refactor: 归档旧代码，保留新框架结构

归档内容：
- core/ (数据源、因子计算、通用工具) → archive/legacy_core/
- strategies/rotation/engine.py, portfolio.py, report.py → archive/legacy_core/
- scripts/ (run_rotation, daily_scheduler) → archive/legacy_scripts/
- examples/ → archive/legacy_examples/
- tests/ (实验、对比测试) → archive/legacy_tests/
- 单独文件 (fetch_*.py, 动量.py, 全球市场.py等) → archive/single_files/

保留新结构：
- framework/ (抽象接口)
- strategies/shared/ (定制组件)
- strategies/rotation/strategy.py (新策略)
- 外层配置: .env, .dockerignore, build-and-push.sh, hk_ecs.pem, README.md, requirements.txt
- Docker相关: Dockerfile, Dockerfile_base, docker-compose.yml

更新README反映新框架架构

archive/single_files/fetch_159930.py

@@ -0,0 +1,183 @@
+#!/usr/bin/env python3
+"""
+获取159930 ETF最新10天的收盘价、净值并计算溢价率
+"""
+
+import os
+import pandas as pd
+import tushare as ts
+from datetime import datetime, timedelta
+
+# 设置Tushare token
+def get_tushare_token():
+    # 首先尝试从环境变量获取
+    token = os.environ.get("TUSHARE_TOKEN")
+    if token:
+        return token
+    
+    # 尝试从.env文件获取
+    try:
+        from dotenv import load_dotenv
+        load_dotenv()
+        token = os.environ.get("TUSHARE_TOKEN")
+        if token:
+            return token
+    except ImportError:
+        pass
+    
+    # 手动读取.env文件
+    env_path = os.path.join(os.path.dirname(__file__), '.env')
+    if os.path.exists(env_path):
+        with open(env_path, 'r') as f:
+            for line in f:
+                if line.startswith('TUSHARE_TOKEN='):
+                    token = line.strip().split('=', 1)[1].strip().strip('"').strip("'")
+                    if token:
+                        return token
+    
+    raise ValueError("请设置 TUSHARE_TOKEN 环境变量或在.env文件中配置")
+
+
+def fetch_etf_data(etf_code: str, days: int = 10):
+    """
+    获取ETF最新N天的价格、净值数据
+    
+    Args:
+        etf_code: ETF代码，如 "159930.SZ"
+        days: 获取天数
+    
+    Returns:
+        DataFrame: 包含日期、收盘价、净值、溢价率
+    """
+    pro = ts.pro_api(get_tushare_token())
+    
+    # 计算日期范围（多取几天确保有足够数据）
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days + 5)
+    
+    start_str = start_date.strftime('%Y%m%d')
+    end_str = end_date.strftime('%Y%m%d')
+    
+    # 转换代码格式
+    ts_code = etf_code.replace(".SS", ".SH")
+    
+    print(f"获取 {etf_code} 数据...")
+    print(f"日期范围: {start_str} ~ {end_str}")
+    
+    # 1. 获取ETF价格数据（fund_daily接口）
+    print("\n1. 获取ETF价格数据...")
+    try:
+        price_df = pro.fund_daily(
+            ts_code=ts_code,
+            start_date=start_str,
+            end_date=end_str
+        )
+        if price_df is not None and len(price_df) > 0:
+            price_df = price_df.sort_values('trade_date')
+            print(f"   获取到 {len(price_df)} 条价格数据")
+            print(f"   最新日期: {price_df['trade_date'].max()}")
+        else:
+            print("   未获取到价格数据")
+            price_df = None
+    except Exception as e:
+        print(f"   获取价格数据失败: {e}")
+        price_df = None
+    
+    # 2. 获取ETF净值数据（fund_nav接口）
+    print("\n2. 获取ETF净值数据...")
+    try:
+        # 净值通常滞后，多取一天
+        nav_end_date = end_date + timedelta(days=1)
+        nav_end_str = nav_end_date.strftime('%Y%m%d')
+        
+        nav_df = pro.fund_nav(
+            ts_code=ts_code,
+            start_date=start_str,
+            end_date=nav_end_str
+        )
+        if nav_df is not None and len(nav_df) > 0:
+            nav_df = nav_df.sort_values('nav_date')
+            print(f"   获取到 {len(nav_df)} 条净值数据")
+            print(f"   最新日期: {nav_df['nav_date'].max()}")
+        else:
+            print("   未获取到净值数据")
+            nav_df = None
+    except Exception as e:
+        print(f"   获取净值数据失败: {e}")
+        nav_df = None
+    
+    # 3. 合并数据并计算溢价率
+    print("\n3. 合并数据并计算溢价率...")
+    
+    if price_df is None:
+        print("错误: 没有价格数据")
+        return None
+    
+    # 准备价格数据
+    price_df['date'] = pd.to_datetime(price_df['trade_date'])
+    price_df = price_df.set_index('date')
+    price_series = price_df['close']
+    
+    # 准备净值数据
+    if nav_df is not None:
+        nav_df['date'] = pd.to_datetime(nav_df['nav_date'])
+        nav_df = nav_df.set_index('date')
+        nav_series = nav_df['unit_nav']
+    else:
+        nav_series = pd.Series()
+    
+    # 创建结果DataFrame
+    result = pd.DataFrame({
+        '收盘价': price_series
+    })
+    
+    # 对齐净值数据（按日期）
+    result = result.join(nav_series.rename('净值'), how='left')
+    
+    # 计算溢价率
+    result['溢价率'] = (result['收盘价'] - result['净值']) / result['净值'] * 100
+    
+    # 取最新N天
+    result = result.tail(days)
+    
+    # 格式化输出
+    result['收盘价'] = result['收盘价'].round(3)
+    result['净值'] = result['净值'].round(3)
+    result['溢价率'] = result['溢价率'].round(2)
+    
+    # 重置索引，将日期作为列
+    result = result.reset_index()
+    result['日期'] = result['date'].dt.strftime('%Y-%m-%d')
+    result = result[['日期', '收盘价', '净值', '溢价率']]
+    
+    return result
+
+
+def main():
+    """主函数"""
+    etf_code = "159930.SZ"
+    days = 10
+    
+    print("=" * 60)
+    print(f"ETF: {etf_code} (中证能源ETF)")
+    print(f"获取最近 {days} 天数据")
+    print("=" * 60)
+    
+    df = fetch_etf_data(etf_code, days)
+    
+    if df is not None and len(df) > 0:
+        print("\n" + "=" * 60)
+        print("结果表格:")
+        print("=" * 60)
+        print(df.to_string(index=False))
+        
+        # 保存到CSV
+        output_file = f"{etf_code.replace('.', '_')}_latest_{days}days.csv"
+        df.to_csv(output_file, index=False, encoding='utf-8-sig')
+        print(f"\n数据已保存到: {output_file}")
+    else:
+        print("\n获取数据失败")
+
+
+if __name__ == "__main__":
+    main()