feat(execution): 回测调仓事件记录功能增强
新增调仓事件记录功能，详细记录每次调仓的信息： 核心改进： 1. BacktestExecutor新增_apply_trade_cost_with_events方法 - 记录每次调仓的基本信息（持仓变化、调入调出标的） - 记录换手率、调仓成本、持仓天数、当日收益 2. 新增_enrich_rebalance_events方法 - 补充净值信息（调仓前净值、调仓后净值、净值变化%） 3. strategy.py保存调仓记录到CSV - 新增rebalances.csv文件 - 返回结果包含rebalance_events 调仓记录字段： - 调仓前持仓、调仓后持仓 - 调入标的、调出标的 - 换手率、调仓成本 - 持仓天数、当日收益 - 调仓前净值、调仓后净值、净值变化% 应用场景： - 分析每次调仓对收益的影响 - 评估调仓决策质量 - 统计调仓频率与效果
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@@ -193,8 +193,8 @@ class BacktestExecutor(Executor):
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# 计算策略日收益率
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result = self._calculate_daily_returns(signals, data, signal_col)
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# 扣除交易成本
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result = self._apply_trade_cost(result, signals, signal_col)
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# 扣除交易成本（同时记录调仓事件）
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result, rebalance_events = self._apply_trade_cost_with_events(result, signals, signal_col)
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# 计算净值（起点归一化）
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result = self._calculate_net_value(result)
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@@ -208,6 +208,12 @@ class BacktestExecutor(Executor):
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# 存储回测结果
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portfolio.backtest_result = result
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portfolio.rebalance_events = rebalance_events # 新增：调仓事件记录
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# 补充调仓事件的净值信息
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if not rebalance_events.empty:
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rebalance_events = self._enrich_rebalance_events(rebalance_events, result)
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portfolio.rebalance_events = rebalance_events
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return portfolio
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@@ -274,6 +280,162 @@ class BacktestExecutor(Executor):
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return result
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def _apply_trade_cost_with_events(self, result: pd.DataFrame, signals: pd.DataFrame, signal_col: str = 'signal') -> tuple:
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"""
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扣除交易成本并记录调仓事件
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Returns:
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(result, rebalance_events): 回测结果DataFrame和调仓事件DataFrame
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"""
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prev_signal = signals[signal_col].shift(1)
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# 记录调仓事件
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rebalance_events = []
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last_rebalance_date = None
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# 先计算累积收益率（用于计算调仓前后的净值）
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cum_return_before_cost = result['策略日收益率'].copy()
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if self.select_num == 1:
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# 单标的策略
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for i, (date, curr, prev) in enumerate(zip(signals.index, signals[signal_col], prev_signal)):
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# 检查是否调仓
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is_rebalance = False
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turnover = 0.0
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added = []
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removed = []
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if pd.notna(prev) and curr != prev:
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is_rebalance = True
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turnover = 1.0 if prev else 0.0
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added = [curr] if curr else []
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removed = [prev] if prev else []
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# 扣除成本
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result.loc[date, '策略日收益率'] -= self.trade_cost
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# 记录调仓事件
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if is_rebalance:
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# 计算持仓天数
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holding_days = 0
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if last_rebalance_date is not None:
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holding_days = (date - last_rebalance_date).days
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event = {
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'日期': date,
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'调仓前持仓': prev if pd.notna(prev) else '',
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'调仓后持仓': curr,
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'调入标的': ','.join(added) if added else '',
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'调出标的': ','.join(removed) if removed else '',
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'换手率': turnover,
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'调仓成本': self.trade_cost * turnover,
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'持仓天数': holding_days,
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'当日收益': result.loc[date, '策略日收益率'] + self.trade_cost * turnover, # 原始收益（扣除成本前）
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}
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rebalance_events.append(event)
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last_rebalance_date = date
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else:
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# 多标的策略
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turnover_list = []
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for i, (date, curr, prev) in enumerate(zip(signals.index, signals[signal_col], prev_signal)):
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# 检查是否调仓
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is_rebalance = False
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turnover = 0.0
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added = []
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removed = []
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if pd.notna(prev) and curr != prev:
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old = set(prev.split(',')) if prev else set()
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new = set(curr.split(',')) if curr else set()
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added = list(new - old)
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removed = list(old - new)
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swapped = len(removed)
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turnover = swapped / len(old) if old else 0.0
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is_rebalance = len(added) > 0 or len(removed) > 0
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turnover_list.append(turnover)
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# 扣除成本
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result.loc[date, '策略日收益率'] -= turnover * self.trade_cost
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else:
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turnover_list.append(0.0)
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# 记录调仓事件
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if is_rebalance:
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# 计算持仓天数
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holding_days = 0
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if last_rebalance_date is not None:
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holding_days = (date - last_rebalance_date).days
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event = {
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'日期': date,
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'调仓前持仓': prev if pd.notna(prev) else '',
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'调仓后持仓': curr,
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'调入标的': ','.join(added) if added else '',
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'调出标的': ','.join(removed) if removed else '',
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'换手率': turnover,
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'调仓成本': self.trade_cost * turnover,
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'持仓天数': holding_days,
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'当日收益': result.loc[date, '策略日收益率'] + turnover * self.trade_cost, # 原始收益（扣除成本前）
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}
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rebalance_events.append(event)
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last_rebalance_date = date
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result['换手率'] = turnover_list
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# 转换为DataFrame
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rebalance_df = pd.DataFrame(rebalance_events) if rebalance_events else pd.DataFrame()
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if not rebalance_df.empty:
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rebalance_df['日期'] = pd.to_datetime(rebalance_df['日期'])
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rebalance_df = rebalance_df.set_index('日期')
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return result, rebalance_df
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def _enrich_rebalance_events(self, rebalance_df: pd.DataFrame, result: pd.DataFrame) -> pd.DataFrame:
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"""
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补充调仓事件的净值信息
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Args:
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rebalance_df: 调仓事件DataFrame
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result: 回测结果DataFrame（含净值序列）
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Returns:
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补充净值信息后的调仓事件DataFrame
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"""
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# 计算调仓前后净值变化
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nav_before_list = []
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nav_after_list = []
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nav_change_list = []
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for date in rebalance_df.index:
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# 获取调仓日的净值
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if date in result.index:
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# 调仓前净值：前一天收盘净值
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prev_date_idx = result.index.get_loc(date) - 1
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if prev_date_idx >= 0:
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nav_before = result['策略净值'].iloc[prev_date_idx]
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else:
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nav_before = 1.0
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# 调仓后净值：当天收盘净值
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nav_after = result.loc[date, '策略净值']
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# 净值变化
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nav_change = (nav_after / nav_before - 1) * 100
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else:
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nav_before = None
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nav_after = None
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nav_change = None
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nav_before_list.append(nav_before)
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nav_after_list.append(nav_after)
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nav_change_list.append(nav_change)
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# 添加净值信息列
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rebalance_df['调仓前净值'] = nav_before_list
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rebalance_df['调仓后净值'] = nav_after_list
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rebalance_df['净值变化%'] = nav_change_list
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return rebalance_df
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def _calculate_net_value(self, result: pd.DataFrame) -> pd.DataFrame:
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"""计算净值（起点归一化）"""
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result['策略净值'] = (1 + result['策略日收益率']).cumprod()
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@@ -451,17 +451,29 @@ class RotationStrategy(StrategyBase):
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print("\n回测结果:")
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print(f" 最终净值: {final_nav:.4f}\n 累计收益: {total_return:.2f}%")
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# 获取调仓事件
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rebalance_events = getattr(portfolio, 'rebalance_events', pd.DataFrame())
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if not rebalance_events.empty:
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print(f" 调仓次数: {len(rebalance_events)} 次")
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# 保存报告
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if save_path:
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result[['策略净值']].to_csv(f"{save_path}_nav.csv")
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signals.to_csv(f"{save_path}_signals.csv")
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print(f" 报告保存: {save_path}_*.csv")
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# 保存调仓事件记录
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if not rebalance_events.empty:
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rebalance_events.to_csv(f"{save_path}_rebalances.csv")
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print(f" 报告保存: {save_path}_*.csv (含调仓记录)")
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else:
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print(f" 报告保存: {save_path}_*.csv")
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return {
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'signals': signals,
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'result': result,
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'portfolio': portfolio,
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'total_return': total_return
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'total_return': total_return,
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'rebalance_events': rebalance_events
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}
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return {'signals': signals, 'result': None}
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