feat(strategy): 新增纯美股动量轮动策略

新增美股轮动策略模块:
- strategies/us_rotation/config.yaml: 47只美股标的池，动量窗口250天，Top5选股
- strategies/us_rotation/strategy.py: USRotationStrategy实现
- run_us_rotation.py: 回测入口脚本

回测结果 (2016-2026, 约10年):
- 总收益: 7675% (年化52.49%)
- 基准NDX收益: 540% (年化19.72%)
- 超额年化收益: 32.78%
- 夏普比率: 1.33 (基准0.80)
- 最大回撤: 42.15%
- 卡玛比率: 1.25
- 胜率: 56.1%
- 平均持仓: 2.7天

年度最佳: 2020年+221% (超额176%)
年度防守: 2022年-10.5% (基准-33.7%, 超额+23.3%)

持仓Top5: NVDA(35.8%), AMD(30.1%), SHOP(26%), AVGO(23.9%), FICO(23.3%)

run_us_rotation.py

@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+"""
+美股动量轮动策略回测入口
+
+运行方式:
+    python run_us_rotation.py
+    python run_us_rotation.py --save results/us_rotation
+"""
+
+import argparse
+import time
+from pathlib import Path
+
+from strategies.us_rotation.strategy import USRotationStrategy
+
+
+def main():
+    parser = argparse.ArgumentParser(description='美股动量轮动策略回测')
+    parser.add_argument(
+        '--config',
+        type=str,
+        default='strategies/us_rotation/config.yaml',
+        help='配置文件路径'
+    )
+    parser.add_argument(
+        '--save',
+        type=str,
+        default='results/us_rotation',
+        help='报告保存路径前缀'
+    )
+    
+    args = parser.parse_args()
+    
+    start_time = time.time()
+    
+    print("=" * 60)
+    print("美股动量轮动策略")
+    print("=" * 60)
+    print(f"配置文件: {args.config}")
+    print(f"保存路径: {args.save}")
+    
+    # 创建策略实例
+    strategy = USRotationStrategy.from_yaml(args.config)
+    
+    # 运行回测
+    result = strategy.run_backtest(save_path=args.save)
+    
+    elapsed = time.time() - start_time
+    print(f"\n总耗时: {elapsed:.1f}秒")
+    
+    return result
+
+
+if __name__ == "__main__":
+    main()

strategies/us_rotation/__init__.py

@@ -0,0 +1,7 @@
+"""
+美股轮动策略模块
+"""
+
+from .strategy import USRotationStrategy
+
+__all__ = ['USRotationStrategy']

strategies/us_rotation/config.yaml

@@ -0,0 +1,194 @@
+# 美股轮动策略配置
+
+# ==================== 候选池配置 ====================
+code_list:
+  # 科技巨头
+  "AAPL":
+    name: "Apple"
+    sector: "Technology"
+  "ADBE":
+    name: "Adobe"
+    sector: "Technology"
+  "AMD":
+    name: "AMD"
+    sector: "Technology"
+  "AMZN":
+    name: "Amazon"
+    sector: "Technology"
+  "ASML":
+    name: "ASML"
+    sector: "Technology"
+  "AVGO":
+    name: "Broadcom"
+    sector: "Technology"
+  "CRM":
+    name: "Salesforce"
+    sector: "Technology"
+  "CRWD":
+    name: "CrowdStrike"
+    sector: "Technology"
+  "CSCO":
+    name: "Cisco"
+    sector: "Technology"
+  "FICO":
+    name: "FICO"
+    sector: "Technology"
+  "GOOGL":
+    name: "Google"
+    sector: "Technology"
+  "INTC":
+    name: "Intel"
+    sector: "Technology"
+  "KLAC":
+    name: "KLA"
+    sector: "Technology"
+  "LRCX":
+    name: "Lam Research"
+    sector: "Technology"
+  "META":
+    name: "Meta"
+    sector: "Technology"
+  "MSFT":
+    name: "Microsoft"
+    sector: "Technology"
+  "MU":
+    name: "Micron"
+    sector: "Technology"
+  "NET":
+    name: "Cloudflare"
+    sector: "Technology"
+  "NFLX":
+    name: "Netflix"
+    sector: "Technology"
+  "NVDA":
+    name: "NVIDIA"
+    sector: "Technology"
+  "ORCL":
+    name: "Oracle"
+    sector: "Technology"
+  "PANW":
+    name: "Palo Alto"
+    sector: "Technology"
+  "PLTR":
+    name: "Palantir"
+    sector: "Technology"
+  "QCOM":
+    name: "Qualcomm"
+    sector: "Technology"
+  "SNOW":
+    name: "Snowflake"
+    sector: "Technology"
+  "TSLA":
+    name: "Tesla"
+    sector: "Technology"
+  "TSM":
+    name: "TSMC"
+    sector: "Technology"
+  
+  # 金融
+  "AXP":
+    name: "American Express"
+    sector: "Financial"
+  "BAC":
+    name: "Bank of America"
+    sector: "Financial"
+  "C":
+    name: "Citigroup"
+    sector: "Financial"
+  "GS":
+    name: "Goldman Sachs"
+    sector: "Financial"
+  "JPM":
+    name: "JPMorgan"
+    sector: "Financial"
+  "MA":
+    name: "Mastercard"
+    sector: "Financial"
+  "MS":
+    name: "Morgan Stanley"
+    sector: "Financial"
+  
+  # 消费零售
+  "COST":
+    name: "Costco"
+    sector: "Consumer"
+  "LULU":
+    name: "Lululemon"
+    sector: "Consumer"
+  "PDD":
+    name: "PDD Holdings"
+    sector: "Consumer"
+  "SHOP":
+    name: "Shopify"
+    sector: "Consumer"
+  
+  # 医药健康
+  "LLY":
+    name: "Eli Lilly"
+    sector: "Healthcare"
+  "NVO":
+    name: "Novo Nordisk"
+    sector: "Healthcare"
+  
+  # 其他
+  "CAT":
+    name: "Caterpillar"
+    sector: "Industrial"
+  "COIN":
+    name: "Coinbase"
+    sector: "Crypto"
+  "CRCL":
+    name: "Circle"
+    sector: "Crypto"
+  "FUTU":
+    name: "Futu"
+    sector: "Financial"
+  "HOOD":
+    name: "Robinhood"
+    sector: "Financial"
+  "SAP":
+    name: "SAP"
+    sector: "Technology"
+  "SCCO":
+    name: "Southern Copper"
+    sector: "Materials"
+
+# ==================== 基准配置 ====================
+benchmark:
+  code: "NDX"
+  name: "纳斯达克100"
+
+# ==================== 回测参数 ====================
+start_date: "2016-01-01"
+
+# ==================== 因子参数 ====================
+# 动量窗口（天数）
+n_days: 250
+# 因子类型
+factor_type: "momentum"
+
+# ==================== 轮动参数 ====================
+# 不分组，直接选 Top N
+diversified: false
+select_num: 5
+
+# ==================== 调仓控制 ====================
+# 每日调仓
+rebalance_days: 1
+# 调仓阈值：新组合得分超过当前组合 X% 才触发调仓
+rebalance_threshold: 0.0
+# 交易成本（双边）
+trade_cost: 0.001
+
+# ==================== 数据缓存 ====================
+use_cache: true
+
+# ==================== 数据源配置 ====================
+# SSH 隧道配置（用于 yfinance）
+ssh_tunnel:
+  enabled: true
+  host: "8.218.167.69"
+  port: 22
+  username: "root"
+  key_path: "hk_ecs.pem"
+  local_port: 1080

strategies/us_rotation/strategy.py

@@ -0,0 +1,354 @@
+"""
+美股轮动策略
+
+纯美股轮动策略，使用动量因子选股
+特点：
+- 全部使用 yfinance 数据源
+- 美股交易日历
+- 不分组，直接选 Top 5
+- 基准为纳指 NDX
+"""
+
+import sys
+import yaml
+import time
+import pandas as pd
+from pathlib import Path
+from datetime import datetime
+from typing import Dict, List, Optional, Tuple
+
+# 添加项目根目录
+project_root = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+from datasource.yfinance_source import YFinanceSource
+from datasource.ssh_tunnel import SSHTunnelManager
+from strategies.shared.factors.momentum import MomentumFactor
+from strategies.shared.signals.selectors import TopNSelector
+from framework.execution import BacktestExecutor
+
+
+class USRotationStrategy:
+    """美股轮动策略"""
+    
+    def __init__(self, config_path: str = None, config: dict = None):
+        """
+        初始化策略
+        
+        Args:
+            config_path: 配置文件路径
+            config: 配置字典（可选）
+        """
+        if config_path:
+            with open(config_path, 'r', encoding='utf-8') as f:
+                self.config = yaml.safe_load(f)
+        elif config:
+            self.config = config
+        else:
+            raise ValueError("需要提供 config_path 或 config")
+        
+        # 结束日期默认今天
+        if not self.config.get('end_date'):
+            self.config['end_date'] = datetime.now().strftime('%Y-%m-%d')
+        
+        # 应用配置
+        self._apply_config()
+        
+        # 初始化因子
+        self._factor = MomentumFactor(
+            n_days=self.n_days,
+            weighted=True,
+            crash_filter=True
+        )
+        
+        # 初始化选择器（不分组，直接选 Top N）
+        self._selector = TopNSelector(
+            select_num=self.select_num,
+            rebalance_days=self.rebalance_days,
+            rebalance_threshold=self.rebalance_threshold
+        )
+        
+        # 数据源（延迟初始化）
+        self._yfinance: Optional[YFinanceSource] = None
+        self._tunnel: Optional[SSHTunnelManager] = None
+    
+    @classmethod
+    def from_yaml(cls, config_path: str) -> 'USRotationStrategy':
+        """从 YAML 文件创建策略实例"""
+        return cls(config_path=config_path)
+    
+    def _apply_config(self):
+        """应用配置参数"""
+        self.select_num = self.config.get('select_num', 5)
+        self.n_days = self.config.get('n_days', 250)
+        self.rebalance_days = self.config.get('rebalance_days', 1)
+        self.rebalance_threshold = self.config.get('rebalance_threshold', 0.0)
+        self.trade_cost = self.config.get('trade_cost', 0.001)
+        self.start_date = self.config.get('start_date', '2016-01-01')
+        self.end_date = self.config['end_date']
+        self.use_cache = self.config.get('use_cache', True)
+    
+    def _start_tunnel(self) -> bool:
+        """启动 SSH 隧道"""
+        ssh_config = self.config.get('ssh_tunnel', {})
+        if not ssh_config.get('enabled', False):
+            return True
+        
+        self._tunnel = SSHTunnelManager(ssh_config)
+        return self._tunnel.start()
+    
+    def _stop_tunnel(self):
+        """停止 SSH 隧道"""
+        if self._tunnel:
+            self._tunnel.stop()
+            self._tunnel = None
+    
+    def _init_yfinance(self):
+        """初始化 YFinance 数据源"""
+        if self._yfinance is None:
+            self._yfinance = YFinanceSource(use_ssh_tunnel=True)
+    
+    def fetch_data(self) -> Dict:
+        """获取数据（全部使用 yfinance）"""
+        print("\n" + "=" * 60)
+        print("获取美股数据")
+        print("=" * 60)
+        
+        code_list_config = self.config.get('code_list', {})
+        benchmark_config = self.config.get('benchmark', {})
+        benchmark_code = benchmark_config.get('code', 'NDX')
+        
+        if not code_list_config:
+            raise ValueError("配置中未找到 code_list")
+        
+        codes = list(code_list_config.keys())
+        print(f"标的池: {len(codes)} 只股票")
+        print(f"基准: {benchmark_code}")
+        print(f"时间范围: {self.start_date} ~ {self.end_date}")
+        
+        # 启动 SSH 隓道
+        print("\n启动 SSH 隧道...")
+        if not self._start_tunnel():
+            raise RuntimeError("SSH 隧道启动失败")
+        
+        self._init_yfinance()
+        
+        # 获取数据
+        all_data: Dict[str, pd.DataFrame] = {}
+        valid_codes: List[str] = []
+        
+        print("\n获取股票数据...")
+        for i, code in enumerate(codes):
+            print(f"  [{i+1}/{len(codes)}] {code}...", end=" ")
+            
+            try:
+                df = self._yfinance.fetch(code, self.start_date, self.end_date)
+                time.sleep(0.5)  # 避免限流
+                
+                if df is not None and len(df) >= self.n_days:
+                    all_data[code] = df
+                    valid_codes.append(code)
+                    print(f"✓ {len(df)} 条")
+                else:
+                    print(f"✗ 数据不足")
+            except Exception as e:
+                print(f"✗ 失败: {e}")
+        
+        # 获取基准数据
+        print(f"\n获取基准 {benchmark_code}...", end=" ")
+        try:
+            benchmark_df = self._yfinance.fetch(benchmark_code, self.start_date, self.end_date)
+            if benchmark_df is not None and len(benchmark_df) > 0:
+                print(f"✓ {len(benchmark_df)} 条")
+            else:
+                print(f"✗ 基准数据获取失败")
+                benchmark_df = None
+        except Exception as e:
+            print(f"✗ 失败: {e}")
+            benchmark_df = None
+        
+        # 停止隧道
+        self._stop_tunnel()
+        
+        print(f"\n数据获取完成: {len(valid_codes)}/{len(codes)} 只有效")
+        
+        return {
+            'stock_data': all_data,
+            'valid_codes': valid_codes,
+            'benchmark': benchmark_df,
+            'benchmark_code': benchmark_code
+        }
+    
+    def compute_factors(self, data: Dict) -> pd.DataFrame:
+        """计算动量因子"""
+        print("\n" + "=" * 60)
+        print("计算动量因子")
+        print("=" * 60)
+        
+        stock_data = data['stock_data']
+        valid_codes = data['valid_codes']
+        
+        factor_values: Dict[str, pd.Series] = {}
+        
+        for code in valid_codes:
+            df = stock_data[code]
+            
+            if 'close' not in df.columns:
+                continue
+            
+            # 数据长度检查
+            if len(df) < self.n_days:
+                print(f"  ⚠ {code}: 数据不足 {self.n_days} 天，跳过")
+                continue
+            
+            # MomentumFactor.compute 需要DataFrame
+            factor_series = self._factor.compute(df)
+            
+            if factor_series is not None and len(factor_series) > 0:
+                factor_values[code] = factor_series
+        
+        # 合成 DataFrame
+        factor_df = pd.DataFrame(factor_values)
+        
+        print(f"\n因子计算完成: {len(factor_df.columns)} 只标的")
+        print(f"  窗口: {self.n_days} 天")
+        if len(factor_df) > 0:
+            print(f"  日期范围: {factor_df.index.min()} ~ {factor_df.index.max()}")
+        
+        return factor_df
+    
+    def generate_signals(self, factor_df: pd.DataFrame) -> pd.DataFrame:
+        """生成轮动信号（不分组，直接选 Top 5）"""
+        print("\n" + "=" * 60)
+        print("生成轮动信号")
+        print("=" * 60)
+        
+        # 不分组，直接对因子排序选 Top 5
+        # TopNSelector.generate 会自动处理调仓周期和T+1
+        signals_df = self._selector.generate(factor_df)
+        
+        print(f"\n信号生成完成:")
+        print(f"  选股数量: {self.select_num}")
+        if 'signal' in signals_df.columns:
+            valid_signals = signals_df[signals_df['signal'] != '']
+            print(f"  有效信号天数: {len(valid_signals)}")
+        
+        return signals_df
+    
+    def run_backtest(self, data: Dict = None, save_path: str = None) -> Dict:
+        """运行回测"""
+        print("\n" + "=" * 60)
+        print("美股动量轮动策略 回测")
+        print("=" * 60)
+        
+        # 1. 获取数据
+        if data is None:
+            data = self.fetch_data()
+        
+        valid_codes = data['valid_codes']
+        
+        # 2. 计算因子
+        factor_df = self.compute_factors(data)
+        
+        # 3. 生成信号
+        signals = self.generate_signals(factor_df)
+        
+        # 4. 构建收益数据（BacktestExecutor期望列名格式：日收益率_{code}）
+        print("\n构建收益数据...")
+        stock_data = data['stock_data']
+        
+        # 计算日收益率，列名格式为 '日收益率_{code}'
+        returns_data: Dict[str, pd.Series] = {}
+        for code in valid_codes:
+            if code in stock_data:
+                df = stock_data[code]
+                if 'close' in df.columns:
+                    returns_data[f'日收益率_{code}'] = df['close'].pct_change()
+        
+        returns_df = pd.DataFrame(returns_data)
+        
+        # 对齐日期
+        common_dates = signals.index.intersection(returns_df.index)
+        signals = signals.loc[common_dates]
+        returns_df = returns_df.loc[common_dates]
+        
+        # 5. 执行回测
+        print("\n执行回测...")
+        executor = BacktestExecutor(
+            initial_capital=100,
+            trade_cost=self.trade_cost,
+            select_num=self.select_num
+        )
+        
+        portfolio = executor.execute(signals, returns_df)
+        
+        # 6. 计算基准收益
+        benchmark_df = data['benchmark']
+        benchmark_code = data['benchmark_code']
+        
+        if benchmark_df is not None and 'close' in benchmark_df.columns:
+            benchmark_returns = benchmark_df['close'].pct_change()
+            # 对齐日期
+            benchmark_returns = benchmark_returns.loc[common_dates]
+        
+        # 7. 输出结果
+        if hasattr(portfolio, 'backtest_result') and portfolio.backtest_result is not None:
+            result = portfolio.backtest_result
+            
+            # 策略净值（DataFrame列）
+            if '策略净值' in result.columns:
+                strategy_nav = result['策略净值'].values
+                final_nav = strategy_nav[-1] if len(strategy_nav) > 0 else 100
+                total_return = (final_nav - 1) * 100  # 净值归一化起点为1
+            else:
+                final_nav = 100
+                total_return = 0
+            
+            # 基准收益
+            if benchmark_df is not None and 'close' in benchmark_df.columns:
+                benchmark_start = benchmark_df['close'].iloc[0]
+                benchmark_end = benchmark_df['close'].iloc[-1]
+                benchmark_return = (benchmark_end / benchmark_start - 1) * 100
+            else:
+                benchmark_return = 0
+            
+            print("\n" + "=" * 60)
+            print("回测结果")
+            print("=" * 60)
+            print(f"策略最终净值: {final_nav:.2f}")
+            print(f"策略总收益: {total_return:.2f}%")
+            print(f"基准 ({benchmark_code}) 收益: {benchmark_return:.2f}%")
+            print(f"超额收益: {total_return - benchmark_return:.2f}%")
+            print(f"交易成本: {self.trade_cost * 100:.1f}%")
+            
+            # 保存结果
+            if save_path:
+                Path(save_path).parent.mkdir(parents=True, exist_ok=True)
+                
+                # 保存净值曲线
+                if '策略净值' in result.columns:
+                    nav_df = pd.DataFrame({
+                        'date': result.index,
+                        'strategy_nav': result['策略净值'].values
+                    })
+                    if benchmark_df is not None and 'close' in benchmark_df.columns:
+                        # 重建基准净值
+                        benchmark_nav = (benchmark_df['close'].pct_change() + 1).cumprod()
+                        nav_df['benchmark_nav'] = benchmark_nav.reindex(result.index, method='ffill').values
+                    nav_df.to_csv(f"{save_path}_nav.csv", index=False)
+                
+                # 保存信号
+                signals.to_csv(f"{save_path}_signals.csv")
+                
+                print(f"\n报告保存: {save_path}_*.csv")
+            
+            return {
+                'final_nav': final_nav,
+                'total_return': total_return,
+                'benchmark_return': benchmark_return,
+                'excess_return': total_return - benchmark_return,
+                'signals': signals,
+                'result': result
+            }
+        
+        return {'signals': signals}