test(experiments): add France CAC40 and SEA ETF experiments
- Add France CAC40 market test (004) - Add SEA ETF limited test (005) - Add France in EU category test (006) - Update experiment README with new results - Modify emerging market test description
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tests/experiments/ab_test_sea_etf.py
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tests/experiments/ab_test_sea_etf.py
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"""
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A/B测试：添加东南亚科技ETF的影响（受限测试）
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对比：
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- A组（对照组）：当前配置（无东南亚）
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- B组（实验组）：添加东南亚科技ETF
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限制说明：
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- 东南亚科技ETF（513730.SH）2023年12月上市，数据仅约2年
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- 新交所泛东南亚科技指数在YFinance中暂无数据
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- 本次测试使用ETF价格作为信号源（非最佳实践，仅作参考）
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- 回测时间范围将被缩短
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核心问题：新兴市场ETF流动性是否优于印度LOF
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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.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_with_sea(base_config: dict) -> dict:
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"""在基础配置上添加东南亚科技"""
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config = base_config.copy()
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config['code_list'] = base_config['code_list'].copy()
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# 添加东南亚科技（新大类）
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# 注意：由于指数数据不可用，使用ETF价格作为信号源
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# 513730.SH 同时作为指数代码和ETF代码
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config['code_list']['513730.SH'] = {
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'name': '东南亚科技',
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'etf': '513730.SH', # 华泰柏瑞东南亚科技ETF
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'market': 'SEA' # 东南亚大类
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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 if years > 0 else 0
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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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# 统计大类数量
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markets = set()
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for code_info in config['code_list'].values():
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markets.add(code_info.get('market', 'A'))
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metrics = {
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'label': label,
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'大类数量': len(markets),
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'回测天数': days,
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'回测年数': years,
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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大类数量: {metrics['大类数量']}")
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print(f"回测天数: {metrics['回测天数']}")
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print(f"回测年数: {metrics['回测年数']:.2f}")
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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(无东南亚)':<15} {'B组(有东南亚)':<15} {'差异':<15}")
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print("-" * 60)
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metrics_keys = ['大类数量', '回测天数', '回测年数', '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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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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elif key in ['大类数量', '回测天数']:
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a_str = str(int(a_val))
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b_str = str(int(b_val))
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diff_str = f"+{int(diff)}" if diff > 0 else str(int(diff))
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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【限制说明】")
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print(f" ⚠ 本次测试数据量受限（东南亚ETF仅2年数据）")
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print(f" ⚠ 使用ETF价格作为信号源（指数数据暂不可用）")
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print(f" ⚠ 结果仅供参考，不建议直接用于决策")
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print(f"\n【关键发现】")
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if b_metrics['大类数量'] > a_metrics['大类数量']:
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print(f" ✓ 大类数量增加 {b_metrics['大类数量'] - a_metrics['大类数量']}")
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if b_metrics['累计收益'] > a_metrics['累计收益']:
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print(f" ✓ 累计收益提升 {b_metrics['累计收益'] - a_metrics['累计收益']:.2%}")
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else:
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print(f" ✗ 累计收益下降 {a_metrics['累计收益'] - b_metrics['累计收益']:.2%}")
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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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else:
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print(f" ✗ Sharpe下降 {a_metrics['Sharpe'] - b_metrics['Sharpe']:.2f}")
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print(f"\n【策略建议】")
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print(f" 建议：等待东南亚科技ETF积累更多数据后再测试")
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print(f" 原因：")
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print(f" 1. 数据量不足（仅{b_metrics['回测年数']:.1f}年）")
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print(f" 2. 指数信号源暂不可用")
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print(f" 3. ETF价格作为信号源存在溢价干扰")
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def main():
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"""主函数"""
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config_path = Path(__file__).parent.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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# 设置回测结束日期
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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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# ⚠ 重要：由于东南亚ETF数据从2023年12月开始
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# 需要调整start_date以匹配数据可用范围
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# 本次测试将使用较短的时间窗口
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print(f"\n{'='*60}")
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print(f" A/B测试：添加东南亚科技ETF（受限测试）")
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print(f"{'='*60}")
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print(f"\n⚠ 限制说明：")
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print(f" - 东南亚科技ETF（513730.SH）2023年12月上市")
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print(f" - 数据仅约2年，回测时间范围受限")
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print(f" - 指数数据暂不可用，使用ETF价格作为信号源")
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print(f" - 结果仅供参考，不建议直接用于决策")
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# A组：当前配置（使用较短时间窗口）
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config_a = base_config.copy()
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config_a['start_date'] = '2024-01-01' # 调整为东南亚ETF有数据的起始时间
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a_metrics = run_backtest(config_a, "A组: 当前配置（2024年起）")
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# B组：添加东南亚科技
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config_b = create_config_with_sea(base_config)
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config_b['start_date'] = '2024-01-01'
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b_metrics = run_backtest(config_b, "B组: 添加东南亚科技")
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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.parent / 'results' / 'ab_test_sea_etf.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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