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_france_in_eu.py
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tests/experiments/ab_test_france_in_eu.py
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"""
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A/B测试：法国CAC40与德国DAX类内竞争对比
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对比：
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- A组（对照组）：仅德国DAX代表欧洲(EU)
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- B组（实验组）：德国DAX + 法国CAC40 同属EU大类（类内竞争）
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核心问题：
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- 法国放在欧洲大类下（market='EU'），与德国竞争
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- diversified模式下，EU大类只输出1个冠军
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- 验证：类内竞争选择最优，而非盲目增加大类
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对比003/004实验：
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- 003/004：法国/印度作为独立大类 → 收益下降
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- 本次：法国放EU大类内 → 类内竞争，不增加大类数量
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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_france_in_eu(base_config: dict) -> dict:
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"""在基础配置上添加法国，但放入EU大类"""
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config = base_config.copy()
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config['code_list'] = base_config['code_list'].copy()
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# 添加法国CAC40，但market='EU'（与德国DAX同大类）
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# diversified模式下：德国 vs 法国 → 选动量最强的1个进入Top3
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config['code_list']['^FCHI'] = {
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'name': '法国CAC40',
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'etf': '513080.SH',
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'market': 'EU' # 与德国DAX同属欧洲大类
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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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# 统计EU大类标的数量
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eu_count = sum(1 for c in config['code_list'].values() if c.get('market') == 'EU')
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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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'EU大类标的数': eu_count,
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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"EU大类标的数: {metrics['EU大类标的数']}")
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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(仅德国)':<15} {'B组(德国+法国)':<15} {'差异':<15}")
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print("-" * 60)
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metrics_keys = ['大类数量', 'EU大类标的数', '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', 'EU大类标的数']:
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a_str = str(a_val)
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b_str = str(b_val)
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diff_str = f"+{diff}" if diff > 0 else str(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【关键发现】")
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print(f"法国放入EU大类（与德国类内竞争）：")
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if b_metrics['大类数量'] == a_metrics['大类数量']:
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print(f" ✓ 大类数量不变（{b_metrics['大类数量']}大类）")
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print(f" → 不增加跨类分散，避免占用Top3权重")
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if b_metrics['EU大类标的数'] > a_metrics['EU大类标的数']:
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print(f" ✓ EU大类标的增加 {b_metrics['EU大类标的数'] - a_metrics['EU大类标的数']}")
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print(f" → 德国 vs 法国 类内竞争，选动量最强")
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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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print(f" → 类内竞争选择最优，比新大类添加更有效")
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elif b_metrics['累计收益'] < a_metrics['累计收益']:
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loss = a_metrics['累计收益'] - b_metrics['累计收益']
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print(f" ✗ 累计收益下降 {loss:.2%}")
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if loss < 0.05: # 下降5%以内
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print(f" → 收益损失较小（vs 004实验下降167%）")
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else:
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print(f" → 法国动量信号不如德国，类内竞争仍选德国")
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print(f"\n【与004实验对比】")
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print(f" 004实验（法国作为独立FR大类）：")
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print(f" ├─ 大类数量: 7 → 8 (+1)")
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print(f" └─ 累计收益: -166.80%")
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print(f" ")
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print(f" 本次实验（法国放入EU大类）：")
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print(f" ├─ 大类数量: 保持{b_metrics['大类数量']}不变")
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print(f" ├─ EU类内竞争：德国DAX vs 法国CAC40")
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print(f" └─ 累计收益变化: {b_metrics['累计收益'] - a_metrics['累计收益']:.2%}")
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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'] * 0.98:
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print(f" 建议：法国放入EU大类可行")
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print(f" 原因：类内竞争自动选最优，不增加大类数量")
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elif abs(b_metrics['累计收益'] - a_metrics['累计收益']) < 0.05:
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print(f" 建议：法国放入EU大类可考虑")
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print(f" 原因：收益损失小于5%，可接受")
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else:
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print(f" 建议：保持仅德国DAX")
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print(f" 原因：法国动量信号不如德国")
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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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# 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：法国CAC40放入EU大类（类内竞争）")
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print(f"{'='*60}")
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print(f"\n研究问题：")
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print(f" - 法国CAC40放入EU大类（market='EU'）")
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print(f" - 与德国DAX类内竞争，选动量最强的1个")
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print(f" - 不增加大类数量，避免占用Top3权重")
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print(f" - 对比004实验（法国独立大类）的差异")
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# A组：仅德国DAX
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a_metrics = run_backtest(base_config, "A组: 仅德国DAX代表欧洲")
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# B组：德国+法国（同属EU大类）
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config_with_france = create_config_with_france_in_eu(base_config)
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b_metrics = run_backtest(config_with_france, "B组: 德国+法国（EU类内竞争）")
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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_france_in_eu.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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