diff --git a/framework_v2/strategies/rotation/rotation.py b/framework_v2/strategies/rotation/rotation.py
index 974d8a7..77ebfdc 100644
--- a/framework_v2/strategies/rotation/rotation.py
+++ b/framework_v2/strategies/rotation/rotation.py
@@ -161,9 +161,10 @@ class GlobalRotationStrategy(StrategyBase):
         
         逻辑：
         1. 计算动态短债阈值（如果使用）
-        2. 每个 group 内竞争，选 Top 1
-        3. 溢价过滤（如果启用）
-        4. 组合所有 group 的选股结果
+        2. 因子对齐到 A 股日历（ffill 填充休市日）
+        3. 每个 group 内竞争，选 Top 1
+        4. 溢价过滤（如果启用）
+        5. 组合所有 group 的选股结果
         
         Args:
             factors: 因子字典 {code: Series}
@@ -174,13 +175,18 @@ class GlobalRotationStrategy(StrategyBase):
         if not factors:
             return pd.DataFrame()
         
-        # 对齐所有因子的日期
+        # 获取 A 股交易日历
+        trading_calendar = self._get_trading_calendar()
+        
+        # 对齐所有因子到 A 股日历（关键：ffill 填充休市日）
         factor_df = pd.DataFrame(factors)
+        factor_df = factor_df.reindex(trading_calendar).ffill()
         
         # 获取动态短债阈值（如果使用）
         bond_threshold = None
         if self.use_dynamic_threshold and self.bond_code and self.bond_code in factors:
-            bond_threshold = factors[self.bond_code]
+            # 也要对齐到 A 股日历
+            bond_threshold = factors[self.bond_code].reindex(trading_calendar).ffill()
             print(f"  [阈值] 使用动态短债阈值: {self.bond_code}")
         
         # 获取溢价率数据（如果启用溢价控制）
@@ -190,14 +196,20 @@ class GlobalRotationStrategy(StrategyBase):
             print(f"  [溢价] 启用溢价过滤，阈值: {self.premium_threshold:.1%}")
         
         # 按 group 分组选股
-        signals = pd.DataFrame(index=factor_df.index, columns=factor_df.columns, data=0)
+        # 注意：signals 的索引现在是 A 股交易日历
+        signals = pd.DataFrame(index=trading_calendar, columns=factor_df.columns, data=0)
         
         groups = self.config.asset_pools.by_group
         
         for date in factor_df.index:
             selected_codes = []
             
-            # 对每个 group 独立选股
+            # 获取 BOND 组的动量作为阈值
+            bond_threshold_value = None
+            if bond_threshold is not None and date in bond_threshold.index:
+                bond_threshold_value = bond_threshold.loc[date] * self.bond_ratio
+            
+            # 对每个 group 独立选股（包括 BOND 组）
             for group_name, assets in groups.items():
                 # 获取该 group 的信号标的
                 group_signal_codes = [asset.signal_source for asset in assets.values()]
@@ -208,10 +220,9 @@ class GlobalRotationStrategy(StrategyBase):
                 if date_factors.empty:
                     continue
                 
-                # 应用动态阈值过滤
-                if bond_threshold is not None and date in bond_threshold.index:
-                    threshold_value = bond_threshold.loc[date] * self.bond_ratio
-                    date_factors = date_factors[date_factors >= threshold_value]
+                # 应用动态阈值过滤（非 BOND 组需要超过 BOND 动量）
+                if bond_threshold_value is not None and group_name != 'BOND':
+                    date_factors = date_factors[date_factors >= bond_threshold_value]
                 
                 if date_factors.empty:
                     continue
@@ -229,7 +240,7 @@ class GlobalRotationStrategy(StrategyBase):
                 top_code = date_factors.idxmax()
                 selected_codes.append(top_code)
             
-            # 第二步：从所有 group 的 Top 1 中，按动量再选 Top select_num 个
+            # 第二步：从所有 group 的 Top 1 中（包括BOND），按动量再选 Top select_num 个
             if selected_codes:
                 # 获取这些标的的当日因子值
                 candidate_factors = factor_df.loc[date][selected_codes].dropna()
@@ -240,6 +251,16 @@ class GlobalRotationStrategy(StrategyBase):
                         final_selected = candidate_factors.nlargest(self.select_num).index.tolist()
                     else:
                         final_selected = candidate_factors.index.tolist()
+                        
+                        # 如果选中的不足 select_num，用 BOND 填充空余仓位
+                        if self.fill_bond and self.bond_code:
+                            bond_has_data = (self.bond_code in factor_df.columns and 
+                                            pd.notna(factor_df.loc[date].get(self.bond_code)))
+                            
+                            if bond_has_data and self.bond_code not in final_selected:
+                                n_bond_slots = self.select_num - len(final_selected)
+                                for _ in range(n_bond_slots):
+                                    final_selected.append(self.bond_code)
                     
                     # 标记信号
                     signals.loc[date, final_selected] = 1
@@ -411,23 +432,43 @@ class GlobalRotationStrategy(StrategyBase):
     
     def _get_premium_data(self) -> Optional[Dict]:
         """
-        获取溢价率数据
+        从已获取的数据中提取溢价率
         
         Returns:
-            溢价率数据字典 {trade_code: {date: premium_rate}}
+            溢价率数据字典 {signal_code: premium_series}
         """
-        # TODO: 从数据源获取溢价率数据
-        # 当前返回 None，后续实现
-        return None
+        if not hasattr(self, '_data') or self._data is None:
+            print("  [警告] 数据未加载，无法获取溢价率")
+            return None
+        
+        signal_to_trade = self.config.asset_pools.get_signal_to_trade_mapping()
+        
+        premium_dict = {}
+        for signal_code, trade_code in signal_to_trade.items():
+            if trade_code in self._data:
+                etf_df = self._data[trade_code]
+                
+                # 从 attrs 中提取溢价率序列
+                premium_series = etf_df.attrs.get('premium_series', {})
+                
+                if premium_series:
+                    # 转换为 Series 并确保 DatetimeIndex
+                    premium_s = pd.Series(premium_series)
+                    premium_s.index = pd.to_datetime(premium_s.index)
+                    premium_dict[signal_code] = premium_s
+        
+        return premium_dict if premium_dict else None
     
     def _filter_by_premium(self, factors: pd.Series, date: pd.Timestamp, premium_data: Dict) -> pd.Series:
         """
         溢价过滤
         
+        逻辑：如果 ETF 溢价率 > 阈值，则从候选中排除
+        
         Args:
             factors: 因子 Series
             date: 日期
-            premium_data: 溢价率数据
+            premium_data: 溢价率数据字典
         
         Returns:
             过滤后的因子 Series
@@ -435,8 +476,24 @@ class GlobalRotationStrategy(StrategyBase):
         if premium_data is None:
             return factors
         
-        # TODO: 实现溢价过滤逻辑
-        return factors
+        filtered_codes = []
+        for code in factors.index:
+            if code in premium_data:
+                # 获取当前日期的溢价率（前向填充）
+                premium_s = premium_data[code]
+                premium_before = premium_s[premium_s.index <= date]
+                
+                if len(premium_before) > 0:
+                    premium_rate = premium_before.iloc[-1]
+                    
+                    # 如果溢价率超过阈值，排除该标的
+                    if premium_rate > self.premium_threshold:
+                        print(f"    [溢价过滤] {code} 溢价率 {premium_rate:.2%} > 阈值 {self.premium_threshold:.2%}，排除")
+                        continue
+            
+            filtered_codes.append(code)
+        
+        return factors[filtered_codes] if filtered_codes else pd.Series(dtype=float)
     
     def _get_trading_calendar(self) -> pd.DatetimeIndex:
         """
@@ -472,3 +529,294 @@ class GlobalRotationStrategy(StrategyBase):
             start_dt = pd.Timestamp(start)
             end_dt = pd.Timestamp(end)
             return pd.date_range(start=start_dt, end=end_dt, freq='B')  # 工作日
+    
+    @staticmethod
+    def _safe_val(v, decimals=4):
+        """安全转换数值，处理 NaN/Inf"""
+        import math
+        
+        if v is None or (isinstance(v, float) and (math.isnan(v) or math.isinf(v))):
+            return None
+        if isinstance(v, (np.floating, float)):
+            return round(float(v), decimals)
+        if isinstance(v, (np.integer, int)):
+            return int(v)
+        return v
+    
+    def _export_backtest_detail(
+        self,
+        factors: Dict[str, pd.Series],
+        signals: pd.DataFrame,
+        positions: pd.DataFrame,
+        result: Dict,
+        output_path: str
+    ):
+        """
+        导出逐日明细到 JSON
+        
+        Args:
+            factors: 因子字典
+            signals: 信号 DataFrame
+            positions: 仓位 DataFrame
+            result: 回测结果
+            output_path: 输出文件路径
+        """
+        import json
+        from pathlib import Path
+        
+        # 准备数据
+        equity_curve = result['equity_curve']
+        strategy_returns = result['strategy_returns']
+        trading_calendar = equity_curve.index
+        
+        # 提取溢价率
+        premium_dict = self._get_premium_data()
+        
+        # 准备价格数据
+        signal_to_trade = self.config.asset_pools.get_signal_to_trade_mapping()
+        index_close_dict = {}
+        etf_close_dict = {}
+        
+        for signal_code, trade_code in signal_to_trade.items():
+            if signal_code in self._data:
+                index_close_dict[signal_code] = self._data[signal_code]['close']
+            if trade_code in self._data:
+                etf_close_dict[signal_code] = self._data[trade_code]['close']
+        
+        # 计算收益率（对齐到 A 股日历）
+        index_return_dict = {}
+        etf_return_dict = {}
+        
+        for signal_code, trade_code in signal_to_trade.items():
+            # 指数收益率
+            if signal_code in index_close_dict:
+                idx_close = index_close_dict[signal_code].reindex(trading_calendar, method='ffill')
+                idx_return = idx_close.pct_change(fill_method=None).fillna(0)
+                index_return_dict[signal_code] = idx_return
+            
+            # ETF 收益率
+            if signal_code in etf_close_dict:
+                etf_close = etf_close_dict[signal_code].reindex(trading_calendar, method='ffill')
+                etf_return = etf_close.pct_change(fill_method=None).fillna(0)
+                etf_return_dict[signal_code] = etf_return
+        
+        # 对齐因子
+        factor_df = pd.DataFrame(factors)
+        if not isinstance(factor_df.index, pd.DatetimeIndex):
+            factor_df.index = pd.to_datetime(factor_df.index)
+        
+        factor_df_aligned = factor_df.reindex(trading_calendar).ffill()
+        
+        # 对齐价格
+        positions_aligned = positions.reindex(trading_calendar, method='ffill')
+        
+        # 持仓状态跟踪
+        holdings_state = {}
+        prev_holdings = set()
+        days_list = []
+        
+        # 配置信息
+        bond_code = self.bond_code if self.use_dynamic_threshold else None
+        bond_ratio = self.bond_ratio
+        
+        # 逐日构建
+        for date in trading_calendar:
+            # 当前持仓
+            pos_row = positions_aligned.loc[date]
+            current_holdings = set(pos_row[pos_row > 0].index.tolist())
+            
+            # 调仓检测
+            added = list(current_holdings - prev_holdings)
+            removed = list(prev_holdings - current_holdings)
+            is_rebalance = len(added) > 0 or len(removed) > 0
+            
+            # 更新持仓状态
+            for code in removed:
+                holdings_state.pop(code, None)
+            for code in added:
+                entry_price = None
+                if code in etf_close_dict:
+                    ep = etf_close_dict[code].reindex(trading_calendar, method='ffill').get(date)
+                    if pd.notna(ep):
+                        entry_price = float(ep)
+                
+                holdings_state[code] = {
+                    'entry_date': date.strftime('%Y-%m-%d'),
+                    'entry_price': entry_price,
+                }
+            
+            # 动量得分和阈值
+            factor_scores = {}
+            if date in factor_df_aligned.index:
+                for code in factor_df_aligned.columns:
+                    v = factor_df_aligned.loc[date, code]
+                    if pd.notna(v):
+                        factor_scores[code] = float(v)
+            
+            bond_score = factor_scores.get(bond_code) if bond_code else None
+            threshold = bond_score * bond_ratio if bond_score else 0.0
+            
+            # 排名（所有标的都参与排名，包括 BOND）
+            groups = self.config.asset_pools.by_group
+            bond_codes = set(groups.get('BOND', {}).keys())
+            
+            # 所有标的都参与排名
+            sorted_codes = sorted(factor_scores.keys(), key=lambda c: factor_scores[c], reverse=True)
+            rank_map = {c: r + 1 for r, c in enumerate(sorted_codes) if c in factor_scores}
+            
+            # 构建每标的详情
+            assets = {}
+            all_codes = factor_df.columns.tolist()
+            
+            for code in all_codes:
+                asset = {}
+                
+                # 动量相关
+                mom = factor_scores.get(code)
+                asset['momentum'] = self._safe_val(mom, 4)
+                asset['rank'] = rank_map.get(code)
+                asset['threshold'] = self._safe_val(threshold, 4)
+                asset['above_threshold'] = mom >= threshold if mom is not None else False
+                
+                # 价格
+                if code in index_close_dict:
+                    idx_close = index_close_dict[code].reindex(trading_calendar, method='ffill').get(date)
+                    asset['index_close'] = self._safe_val(idx_close, 2) if pd.notna(idx_close) else None
+                else:
+                    asset['index_close'] = None
+                
+                if code in etf_close_dict:
+                    etf_close = etf_close_dict[code].reindex(trading_calendar, method='ffill').get(date)
+                    asset['etf_close'] = self._safe_val(etf_close, 3) if pd.notna(etf_close) else None
+                else:
+                    asset['etf_close'] = None
+                
+                # 当日收益率
+                if code in index_return_dict:
+                    idx_ret = index_return_dict[code].loc[date] if date in index_return_dict[code].index else 0
+                    asset['index_return'] = self._safe_val(idx_ret, 6) if pd.notna(idx_ret) else 0.0
+                else:
+                    asset['index_return'] = 0.0
+                
+                if code in etf_return_dict:
+                    etf_ret = etf_return_dict[code].loc[date] if date in etf_return_dict[code].index else 0
+                    asset['etf_return_ctc'] = self._safe_val(etf_ret, 6) if pd.notna(etf_ret) else 0.0
+                else:
+                    asset['etf_return_ctc'] = 0.0
+                
+                # 溢价率
+                if code in premium_dict:
+                    premium_s = premium_dict[code]
+                    if date in premium_s.index:
+                        premium_val = premium_s.loc[date]
+                        asset['premium'] = round(float(premium_val), 4) if pd.notna(premium_val) else None
+                    else:
+                        premium_before = premium_s[premium_s.index <= date]
+                        if len(premium_before) > 0:
+                            asset['premium'] = round(float(premium_before.iloc[-1]), 4)
+                        else:
+                            asset['premium'] = None
+                else:
+                    asset['premium'] = None
+                
+                # 持仓状态
+                is_held = code in current_holdings
+                asset['is_held'] = is_held
+                
+                if is_held and code in holdings_state:
+                    hs = holdings_state[code]
+                    asset['entry_date'] = hs['entry_date']
+                    asset['entry_price_etf'] = self._safe_val(hs['entry_price'], 4)
+                    asset['entry_price_idx'] = None
+                    
+                    entry_dt = pd.Timestamp(hs['entry_date'])
+                    trading_days_held = len(trading_calendar[(trading_calendar >= entry_dt) & (trading_calendar <= date)])
+                    asset['holding_days'] = trading_days_held
+                    
+                    # 累计收益
+                    if hs['entry_price'] and hs['entry_price'] > 0:
+                        if code in etf_close_dict:
+                            cur = etf_close_dict[code].reindex(trading_calendar, method='ffill').get(date)
+                            if cur and pd.notna(cur):
+                                cum_ret = float(cur) / hs['entry_price'] - 1
+                                asset['cum_return_etf'] = self._safe_val(cum_ret, 4)
+                                asset['cum_return_idx'] = self._safe_val(cum_ret, 4)
+                            else:
+                                asset['cum_return_etf'] = None
+                                asset['cum_return_idx'] = None
+                        else:
+                            asset['cum_return_etf'] = None
+                            asset['cum_return_idx'] = None
+                    else:
+                        asset['cum_return_etf'] = None
+                        asset['cum_return_idx'] = None
+                else:
+                    asset['entry_date'] = None
+                    asset['entry_price_etf'] = None
+                    asset['entry_price_idx'] = None
+                    asset['holding_days'] = 0
+                    asset['cum_return_etf'] = None
+                    asset['cum_return_idx'] = None
+                
+                assets[code] = asset
+            
+            # 信号
+            signal_row = signals.loc[date] if date in signals.index else pd.Series(dtype=float)
+            active_signals = {code: int(val) for code, val in signal_row.items() if val > 0}
+            
+            # 构建日记录
+            day_record = {
+                'date': date.strftime('%Y-%m-%d'),
+                'nav': self._safe_val(equity_curve.loc[date], 4),
+                'daily_return': self._safe_val(strategy_returns.loc[date], 6),
+                'is_rebalance': is_rebalance,
+                'signals': active_signals,
+                'holdings': sorted(list(current_holdings)),
+                'added': sorted(added),
+                'removed': sorted(removed),
+                'assets': assets
+            }
+            days_list.append(day_record)
+            prev_holdings = current_holdings
+        
+        # 构建元数据
+        codes_meta = {}
+        for code in all_codes:
+            asset_config = self.config.asset_pools.assets.get(code)
+            codes_meta[code] = {
+                'name': asset_config.name if asset_config else code,
+                'etf': asset_config.trade_source if asset_config else None,
+                'market': asset_config.group if asset_config else None
+            }
+        
+        output = {
+            'meta': {
+                'mode': 'V2: 指数信号 + ETF收益',
+                'start_date': trading_calendar[0].strftime('%Y-%m-%d'),
+                'end_date': trading_calendar[-1].strftime('%Y-%m-%d'),
+                'total_days': len(trading_calendar),
+                'select_num': self.select_num,
+                'n_days': self.config.factor.n_days,
+                'trade_cost': self.trade_cost,
+                'bond_threshold': {
+                    'enabled': self.use_dynamic_threshold,
+                    'bond_code': bond_code,
+                    'ratio': bond_ratio
+                },
+                'codes': codes_meta
+            },
+            'days': days_list
+        }
+        
+        # 输出
+        output_path = Path(output_path)
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+        
+        with open(output_path, 'w', encoding='utf-8') as f:
+            json.dump(output, f, ensure_ascii=False)
+        
+        file_size_mb = output_path.stat().st_size / 1024 / 1024
+        print(f"      写入 {output_path}")
+        print(f"      大小: {file_size_mb:.1f} MB")
+        print(f"      天数: {len(days_list)}")
+        print(f"      标的: {len(all_codes)}")