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%)
This commit is contained in:
55
run_us_rotation.py
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run_us_rotation.py
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#!/usr/bin/env python3
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"""
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美股动量轮动策略回测入口
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运行方式:
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python run_us_rotation.py
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python run_us_rotation.py --save results/us_rotation
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"""
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import argparse
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import time
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from pathlib import Path
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from strategies.us_rotation.strategy import USRotationStrategy
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def main():
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parser = argparse.ArgumentParser(description='美股动量轮动策略回测')
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parser.add_argument(
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'--config',
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type=str,
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default='strategies/us_rotation/config.yaml',
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help='配置文件路径'
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)
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parser.add_argument(
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'--save',
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type=str,
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default='results/us_rotation',
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help='报告保存路径前缀'
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)
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args = parser.parse_args()
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start_time = time.time()
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print("=" * 60)
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print("美股动量轮动策略")
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print("=" * 60)
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print(f"配置文件: {args.config}")
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print(f"保存路径: {args.save}")
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# 创建策略实例
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strategy = USRotationStrategy.from_yaml(args.config)
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# 运行回测
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result = strategy.run_backtest(save_path=args.save)
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elapsed = time.time() - start_time
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print(f"\n总耗时: {elapsed:.1f}秒")
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return result
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if __name__ == "__main__":
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main()
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7
strategies/us_rotation/__init__.py
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strategies/us_rotation/__init__.py
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"""
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美股轮动策略模块
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"""
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from .strategy import USRotationStrategy
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__all__ = ['USRotationStrategy']
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194
strategies/us_rotation/config.yaml
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strategies/us_rotation/config.yaml
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# 美股轮动策略配置
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# ==================== 候选池配置 ====================
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code_list:
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# 科技巨头
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"AAPL":
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name: "Apple"
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sector: "Technology"
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"ADBE":
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name: "Adobe"
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sector: "Technology"
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"AMD":
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name: "AMD"
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sector: "Technology"
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"AMZN":
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name: "Amazon"
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sector: "Technology"
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"ASML":
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name: "ASML"
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sector: "Technology"
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"AVGO":
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name: "Broadcom"
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sector: "Technology"
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"CRM":
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name: "Salesforce"
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sector: "Technology"
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"CRWD":
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name: "CrowdStrike"
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sector: "Technology"
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"CSCO":
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name: "Cisco"
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sector: "Technology"
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"FICO":
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name: "FICO"
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sector: "Technology"
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"GOOGL":
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name: "Google"
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sector: "Technology"
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"INTC":
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name: "Intel"
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sector: "Technology"
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"KLAC":
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name: "KLA"
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sector: "Technology"
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"LRCX":
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name: "Lam Research"
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sector: "Technology"
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"META":
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name: "Meta"
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sector: "Technology"
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"MSFT":
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name: "Microsoft"
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sector: "Technology"
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"MU":
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name: "Micron"
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sector: "Technology"
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"NET":
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name: "Cloudflare"
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sector: "Technology"
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"NFLX":
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name: "Netflix"
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sector: "Technology"
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"NVDA":
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name: "NVIDIA"
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sector: "Technology"
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"ORCL":
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name: "Oracle"
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sector: "Technology"
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"PANW":
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name: "Palo Alto"
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sector: "Technology"
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"PLTR":
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name: "Palantir"
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sector: "Technology"
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"QCOM":
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name: "Qualcomm"
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sector: "Technology"
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"SNOW":
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name: "Snowflake"
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sector: "Technology"
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"TSLA":
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name: "Tesla"
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sector: "Technology"
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"TSM":
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name: "TSMC"
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sector: "Technology"
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# 金融
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"AXP":
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name: "American Express"
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sector: "Financial"
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"BAC":
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name: "Bank of America"
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sector: "Financial"
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"C":
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name: "Citigroup"
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sector: "Financial"
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"GS":
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name: "Goldman Sachs"
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sector: "Financial"
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"JPM":
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name: "JPMorgan"
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sector: "Financial"
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"MA":
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name: "Mastercard"
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sector: "Financial"
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"MS":
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name: "Morgan Stanley"
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sector: "Financial"
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# 消费零售
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"COST":
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name: "Costco"
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sector: "Consumer"
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"LULU":
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name: "Lululemon"
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sector: "Consumer"
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"PDD":
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name: "PDD Holdings"
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sector: "Consumer"
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"SHOP":
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name: "Shopify"
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sector: "Consumer"
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# 医药健康
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"LLY":
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name: "Eli Lilly"
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sector: "Healthcare"
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"NVO":
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name: "Novo Nordisk"
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sector: "Healthcare"
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# 其他
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"CAT":
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name: "Caterpillar"
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sector: "Industrial"
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"COIN":
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name: "Coinbase"
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sector: "Crypto"
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"CRCL":
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name: "Circle"
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sector: "Crypto"
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"FUTU":
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name: "Futu"
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sector: "Financial"
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"HOOD":
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name: "Robinhood"
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sector: "Financial"
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"SAP":
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name: "SAP"
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sector: "Technology"
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"SCCO":
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name: "Southern Copper"
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sector: "Materials"
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# ==================== 基准配置 ====================
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benchmark:
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code: "NDX"
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name: "纳斯达克100"
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# ==================== 回测参数 ====================
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start_date: "2016-01-01"
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# ==================== 因子参数 ====================
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# 动量窗口（天数）
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n_days: 250
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# 因子类型
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factor_type: "momentum"
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# ==================== 轮动参数 ====================
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# 不分组，直接选 Top N
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diversified: false
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select_num: 5
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# ==================== 调仓控制 ====================
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# 每日调仓
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rebalance_days: 1
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# 调仓阈值：新组合得分超过当前组合 X% 才触发调仓
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rebalance_threshold: 0.0
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# 交易成本（双边）
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trade_cost: 0.001
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# ==================== 数据缓存 ====================
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use_cache: true
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# ==================== 数据源配置 ====================
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# SSH 隧道配置（用于 yfinance）
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ssh_tunnel:
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enabled: true
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host: "8.218.167.69"
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port: 22
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username: "root"
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key_path: "hk_ecs.pem"
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local_port: 1080
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354
strategies/us_rotation/strategy.py
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354
strategies/us_rotation/strategy.py
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"""
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美股轮动策略
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纯美股轮动策略，使用动量因子选股
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特点：
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- 全部使用 yfinance 数据源
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- 美股交易日历
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- 不分组，直接选 Top 5
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- 基准为纳指 NDX
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"""
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import sys
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import yaml
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import time
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import pandas as pd
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from pathlib import Path
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from datetime import datetime
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from typing import Dict, List, Optional, Tuple
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# 添加项目根目录
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project_root = Path(__file__).parent.parent.parent
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sys.path.insert(0, str(project_root))
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from datasource.yfinance_source import YFinanceSource
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from datasource.ssh_tunnel import SSHTunnelManager
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from strategies.shared.factors.momentum import MomentumFactor
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from strategies.shared.signals.selectors import TopNSelector
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from framework.execution import BacktestExecutor
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class USRotationStrategy:
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"""美股轮动策略"""
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def __init__(self, config_path: str = None, config: dict = None):
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"""
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初始化策略
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Args:
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config_path: 配置文件路径
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config: 配置字典（可选）
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"""
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if config_path:
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with open(config_path, 'r', encoding='utf-8') as f:
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self.config = yaml.safe_load(f)
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elif config:
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self.config = config
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else:
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raise ValueError("需要提供 config_path 或 config")
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# 结束日期默认今天
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if not self.config.get('end_date'):
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self.config['end_date'] = datetime.now().strftime('%Y-%m-%d')
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# 应用配置
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self._apply_config()
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# 初始化因子
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self._factor = MomentumFactor(
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n_days=self.n_days,
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weighted=True,
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crash_filter=True
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)
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# 初始化选择器（不分组，直接选 Top N）
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self._selector = TopNSelector(
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select_num=self.select_num,
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rebalance_days=self.rebalance_days,
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rebalance_threshold=self.rebalance_threshold
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)
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# 数据源（延迟初始化）
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self._yfinance: Optional[YFinanceSource] = None
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self._tunnel: Optional[SSHTunnelManager] = None
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@classmethod
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def from_yaml(cls, config_path: str) -> 'USRotationStrategy':
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"""从 YAML 文件创建策略实例"""
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return cls(config_path=config_path)
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def _apply_config(self):
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"""应用配置参数"""
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self.select_num = self.config.get('select_num', 5)
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self.n_days = self.config.get('n_days', 250)
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self.rebalance_days = self.config.get('rebalance_days', 1)
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self.rebalance_threshold = self.config.get('rebalance_threshold', 0.0)
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self.trade_cost = self.config.get('trade_cost', 0.001)
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self.start_date = self.config.get('start_date', '2016-01-01')
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self.end_date = self.config['end_date']
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self.use_cache = self.config.get('use_cache', True)
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def _start_tunnel(self) -> bool:
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"""启动 SSH 隧道"""
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ssh_config = self.config.get('ssh_tunnel', {})
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if not ssh_config.get('enabled', False):
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return True
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self._tunnel = SSHTunnelManager(ssh_config)
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return self._tunnel.start()
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def _stop_tunnel(self):
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"""停止 SSH 隧道"""
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if self._tunnel:
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self._tunnel.stop()
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self._tunnel = None
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def _init_yfinance(self):
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"""初始化 YFinance 数据源"""
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if self._yfinance is None:
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self._yfinance = YFinanceSource(use_ssh_tunnel=True)
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def fetch_data(self) -> Dict:
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"""获取数据（全部使用 yfinance）"""
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print("\n" + "=" * 60)
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print("获取美股数据")
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print("=" * 60)
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code_list_config = self.config.get('code_list', {})
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benchmark_config = self.config.get('benchmark', {})
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benchmark_code = benchmark_config.get('code', 'NDX')
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if not code_list_config:
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raise ValueError("配置中未找到 code_list")
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codes = list(code_list_config.keys())
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print(f"标的池: {len(codes)} 只股票")
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print(f"基准: {benchmark_code}")
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print(f"时间范围: {self.start_date} ~ {self.end_date}")
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# 启动 SSH 隓道
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print("\n启动 SSH 隧道...")
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if not self._start_tunnel():
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raise RuntimeError("SSH 隧道启动失败")
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self._init_yfinance()
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# 获取数据
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all_data: Dict[str, pd.DataFrame] = {}
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valid_codes: List[str] = []
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print("\n获取股票数据...")
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for i, code in enumerate(codes):
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print(f" [{i+1}/{len(codes)}] {code}...", end=" ")
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try:
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df = self._yfinance.fetch(code, self.start_date, self.end_date)
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time.sleep(0.5) # 避免限流
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if df is not None and len(df) >= self.n_days:
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all_data[code] = df
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valid_codes.append(code)
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print(f"✓ {len(df)} 条")
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else:
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print(f"✗ 数据不足")
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except Exception as e:
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print(f"✗ 失败: {e}")
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# 获取基准数据
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print(f"\n获取基准 {benchmark_code}...", end=" ")
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try:
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benchmark_df = self._yfinance.fetch(benchmark_code, self.start_date, self.end_date)
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if benchmark_df is not None and len(benchmark_df) > 0:
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print(f"✓ {len(benchmark_df)} 条")
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else:
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print(f"✗ 基准数据获取失败")
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benchmark_df = None
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except Exception as e:
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print(f"✗ 失败: {e}")
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benchmark_df = None
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# 停止隧道
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self._stop_tunnel()
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print(f"\n数据获取完成: {len(valid_codes)}/{len(codes)} 只有效")
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return {
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'stock_data': all_data,
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'valid_codes': valid_codes,
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'benchmark': benchmark_df,
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'benchmark_code': benchmark_code
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}
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def compute_factors(self, data: Dict) -> pd.DataFrame:
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"""计算动量因子"""
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print("\n" + "=" * 60)
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print("计算动量因子")
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print("=" * 60)
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stock_data = data['stock_data']
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valid_codes = data['valid_codes']
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factor_values: Dict[str, pd.Series] = {}
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for code in valid_codes:
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df = stock_data[code]
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if 'close' not in df.columns:
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continue
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# 数据长度检查
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if len(df) < self.n_days:
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print(f" ⚠ {code}: 数据不足 {self.n_days} 天，跳过")
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continue
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# MomentumFactor.compute 需要DataFrame
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factor_series = self._factor.compute(df)
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if factor_series is not None and len(factor_series) > 0:
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factor_values[code] = factor_series
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# 合成 DataFrame
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factor_df = pd.DataFrame(factor_values)
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print(f"\n因子计算完成: {len(factor_df.columns)} 只标的")
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print(f" 窗口: {self.n_days} 天")
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if len(factor_df) > 0:
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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}
|
||||
Reference in New Issue
Block a user