diff --git a/framework_v2/strategies/rotation/rotation.py b/framework_v2/strategies/rotation/rotation.py
index 5bae48a..80e580b 100644
--- a/framework_v2/strategies/rotation/rotation.py
+++ b/framework_v2/strategies/rotation/rotation.py
@@ -18,6 +18,7 @@ from datetime import datetime, timedelta
 from framework_v2.core.strategy import StrategyBase
 from framework_v2.config.schemas import StrategyConfig
 from framework_v2.shared.factors import MomentumFactor
+from framework_v2.shared.data.alignment import CrossMarketAligner
 
 
 class GlobalRotationStrategy(StrategyBase):
@@ -275,7 +276,7 @@ class GlobalRotationStrategy(StrategyBase):
     
     def _execute_backtest(self, positions: pd.DataFrame, data: Dict[str, pd.DataFrame]) -> Dict[str, any]:
         """
-        执行回测（包含交易成本和调仓控制）
+        执行回测（使用 CrossMarketAligner 进行正确的数据对齐）
         
         Args:
             positions: 仓位 DataFrame
@@ -287,33 +288,38 @@ class GlobalRotationStrategy(StrategyBase):
         # 获取信号→交易映射
         signal_to_trade = self.config.asset_pools.get_signal_to_trade_mapping()
         
-        # 提取交易标的的收盘价
-        close_prices = {}
+        # 获取 A 股交易日历
+        print("\n  [对齐] 获取 A 股交易日历...")
+        trading_calendar = self._get_trading_calendar()
+        print(f"  [日历] A 股交易日: {len(trading_calendar)} 天 ({trading_calendar[0]} ~ {trading_calendar[-1]})")
+        
+        # 创建对齐器
+        aligner = CrossMarketAligner(target_calendar=trading_calendar)
+        
+        # 提取交易标的的收盘价，并对齐到 A 股日历
+        print("  [对齐] 对齐 ETF 价格到 A 股日历...")
+        close_dict = {}
         for signal_code, trade_code in signal_to_trade.items():
             if trade_code in data:
-                close_prices[signal_code] = data[trade_code]['close']
+                # 提取收盘价
+                close_series = data[trade_code]['close']
+                # 使用 signal_code 作为键（与 positions 列名一致）
+                close_dict[signal_code] = close_series
             else:
                 print(f"  警告: {trade_code} 数据不存在，跳过")
         
-        close_df = pd.DataFrame(close_prices)
+        # 使用 CrossMarketAligner 对齐多标的收益率
+        # 内部逻辑：先 ffill 价格到 A 股日历，再计算收益率
+        print("  [对齐] 计算收益率（先对齐价格，再计算）...")
+        returns_df = aligner.align_multi_asset(close_dict)
+        print(f"  [对齐] 收益率数据: {len(returns_df)} 天, {len(returns_df.columns)} 个标的")
         
-        # 计算收益率
-        returns = close_df.pct_change()
-        
-        # 获取 A 股交易日历并过滤
-        print("\n  [过滤] 获取 A 股交易日历...")
-        trading_calendar = self._get_trading_calendar()
-        
-        # 过滤到 A 股交易日
-        original_days = len(returns)
-        returns = returns[returns.index.isin(trading_calendar)]
-        positions = positions[positions.index.isin(trading_calendar)]
-        filtered_days = len(returns)
-        print(f"  [过滤] 原始数据: {original_days} 天 -> A 股交易日: {filtered_days} 天 (过滤 {original_days - filtered_days} 天)")
+        # 对齐 positions 到 A 股日历
+        positions = positions.reindex(trading_calendar, method='ffill')
         
         # 计算策略收益（仓位加权，T+1 执行）
         positions_delayed = positions.shift(1).fillna(0)
-        strategy_returns = (positions_delayed * returns).sum(axis=1)
+        strategy_returns = (positions_delayed * returns_df).sum(axis=1)
         
         # 扣除交易成本
         strategy_returns, rebalance_count = self._apply_trade_cost(