fix: GlobalRotationStrategy select_num 未生效

- 修复分散化选股逻辑：每个 group 选 Top 1 后，需要再按动量排序选 Top select_num
- 之前：所有 group 的 Top 1 都标记为信号（忽略 select_num）
- 现在：先从每个 group 选 Top 1，再从中按动量选 Top select_num 个
- 影响：配置 select_num=3 时，实际持仓 3 只而不是 4 只（group 数量）

framework_v2/scripts/convert_to_viewer_csv.py

@@ -0,0 +1,158 @@
+#!/usr/bin/env python3
+"""
+将 V2 回测细节 JSON 转换为 HTML viewer 需要的 CSV 格式。
+
+用途：
+- 适配 backtest_viewer.html 的 CSV 格式要求
+- 生成 _detail.csv、_close.csv、_holdings.csv、_code_info.csv
+
+用法:
+    python framework_v2/scripts/convert_to_viewer_csv.py
+"""
+
+import sys
+import json
+from pathlib import Path
+
+import pandas as pd
+
+project_root = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+
+def safe_float(val, decimals=4):
+    """安全转换浮点数"""
+    if val is None:
+        return None
+    try:
+        return round(float(val), decimals)
+    except (ValueError, TypeError):
+        return None
+
+
+def convert_v2_to_viewer_csv(json_path=None):
+    """将 V2 JSON 转换为 V1 CSV 格式"""
+    
+    # 1. 加载 JSON
+    if json_path is None:
+        json_path = project_root / 'framework_v2' / 'results' / 'backtest_detail_v2.json'
+    
+    print(f"加载 V2 JSON: {json_path}")
+    with open(json_path, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+    
+    meta = data['meta']
+    days = data['days']
+    
+    print(f"  天数: {len(days)}")
+    print(f"  标的: {len(meta['codes'])}")
+    
+    # 2. 生成 _detail.csv
+    print("\n生成 _detail.csv...")
+    detail_rows = []
+    
+    for day in days:
+        row = {
+            '日期': day['date'],
+            '净值': day['nav'],
+            '日收益': day['daily_return'],
+            '持仓标的': ','.join(day['holdings']) if day['holdings'] else ''
+        }
+        
+        # 添加每个标的的动量
+        for code, asset in day['assets'].items():
+            row[f'动量_{code}'] = asset.get('momentum')
+        
+        detail_rows.append(row)
+    
+    detail_df = pd.DataFrame(detail_rows)
+    detail_path = json_path.parent / 'backtest_detail_v2_detail.csv'
+    detail_df.to_csv(detail_path, index=False, encoding='utf-8-sig')
+    print(f"  ✅ {detail_path.name} ({len(detail_df)} 行)")
+    
+    # 3. 生成 _close.csv
+    print("\n生成 _close.csv...")
+    close_rows = []
+    
+    for day in days:
+        row = {'日期': day['date']}
+        
+        for code, asset in day['assets'].items():
+            # ETF 收盘价
+            etf_close = asset.get('etf_close')
+            row[f'收盘价_{code}'] = etf_close
+        
+        close_rows.append(row)
+    
+    close_df = pd.DataFrame(close_rows)
+    close_path = json_path.parent / 'backtest_detail_v2_close.csv'
+    close_df.to_csv(close_path, index=False, encoding='utf-8-sig')
+    print(f"  ✅ {close_path.name} ({len(close_df)} 行)")
+    
+    # 4. 生成 _holdings.csv
+    print("\n生成 _holdings.csv...")
+    holdings_rows = []
+    
+    for day in days:
+        if not day['holdings']:
+            continue
+        
+        for code in day['holdings']:
+            asset = day['assets'].get(code, {})
+            
+            code_meta = meta['codes'].get(code, {})
+            
+            row = {
+                '日期': day['date'],
+                '标的代码': code,
+                '标的名称': code_meta.get('name', code),
+                '大类': code_meta.get('group', ''),
+                '进场日期': asset.get('entry_date'),
+                '进场价': asset.get('entry_price'),
+                '当前价': asset.get('etf_close'),
+                '持仓天数': asset.get('holding_days', 0),
+                '持仓比例': asset.get('weight', 0),
+                '累计收益': asset.get('cum_return')
+            }
+            
+            holdings_rows.append(row)
+    
+    holdings_df = pd.DataFrame(holdings_rows)
+    holdings_path = json_path.parent / 'backtest_detail_v2_holdings.csv'
+    holdings_df.to_csv(holdings_path, index=False, encoding='utf-8-sig')
+    print(f"  ✅ {holdings_path.name} ({len(holdings_df)} 行)")
+    
+    # 5. 生成 _code_info.csv
+    print("\n生成 _code_info.csv...")
+    code_info_rows = []
+    
+    for code, meta_info in meta['codes'].items():
+        row = {
+            '代码': code,
+            '名称': meta_info.get('name', code),
+            '大类': meta_info.get('group', '')
+        }
+        code_info_rows.append(row)
+    
+    code_info_df = pd.DataFrame(code_info_rows)
+    code_info_path = json_path.parent / 'backtest_detail_v2_code_info.csv'
+    code_info_df.to_csv(code_info_path, index=False, encoding='utf-8-sig')
+    print(f"  ✅ {code_info_path.name} ({len(code_info_df)} 行)")
+    
+    # 6. 汇总
+    print("\n" + "=" * 80)
+    print("转换完成！")
+    print("=" * 80)
+    print(f"\n输出文件:")
+    print(f"  {detail_path.name}")
+    print(f"  {close_path.name}")
+    print(f"  {holdings_path.name}")
+    print(f"  {code_info_path.name}")
+    print(f"\n使用方法:")
+    print(f"  1. 打开 visualization/backtest_viewer.html")
+    print(f"  2. 选择 'V2 GlobalRotationStrategy (最新)'")
+    print(f"  3. 自动加载 CSV 文件")
+
+
+if __name__ == '__main__':
+    convert_v2_to_viewer_csv()

framework_v2/scripts/export_backtest_detail.py

@@ -87,6 +87,24 @@ def main():
     trading_calendar = strategy._get_trading_calendar()
     print(f"      A 股交易日: {len(trading_calendar)} 天")
     
+    # 准备收盘价和溢价率数据
+    print("[7.5] 准备价格和溢价率数据...")
+    index_close_dict = {}  # 指数收盘价
+    etf_close_dict = {}    # ETF 收盘价
+    etf_premium_dict = {}  # ETF 溢价率（需要从 API 获取）
+    
+    for signal_code, trade_code in signal_to_trade.items():
+        # 指数收盘价
+        if signal_code in data:
+            index_close_dict[signal_code] = data[signal_code]['close']
+        
+        # ETF 收盘价
+        if trade_code in data:
+            etf_close_dict[signal_code] = data[trade_code]['close']  # 注意：用 signal_code 作为键
+    
+    # 溢价率暂时设为 None（需要额外 API 支持）
+    # TODO: 接入 ETF 净值数据计算溢价率
+    
     # 创建对齐器
     aligner = CrossMarketAligner(target_calendar=trading_calendar)
     
@@ -192,6 +210,25 @@ def main():
         assets = {}
         all_codes = factor_df.columns.tolist()
         
+        # 对齐价格到 A 股日历
+        index_close_aligned = {}
+        etf_close_aligned = {}
+        
+        for code in all_codes:
+            if code in index_close_dict:
+                index_close_aligned[code] = index_close_dict[code].reindex(common_dates, method='ffill')
+            if code in etf_close_dict:
+                etf_close_aligned[code] = etf_close_dict[code].reindex(common_dates, method='ffill')
+        
+        # 计算指数和 ETF 收益率
+        index_returns = {}
+        etf_returns = {}
+        for code in all_codes:
+            if code in index_close_aligned:
+                index_returns[code] = index_close_aligned[code].pct_change(fill_method=None)
+            if code in etf_close_aligned:
+                etf_returns[code] = etf_close_aligned[code].pct_change(fill_method=None)
+        
         for code in all_codes:
             asset = {}
             
@@ -206,49 +243,75 @@ def main():
             asset['threshold'] = safe_val(threshold, 4)
             asset['above_threshold'] = mom >= threshold if mom is not None else False
             
+            # 指数价格
+            if code in index_close_aligned:
+                idx_close = index_close_aligned[code].get(date)
+                asset['index_close'] = safe_val(idx_close, 2) if pd.notna(idx_close) else None
+            else:
+                asset['index_close'] = None
+            
+            # ETF 价格
+            if code in etf_close_aligned:
+                etf_close = etf_close_aligned[code].get(date)
+                asset['etf_close'] = safe_val(etf_close, 3) if pd.notna(etf_close) else None
+            else:
+                asset['etf_close'] = None
+            
+            # 指数收益率
+            if code in index_returns:
+                idx_ret = index_returns[code].get(date)
+                asset['index_return'] = safe_val(idx_ret, 6) if pd.notna(idx_ret) else None
+            else:
+                asset['index_return'] = None
+            
+            # ETF 收益率（兼容 V1 命名：etf_return_ctc）
+            if code in etf_returns:
+                etf_ret = etf_returns[code].get(date)
+                asset['etf_return_ctc'] = safe_val(etf_ret, 6) if pd.notna(etf_ret) else None
+            else:
+                asset['etf_return_ctc'] = None
+            
+            # 溢价率（暂时为 None）
+            asset['premium'] = None
+            
             # 持仓状态
             is_held = code in current_holdings
             asset['is_held'] = is_held
-            asset['weight'] = safe_val(pos_row.get(code, 0), 4) if is_held else 0.0
             
             if is_held and code in holdings_state:
                 hs = holdings_state[code]
                 asset['entry_date'] = hs['entry_date']
-                asset['entry_price'] = safe_val(hs['entry_price'], 4)
+                asset['entry_price_etf'] = safe_val(hs['entry_price'], 4)
+                asset['entry_price_idx'] = None  # V2 暂不记录指数进场价
                 
                 entry_dt = pd.Timestamp(hs['entry_date'])
                 trading_days_held = len(common_dates[(common_dates >= entry_dt) & (common_dates <= date)])
                 asset['holding_days'] = trading_days_held
                 
-                # 累计收益
+                # 累计收益（区分 ETF 和指数，兼容 V1）
                 if hs['entry_price'] and hs['entry_price'] > 0:
                     if code in close_dict:
                         cur = close_dict[code].get(date)
                         if cur and pd.notna(cur):
-                            asset['cum_return'] = safe_val(float(cur) / hs['entry_price'] - 1, 4)
+                            cum_ret = float(cur) / hs['entry_price'] - 1
+                            asset['cum_return_etf'] = safe_val(cum_ret, 4)
+                            asset['cum_return_idx'] = safe_val(cum_ret, 4)  # V2 暂不区分
                         else:
-                            asset['cum_return'] = None
+                            asset['cum_return_etf'] = None
+                            asset['cum_return_idx'] = None
                     else:
-                        asset['cum_return'] = None
+                        asset['cum_return_etf'] = None
+                        asset['cum_return_idx'] = None
                 else:
-                    asset['cum_return'] = None
+                    asset['cum_return_etf'] = None
-                
+                    asset['cum_return_idx'] = None
-                # 当日收益贡献
-                if code in returns_df.columns:
-                    asset['daily_return'] = safe_val(returns_df.loc[date, code], 6)
-                    asset['return_contribution'] = safe_val(
-                        pos_row.get(code, 0) * returns_df.loc[date, code], 6
-                    )
-                else:
-                    asset['daily_return'] = None
-                    asset['return_contribution'] = None
             else:
                 asset['entry_date'] = None
-                asset['entry_price'] = None
+                asset['entry_price_etf'] = None
+                asset['entry_price_idx'] = None
                 asset['holding_days'] = 0
-                asset['cum_return'] = None
+                asset['cum_return_etf'] = None
-                asset['daily_return'] = None
+                asset['cum_return_idx'] = None
-                asset['return_contribution'] = None
             
             assets[code] = asset
         
@@ -269,32 +332,30 @@ def main():
         days_list.append(day_record)
         prev_holdings = current_holdings
     
-    # 10. 构建元数据
+    # 10. 构建元数据（兼容 V1 格式）
     codes_meta = {}
-    for signal_code, asset in config.asset_pools.assets.items():
+    for code in all_codes:
-        codes_meta[signal_code] = {
+        asset_config = config.asset_pools.assets.get(code)
-            'name': asset.name,
+        codes_meta[code] = {
-            'etf': asset.trade_source,
+            'name': asset_config.name if asset_config else code,
-            'group': asset.group
+            'etf': asset_config.trade_source if asset_config else None,
+            'market': asset_config.group if asset_config else None  # V1 使用 market 字段
         }
     
     output = {
         'meta': {
-            'version': 'V2',
+            'mode': 'V2: 指数信号 + ETF收益',
-            'strategy': 'GlobalRotationStrategy',
-            'mode': '指数信号 + ETF收益',
             'start_date': common_dates[0].strftime('%Y-%m-%d'),
             'end_date': common_dates[-1].strftime('%Y-%m-%d'),
             'total_days': len(common_dates),
+            'select_num': strategy.select_num,
+            'n_days': config.factor.n_days,
             'trade_cost': strategy.trade_cost,
-            'dynamic_threshold': {
+            'bond_threshold': {
                 'enabled': strategy.use_dynamic_threshold,
                 'bond_code': bond_code,
                 'ratio': bond_ratio
             },
-            'diversified': strategy.diversified,
-            'rebalance_count': int(rebalance_count),
-            'final_nav': safe_val(equity_curve.iloc[-1], 4),
             'codes': codes_meta
         },
         'days': days_list

framework_v2/strategies/rotation/rotation.py

@@ -229,9 +229,20 @@ class GlobalRotationStrategy(StrategyBase):
                 top_code = date_factors.idxmax()
                 selected_codes.append(top_code)
             
-            # 标记信号
+            # 第二步：从所有 group 的 Top 1 中，按动量再选 Top select_num 个
             if selected_codes:
-                signals.loc[date, selected_codes] = 1
+                # 获取这些标的的当日因子值
+                candidate_factors = factor_df.loc[date][selected_codes].dropna()
+                
+                if not candidate_factors.empty:
+                    # 按动量排序，选 Top select_num
+                    if len(candidate_factors) > self.select_num:
+                        final_selected = candidate_factors.nlargest(self.select_num).index.tolist()
+                    else:
+                        final_selected = candidate_factors.index.tolist()
+                    
+                    # 标记信号
+                    signals.loc[date, final_selected] = 1
         
         return signals.astype(int)