diff --git a/framework_v2/scripts/export_backtest_detail.py b/framework_v2/scripts/export_backtest_detail.py
new file mode 100644
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+++ b/framework_v2/scripts/export_backtest_detail.py
@@ -0,0 +1,327 @@
+#!/usr/bin/env python3
+"""
+导出 V2 框架回测逐日明细到 JSON，供 HTML 回放器加载。
+
+适用于 GlobalRotationStrategy（V2 正式版）
+- 指数信号 + ETF 收益
+- 动态短债阈值
+- 强制分散化
+- 交易成本
+- CrossMarketAligner 数据对齐
+
+用法:
+    python framework_v2/scripts/export_backtest_detail.py
+"""
+
+import sys
+import json
+import math
+from pathlib import Path
+from datetime import datetime
+
+import numpy as np
+import pandas as pd
+
+project_root = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+from dotenv import load_dotenv
+load_dotenv()
+
+from framework_v2.config import load_config
+from framework_v2.strategies.rotation.rotation import GlobalRotationStrategy
+from framework_v2.shared.data.alignment import CrossMarketAligner
+
+
+# ==================== 辅助函数 ====================
+
+def safe_val(v, decimals=4):
+    """安全转换数值，处理 NaN/Inf"""
+    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 main():
+    print("=" * 80)
+    print("   V2 回测逐日明细导出（GlobalRotationStrategy）")
+    print("=" * 80)
+    
+    # 1. 加载配置
+    config_file = project_root / 'framework_v2' / 'strategies' / 'rotation' / 'config_simple.yaml'
+    print(f"\n[1] 加载配置: {config_file}")
+    config = load_config(str(config_file))
+    
+    # 2. 初始化策略
+    print("[2] 初始化策略...")
+    strategy = GlobalRotationStrategy(config)
+    
+    # 3. 获取数据
+    print("[3] 获取数据...")
+    data = strategy.get_data()
+    print(f"      获取 {len(data)} 个标的")
+    
+    # 4. 计算因子
+    print("[4] 计算因子...")
+    factors = strategy.compute_factors(data)
+    print(f"      计算 {len(factors)} 个因子")
+    
+    # 5. 生成信号
+    print("[5] 生成信号...")
+    signals = strategy.generate_signals(factors)
+    print(f"      生成 {signals.shape[0]} 个信号")
+    
+    # 6. 仓位管理
+    print("[6] 仓位管理...")
+    positions = strategy.manage_positions(signals)
+    
+    # 7. 准备收益率数据（使用 CrossMarketAligner）
+    print("[7] 准备收益率数据...")
+    signal_to_trade = config.asset_pools.get_signal_to_trade_mapping()
+    
+    # 获取 A 股交易日历
+    trading_calendar = strategy._get_trading_calendar()
+    print(f"      A 股交易日: {len(trading_calendar)} 天")
+    
+    # 创建对齐器
+    aligner = CrossMarketAligner(target_calendar=trading_calendar)
+    
+    # 提取收盘价
+    close_dict = {}
+    for signal_code, trade_code in signal_to_trade.items():
+        if trade_code in data:
+            close_dict[signal_code] = data[trade_code]['close']
+    
+    # 对齐收益率
+    returns_df = aligner.align_multi_asset(close_dict)
+    print(f"      收益率数据: {len(returns_df)} 天, {len(returns_df.columns)} 个标的")
+    
+    # 8. 计算策略收益和净值
+    print("[8] 计算策略收益...")
+    positions_aligned = positions.reindex(trading_calendar, method='ffill')
+    positions_delayed = positions_aligned.shift(1).fillna(0)
+    strategy_returns = (positions_delayed * returns_df).sum(axis=1)
+    
+    # 扣除交易成本
+    strategy_returns_clean, rebalance_count = strategy._apply_trade_cost(
+        strategy_returns, positions_aligned
+    )
+    print(f"      调仓次数: {rebalance_count}")
+    
+    # 计算净值
+    equity_curve = (1 + strategy_returns_clean).cumprod()
+    print(f"      最终净值: {equity_curve.iloc[-1]:.4f}")
+    
+    # 9. 构建逐日明细
+    print("[9] 构建逐日明细...")
+    
+    # 因子数据（DataFrame 格式）
+    factor_df = pd.DataFrame(factors)
+    
+    # 持仓状态跟踪
+    holdings_state = {}  # {code: {'entry_date': str, 'entry_price': float}}
+    prev_holdings = set()
+    
+    days_list = []
+    common_dates = equity_curve.index
+    
+    # 获取配置信息
+    bond_code = strategy.bond_code if strategy.use_dynamic_threshold else None
+    bond_ratio = strategy.bond_ratio
+    
+    for i, date in enumerate(common_dates):
+        # 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:
+            # 获取入场价格
+            entry_price = None
+            if code in close_dict:
+                ep = close_dict[code].get(date)
+                if pd.notna(ep):
+                    entry_price = float(ep)
+            
+            holdings_state[code] = {
+                'entry_date': date.strftime('%Y-%m-%d'),
+                'entry_price': entry_price,
+            }
+        
+        # 动态阈值
+        factor_scores = {}
+        if date in factor_df.index:
+            for code in factor_df.columns:
+                v = factor_df.loc[date, code]
+                if pd.notna(v):
+                    factor_scores[code] = float(v)
+        
+        bond_score = factor_scores.get(bond_code) if bond_code else None
+        if bond_score is not None:
+            threshold = bond_score * bond_ratio
+        else:
+            threshold = 0.0
+        
+        # 排名（按动量降序，排除 BOND）
+        groups = config.asset_pools.by_group
+        bond_assets = groups.get('BOND', {})
+        bond_codes = set(bond_assets.keys())
+        
+        non_bond_scores = {k: v for k, v in factor_scores.items() if k not in bond_codes}
+        sorted_codes = sorted(non_bond_scores.keys(), 
+                             key=lambda c: non_bond_scores[c], reverse=True)
+        rank_map = {c: r + 1 for r, c in enumerate(sorted_codes)}
+        
+        # BOND 不参与排名
+        for code in bond_codes:
+            if code in factor_scores:
+                rank_map[code] = None
+        
+        # 每标的详情
+        assets = {}
+        all_codes = factor_df.columns.tolist()
+        
+        for code in all_codes:
+            asset = {}
+            
+            # 动量得分
+            mom = factor_scores.get(code)
+            asset['momentum'] = safe_val(mom, 4)
+            
+            # 排名
+            asset['rank'] = rank_map.get(code)
+            
+            # 阈值
+            asset['threshold'] = safe_val(threshold, 4)
+            asset['above_threshold'] = mom >= threshold if mom is not None else False
+            
+            # 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)
+                
+                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
+                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)
+                        else:
+                            asset['cum_return'] = None
+                    else:
+                        asset['cum_return'] = None
+                else:
+                    asset['cum_return'] = 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['holding_days'] = 0
+                asset['cum_return'] = None
+                asset['daily_return'] = None
+                asset['return_contribution'] = None
+            
+            assets[code] = asset
+        
+        # 构建当天记录
+        nav_val = equity_curve.loc[date] if date in equity_curve.index else None
+        ret_val = strategy_returns_clean.loc[date] if date in strategy_returns_clean.index else None
+        
+        day_record = {
+            'date': date.strftime('%Y-%m-%d'),
+            'nav': safe_val(nav_val, 4),
+            'daily_return': safe_val(ret_val, 6),
+            'is_rebalance': is_rebalance,
+            'holdings': sorted(list(current_holdings)),
+            'added': sorted(added),
+            'removed': sorted(removed),
+            'assets': assets
+        }
+        days_list.append(day_record)
+        prev_holdings = current_holdings
+    
+    # 10. 构建元数据
+    codes_meta = {}
+    for signal_code, asset in config.asset_pools.assets.items():
+        codes_meta[signal_code] = {
+            'name': asset.name,
+            'etf': asset.trade_source,
+            'group': asset.group
+        }
+    
+    output = {
+        'meta': {
+            'version': 'V2',
+            '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),
+            'trade_cost': strategy.trade_cost,
+            'dynamic_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
+    }
+    
+    # 11. 输出
+    output_path = project_root / 'framework_v2' / 'results' / 'backtest_detail_v2.json'
+    print(f"\n[10] 写入 {output_path}...")
+    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"      大小: {file_size_mb:.1f} MB")
+    print(f"      天数: {len(days_list)}")
+    print(f"      标的: {len(all_codes)}")
+    print("      完成!")
+    
+    # 打印汇总统计
+    print("\n" + "=" * 80)
+    print("   回测汇总")
+    print("=" * 80)
+    print(f"  总收益: {(equity_curve.iloc[-1] - 1) * 100:.2f}%")
+    print(f"  年化收益: {((equity_curve.iloc[-1]) ** (252 / len(common_dates)) - 1) * 100:.2f}%")
+    print(f"  调仓次数: {rebalance_count}")
+    print(f"  交易天数: {len(common_dates)}")
+    print(f"  输出文件: {output_path}")
+
+
+if __name__ == '__main__':
+    main()