refactor(archive): move unused modules to archive/

Archive legacy framework and utility modules that are no longer
referenced by the active core (datasource/ and rotation/):

- framework/ -> archive/framework/
- framework_v2/ -> archive/framework_v2/
- strategies/ -> archive/strategies/
- config/ -> archive/config/
- visualization/ -> archive/visualization/
- scripts/ -> archive/scripts/
- tests/ -> archive/tests/
- run_rotation.py, run_us_rotation.py -> archive/single_files/
- compare_*.py, test_api_dates.py -> archive/single_files/

archive/framework_v2/scripts/verify_etf_hfq_fix.py

@@ -0,0 +1,133 @@
+#!/usr/bin/env python3
+"""
+验证 ETF 数据获取修复
+
+测试点：
+1. 指数数据使用 adj='raw'
+2. ETF 数据使用 adj='hfq'
+3. 数据字典中同时包含指数和 ETF
+"""
+
+import sys
+from pathlib import Path
+
+# 添加项目根目录到路径
+project_root = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+from framework_v2.config import load_config
+from framework_v2.strategies.rotation.rotation import GlobalRotationStrategy
+
+
+def main():
+    print("=" * 70)
+    print("   验证 ETF 数据获取修复")
+    print("=" * 70)
+    
+    # 加载配置
+    config_path = project_root / 'framework_v2' / 'config' / 'rotation_global.yaml'
+    print(f"\n加载配置: {config_path}")
+    config = load_config(str(config_path))
+    
+    # 初始化策略
+    strategy = GlobalRotationStrategy(config)
+    
+    # 获取数据
+    print("\n" + "=" * 70)
+    print("获取数据...")
+    print("=" * 70)
+    
+    data = strategy.get_data()
+    
+    # 分析数据结构
+    print("\n" + "=" * 70)
+    print("数据结构分析")
+    print("=" * 70)
+    
+    # 获取映射关系
+    signal_to_trade = config.asset_pools.get_signal_to_trade_mapping()
+    signal_codes = config.asset_pools.get_signal_codes()
+    trade_codes = set(signal_to_trade.values())
+    
+    print(f"\n信号标的（指数）: {len(signal_codes)} 只")
+    for code in sorted(signal_codes):
+        if code in data:
+            df = data[code]
+            has_hfq = 'close_hfq' in df.columns if 'close' in df.columns else False
+            print(f"  ✓ {code}: {len(df)} 条, 有 close_hfq: {has_hfq}")
+        else:
+            print(f"  ✗ {code}: 数据缺失")
+    
+    print(f"\n交易标的（ETF）: {len(trade_codes)} 只")
+    for code in sorted(trade_codes):
+        if code in data:
+            df = data[code]
+            has_nav = 'nav' in df.attrs
+            has_premium = 'premium_series' in df.attrs
+            
+            print(f"  ✓ {code}: {len(df)} 条")
+            print(f"      close (最新): {df['close'].iloc[-1]:.4f}")
+            print(f"      有 nav: {has_nav}")
+            print(f"      有 premium: {has_premium}")
+        else:
+            print(f"  ✗ {code}: 数据缺失")
+    
+    # 验证关键指标
+    print("\n" + "=" * 70)
+    print("验证结果")
+    print("=" * 70)
+    
+    # 检查指数数据
+    index_ok = all(code in data for code in signal_codes)
+    print(f"\n指数数据完整性: {'✓ 全部获取' if index_ok else '✗ 部分缺失'}")
+    
+    # 检查 ETF 数据
+    etf_ok = all(code in data for code in trade_codes)
+    print(f"ETF 数据完整性: {'✓ 全部获取' if etf_ok else '✗ 部分缺失'}")
+    
+    # 检查 ETF 是否使用 hfq（对比 raw 和 hfq 的价格差异）
+    print("\n验证 ETF 是否使用 hfq（抽样检查）...")
+    from framework_v2.shared.data import FlaskAPIFetcher
+    fetcher = FlaskAPIFetcher()
+    
+    etf_hfq_verified = 0
+    sample_codes = list(trade_codes)[:3]  # 抽样前3个
+    
+    # 获取日期范围
+    from datetime import date
+    start = config.backtest.start_date
+    end = config.backtest.end_date
+    if end is None:
+        end = date.today().strftime('%Y-%m-%d')
+    
+    for code in sample_codes:
+        if code in data:
+            hfq_close = data[code]['close'].iloc[-1]
+            
+            # 获取 raw 数据对比
+            raw_df = fetcher._source.fetch(code, start, end, adj='raw', asset_type='china_etf')
+            if raw_df is not None:
+                raw_close = raw_df['close'].iloc[-1]
+                ratio = hfq_close / raw_close if raw_close > 0 else 1
+                
+                if ratio > 1.01:  # 差异超过1%说明使用了 hfq
+                    print(f"  ✓ {code}: raw={raw_close:.4f}, hfq={hfq_close:.4f}, 倍数={ratio:.4f} (正确)")
+                    etf_hfq_verified += 1
+                else:
+                    print(f"  ✗ {code}: raw={raw_close:.4f}, hfq={hfq_close:.4f}, 倍数={ratio:.4f} (错误)")
+    
+    print(f"ETF 使用 hfq: {etf_hfq_verified}/{len(sample_codes)} {'✓ 正确' if etf_hfq_verified == len(sample_codes) else '✗ 错误'}")
+    
+    # 总结
+    print("\n" + "=" * 70)
+    if index_ok and etf_ok and etf_hfq_verified == len(sample_codes):
+        print("✓ 验证通过：数据获取逻辑正确")
+        print("  - 指数使用 raw（原始价格）")
+        print("  - ETF 使用 hfq（后复权价格）")
+    else:
+        print("✗ 验证失败：数据获取存在问题")
+    print("=" * 70)
+
+
+if __name__ == '__main__':
+    main()