experiment(rotation): 同大类扩充与纳指vs标普替换对比实验
技术修复： - SOCKS5代理IPv6问题：socks5:// → socks5h:// (hybrid_source.py, yfinance_source.py) 目录整理： - scripts/ → 仅保留策略入口(daily_scheduler, run_rotation, run_cci_screener) - 实验脚本移至 tests/experiments/ - 工具脚本移至 tests/utils/ - 实验记录新增 docs/experiments/ - results/ 添加到 gitignore 实验结果： 实验001 - 同大类扩充（添加标普500）： ├─ 累计收益: 1467.35% → 1176.26% (-291%) ├─ CAGR: 48.10% → 43.82% (-4.28%) ├─ 调仓次数: 459 → 501 (+42次) └─ 结论: 添加同大类标的不增加跨类分散，反而侵蚀收益 实验002 - 纳指vs标普替换对比： ├─ 累计收益: 1467.35% → 1118.77% (-348%) ├─ CAGR: 48.10% → 42.87% (-5.22%) ├─ Sharpe: 2.21 → 2.08 (-0.13) ├─ MaxDD: -17.33% → -15.14% (+2.18%) └─ 结论: 纳指100优于标普500，成长风格更适合动量策略 策略建议： - 保持纳指100作为美股大类代表 - 不添加同大类新标的（避免类内切换成本） - 新增标的应优先考虑新大类（增加跨类分散）
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tests/experiments/ab_test_ndx_vs_spx.py
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tests/experiments/ab_test_ndx_vs_spx.py
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"""
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A/B测试：纳指100 vs 标普500 替换对比
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对比：
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- A组（对照组）：纳指100作为美股大类代表
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- B组（实验组）：标普500替换纳指100作为美股大类代表
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核心问题：替换后对策略绩效的影响（无类内竞争）
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"""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from strategies.rotation.engine import RotationStrategy
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import pandas as pd
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import yaml
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def create_config_replace_ndx_with_spx(base_config: dict) -> dict:
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"""将纳指100替换为标普500"""
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config = base_config.copy()
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config['code_list'] = base_config['code_list'].copy()
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# 移除纳指100
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if 'NDX' in config['code_list']:
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del config['code_list']['NDX']
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# 添加标普500（替换纳指100）
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config['code_list']['SPX'] = {
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'name': '标普500',
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'etf': '513500.SH',
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'market': 'US'
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}
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return config
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def run_backtest(config: dict, label: str) -> dict:
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"""运行回测并返回关键指标"""
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print(f"\n{'='*60}")
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print(f" {label}")
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print(f"{'='*60}")
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strategy = RotationStrategy(config)
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result = strategy.run()
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if result is None or len(result) == 0:
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return None
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# 计算指标
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strategy_nav = result['轮动策略净值']
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strategy_ret = result['轮动策略日收益率']
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total_return = strategy_nav.iloc[-1] - 1
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days = len(result)
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years = days / 250
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cagr = (strategy_nav.iloc[-1] ** (1/years)) - 1
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excess_ret = strategy_ret.mean() * 250
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vol = strategy_ret.std() * (250 ** 0.5)
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sharpe = excess_ret / vol if vol > 0 else 0
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rolling_max = strategy_nav.cummax()
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drawdown = (strategy_nav - rolling_max) / rolling_max
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max_dd = drawdown.min()
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calmar = cagr / abs(max_dd) if max_dd < 0 else 0
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win_rate = (strategy_ret > 0).sum() / len(strategy_ret)
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metrics = {
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'label': label,
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'美股标的': '纳指100' if 'NDX' in config['code_list'] else '标普500',
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'累计收益': total_return,
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'CAGR': cagr,
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'Sharpe': sharpe,
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'MaxDD': max_dd,
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'Calmar': calmar,
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'日胜率': win_rate,
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}
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print(f"\n美股代表: {metrics['美股标的']}")
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print(f"累计收益: {metrics['累计收益']:.2%}")
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print(f"CAGR: {metrics['CAGR']:.2%}")
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print(f"Sharpe: {metrics['Sharpe']:.2f}")
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print(f"MaxDD: {metrics['MaxDD']:.2%}")
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print(f"Calmar: {metrics['Calmar']:.2f}")
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print(f"日胜率: {metrics['日胜率']:.2%}")
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return metrics
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def compare_results(a_metrics: dict, b_metrics: dict):
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"""对比两组结果"""
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print(f"\n{'='*60}")
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print(f" 对比结果")
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print(f"{'='*60}")
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print(f"\n{'指标':<15} {'A组(纳指100)':<15} {'B组(标普500)':<15} {'差异':<15}")
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print("-" * 60)
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metrics_keys = ['美股标的', '累计收益', 'CAGR', 'Sharpe', 'MaxDD', 'Calmar', '日胜率']
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for key in metrics_keys:
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a_val = a_metrics.get(key, 0)
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b_val = b_metrics.get(key, 0)
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if key == '美股标的':
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print(f"{key:<15} {a_val:<15} {b_val:<15} {'替换':<15}")
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continue
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diff = b_val - a_val
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if key in ['累计收益', 'CAGR', 'MaxDD', '日胜率']:
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a_str = f"{a_val:.2%}"
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b_str = f"{b_val:.2%}"
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diff_str = f"{diff*100:+.2f}%"
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else:
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a_str = f"{a_val:.2f}"
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b_str = f"{b_val:.2f}"
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diff_str = f"{diff:+.2f}"
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print(f"{key:<15} {a_str:<15} {b_str:<15} {diff_str:<15}")
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print("-" * 60)
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print(f"\n【关键发现】")
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print(f"纳指100 → 标普500 替换效果：")
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if b_metrics['CAGR'] < a_metrics['CAGR']:
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print(f" - CAGR下降 {a_metrics['CAGR'] - b_metrics['CAGR']:.2%}")
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print(f" → 标普500动量信号可能不如纳指强")
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if b_metrics['MaxDD'] > a_metrics['MaxDD']: # 注意MaxDD是负数
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print(f" - MaxDD改善 {b_metrics['MaxDD'] - a_metrics['MaxDD']:.2%}")
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print(f" → 标普500更稳定，回撤更小")
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if b_metrics['Sharpe'] > a_metrics['Sharpe']:
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print(f" - Sharpe改善 {b_metrics['Sharpe'] - a_metrics['Sharpe']:.2f}")
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print(f" → 标普500风险调整后收益更优")
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elif b_metrics['Sharpe'] < a_metrics['Sharpe']:
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print(f" - Sharpe下降 {b_metrics['Sharpe'] - a_metrics['Sharpe']:.2f}")
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print(f" → 纳指100风险调整后收益更优")
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print(f"\n【策略建议】")
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if b_metrics['累计收益'] < a_metrics['累计收益'] * 0.9:
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print(f" 建议：保持纳指100（成长风格更适合动量策略）")
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elif b_metrics['Sharpe'] > a_metrics['Sharpe']:
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print(f" 建议：考虑标普500（更稳定、风险调整收益更优）")
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else:
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print(f" 建议：保持纳指100（累计收益更高）")
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def main():
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"""主函数"""
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# 加载基础配置
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config_path = Path(__file__).parent.parent / 'config' / 'strategies' / 'rotation.yaml'
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with open(config_path, 'r') as f:
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base_config = yaml.safe_load(f)
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# 添加 end_date
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from datetime import datetime
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base_config['end_date'] = datetime.now().strftime('%Y-%m-%d')
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print(f"\n{'='*60}")
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print(f" A/B测试：纳指100 vs 标普500 替换对比")
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print(f"{'='*60}")
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print(f"\n研究问题：")
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print(f" - 将美股大类代表从纳指100替换为标普500")
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print(f" - 无类内竞争（每大类还是1只）")
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print(f" - 评估标的特性变化对绩效的影响")
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# A组：纳指100（当前配置）
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a_metrics = run_backtest(base_config, "A组: 纳指100作为美股代表")
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# B组：标普500替换纳指100
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config_replace = create_config_replace_ndx_with_spx(base_config)
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b_metrics = run_backtest(config_replace, "B组: 标普500替换纳指100")
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# 对比
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if a_metrics and b_metrics:
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compare_results(a_metrics, b_metrics)
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# 保存结果
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results_df = pd.DataFrame([a_metrics, b_metrics])
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results_path = Path(__file__).parent.parent / 'results' / 'ab_test_ndx_vs_spx.csv'
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results_df.to_csv(results_path, index=False)
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print(f"\n对比结果已保存: {results_path}")
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if __name__ == '__main__':
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main()
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