLOSE_LONG" or
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order.direction == "CLOSE_SHORT")
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if actual_direction == "BUY": # 处理买入 (开多 / 平空)
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# 开多仓或平空仓
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if current_position >= 0: # 当前持有多仓或无仓位 (开多)
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is_open_trade = True
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is_open_trade = not is_close_order_intent # 如果是平仓意图，则不是开仓交易
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# 更新平均成本 (加权平均)
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new_total_cost = (current_average_cost * current_position) + (fill_price * volume)
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new_total_volume = current_position + volume
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self.average_costs[symbol] = new_total_cost / new_total_volume if new_total_volume > 0 else 0.0
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self.positions[symbol] = new_total_volume
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else: # 当前持有空仓 (平空)
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is_close_trade = True
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is_close_trade = is_close_order_intent # 这是平仓交易
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# 计算平空盈亏
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# PnL = (开仓成本 - 平仓价格) * 平仓数量 (注意空头方向)
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# 简化：假设平空时，直接使用当前的平均开仓成本来计算盈亏
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# 更精确的FIFO/LIFO需更多逻辑
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pnl_per_share = current_average_cost - fill_price # (买入平空，成本高于平仓价则盈利)
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pnl_per_share = current_average_cost - fill_price
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realized_pnl = pnl_per_share * volume
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# 更新持仓和成本
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self.positions[symbol] += volume
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if self.positions[symbol] == 0:
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if self.positions[symbol] == 0: # 如果全部平仓
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del self.positions[symbol]
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if symbol in self.average_costs: del self.average_costs[symbol] # 清理成本
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elif self.positions[symbol] > 0 and current_position < 0: # 部分平空转为多头，需重新设置成本
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# 这部分逻辑可以更复杂，这里简化处理，如果转为多头，成本重置为0
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# 实际应该用剩余的空头成本 + 新开多的成本
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self.average_costs[symbol] = fill_price # 简单地将剩下的多头仓位成本设为当前价格
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if symbol in self.average_costs: del self.average_costs[symbol]
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elif self.positions[symbol] > 0 and current_position < 0: # 部分平空，且空头仓位被买平为多头仓位
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# 这是从空头转为多头的复杂情况。需要重新计算平均成本
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# 简单处理：将剩余的多头仓位成本设为当前价格
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self.average_costs[symbol] = fill_price
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# 资金扣除
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if self.cash >= trade_value + commission:
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self.cash -= (trade_value + commission)
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else:
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# print(f"[{current_bar.datetime}] 资金不足，无法执行买入 {volume} {symbol}")
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print(f"[{current_bar.datetime}] 资金不足，无法执行买入 {volume} {symbol}")
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return None
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elif order.direction == "SELL":
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elif actual_direction == "SELL": # 处理卖出 (开空 / 平多)
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# 开空仓或平多仓
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if current_position <= 0: # 当前持有空仓或无仓位 (开空)
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is_open_trade = True
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is_open_trade = not is_close_order_intent # 如果是平仓意图，则不是开仓交易
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# 更新平均成本 (空头成本为负值)
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new_total_cost = (current_average_cost * current_position) - (fill_price * volume) # 负的持仓乘以负的卖出价
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new_total_volume = current_position - volume # 空头持仓量更负
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self.average_costs[symbol] = new_total_cost / new_total_volume if new_total_volume < 0 else 0.0
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self.positions[symbol] = new_total_volume
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# 对于空头，平均成本通常是指你卖出开仓的平均价格
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# 这里需要根据你的空头成本计算方式来调整
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# 常见的做法是：总卖出价值 / 总卖出数量
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new_total_value = (current_average_cost * abs(current_position)) + (fill_price * volume)
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new_total_volume = abs(current_position) + volume
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self.average_costs[symbol] = new_total_value / new_total_volume if new_total_volume > 0 else 0.0
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self.positions[symbol] -= volume # 空头数量增加，持仓量变为负更多
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else: # 当前持有多仓 (平多)
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is_close_trade = True
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is_close_trade = is_close_order_intent # 这是平仓交易
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# 计算平多盈亏
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# PnL = (平仓价格 - 开仓成本) * 平仓数量
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pnl_per_share = fill_price - current_average_cost # (卖出平多，平仓价高于成本则盈利)
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pnl_per_share = fill_price - current_average_cost
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realized_pnl = pnl_per_share * volume
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# 更新持仓和成本
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self.positions[symbol] -= volume
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if self.positions[symbol] == 0:
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if self.positions[symbol] == 0: # 如果全部平仓
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del self.positions[symbol]
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if symbol in self.average_costs: del self.average_costs[symbol] # 清理成本
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elif self.positions[symbol] < 0 and current_position > 0: # 部分平多转为空头，需重新设置成本
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self.average_costs[symbol] = fill_price # 简单地将剩下的空头仓位成本设为当前价格
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if symbol in self.average_costs: del self.average_costs[symbol]
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elif self.positions[symbol] < 0 and current_position > 0: # 部分平多，且多头仓位被卖平为空头仓位
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# 从多头转为空头的复杂情况
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self.average_costs[symbol] = fill_price # 简单将剩余空头仓位成本设为当前价格
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# 资金扣除 (佣金) 和增加 (卖出收入)
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if self.cash >= commission:
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self.cash -= commission
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self.cash += trade_value
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else:
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# print(f"[{current_bar.datetime}] 资金不足，无法执行卖出 {volume} {symbol}")
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print(f"[{current_bar.datetime}] 资金不足，无法执行卖出 {volume} {symbol}")
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return None
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else: # 既不是 BUY 也不是 SELL，且也不是 CANCEL。这可能是未知的 direction
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print(
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f"[{current_bar.datetime}] 模拟器: 收到未知订单方向 {order.direction} for Order ID: {order.id}. 订单未处理。")
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return None
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# 创建 Trade 对象
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# 创建 Trade 对象
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executed_trade = Trade(
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order_id=order.id, fill_time=current_bar.datetime, symbol=symbol,
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direction=order.direction, # 记录原始订单方向 (BUY/SELL)
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direction=order.direction, # 记录原始订单方向 (BUY/SELL/CLOSE_X)
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volume=volume, price=fill_price, commission=commission,
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cash_after_trade=self.cash, positions_after_trade=self.positions.copy(),
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realized_pnl=realized_pnl, # 填充实现盈亏
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@@ -200,8 +289,6 @@ class ExecutionSimulator:
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if order.id in self.pending_orders:
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del self.pending_orders[order.id]
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# print(f"[{current_bar.datetime}] 成交: {executed_trade.direction} {executed_trade.volume} {executed_trade.symbol} @ {executed_trade.price:.2f}, 佣金: {executed_trade.commission:.2f}, PnL: {executed_trade.realized_pnl:.2f}")
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return executed_trade
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def cancel_order(self, order_id: str) -> bool:
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@@ -245,7 +332,7 @@ class ExecutionSimulator:
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quantity = self.positions[symbol_in_position]
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# 持仓市值 = 数量 * 当前市场价格 (current_bar.close)
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# 无论多头(quantity > 0)还是空头(quantity < 0)，这个计算都是正确的
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total_value += quantity * current_bar.close
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total_value += quantity * current_bar.open
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# 您也可以选择在这里打印调试信息
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# print(f" DEBUG Portfolio Value Calculation: Cash={self.cash:.2f}, "
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@@ -287,3 +374,14 @@ class ExecutionSimulator:
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"""
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print("ExecutionSimulator: 清空交易历史。")
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self.trade_history = []
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def get_average_position_price(self, symbol: str) -> Optional[float]:
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"""
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获取指定合约的平均持仓成本。
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如果无持仓或无该合约记录，返回 None。
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"""
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# 返回 average_costs 字典中对应 symbol 的值
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# 如果没有持仓或者没有记录，返回 None
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if symbol in self.positions and self.positions[symbol] != 0:
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return self.average_costs.get(symbol)
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return None
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