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_iterations.py
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tests/experiments/ab_test_iterations.py
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"""
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策略迭代 A/B 对比实验脚本
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量化三个维度的改进贡献度：
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1. 标的池: 原始全市场池 vs. 精选11只核心池
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2. 评分公式: 简单斜率(slope_r2) vs. 年化收益率*R2 (weighted_momentum)
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3. 观察窗口: 固定25日窗口 vs. 动态ATR窗口 (20-60天)
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"""
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import sys
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import pandas as pd
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import numpy as np
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from pathlib import Path
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from datetime import datetime
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# 添加项目根目录
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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 matplotlib.pyplot as plt
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# ==================== 标的池定义 ====================
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ORIGINAL_POOL = {
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"000300.SH": {"name": "沪深300", "market": "A", "etf": "510300.SH"},
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"000905.SH": {"name": "中证500", "market": "A", "etf": "510500.SH"},
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"000852.SH": {"name": "中证1000", "market": "A", "etf": "512100.SH"},
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"399006.SZ": {"name": "创业板指", "market": "A", "etf": "159915.SZ"},
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"000015.SH": {"name": "上证红利", "market": "A", "etf": "510880.SH"},
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"399986.SZ": {"name": "中证银行", "market": "A", "etf": "516310.SH"},
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"399997.SZ": {"name": "中证白酒", "market": "A", "etf": "512690.SH"},
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"399989.SZ": {"name": "中证医疗", "market": "A", "etf": "512170.SH"},
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"399395.SZ": {"name": "国证有色", "market": "COMMODITY", "etf": "159880.SZ"},
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"399998.SZ": {"name": "中证煤炭", "market": "A", "etf": "515220.SH"},
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"399967.SZ": {"name": "中证军工", "market": "A", "etf": "512660.SH"},
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"HSTECH.HK": {"name": "恒生科技", "market": "HK", "etf": "513180.SH"},
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"NDX": {"name": "纳指100", "market": "US", "etf": "513100.SH"},
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"AU.SHF": {"name": "黄金", "market": "COMMODITY", "etf": "518880.SH"}
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}
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FINAL_POOL = {
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"399006.SZ": {"name": "创业板指", "market": "A", "etf": "159915.SZ"},
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"H30269.CSI": {"name": "中证红利低波", "market": "A", "etf": "512890.SH"},
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"000015.SH": {"name": "上证红利", "market": "A", "etf": "510880.SH"},
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"NDX": {"name": "纳指100", "market": "US", "etf": "513100.SH"},
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"N225": {"name": "日经225", "market": "JP", "etf": "513520.SH"},
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"GDAXI": {"name": "德国DAX", "market": "EU", "etf": "513030.SH"},
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"HSI": {"name": "恒生指数", "market": "HK", "etf": "159920.SZ"},
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"HSTECH.HK": {"name": "恒生科技", "market": "HK", "etf": "513130.SH"},
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"AU.SHF": {"name": "黄金", "market": "COMMODITY", "etf": "518880.SH"},
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"CL.NYM": {"name": "原油", "market": "COMMODITY", "etf": "160723.SZ"},
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"931862.CSI": {"name": "30年国债", "market": "BOND", "etf": "511090.SH"}
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}
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# ==================== 实验配置 ====================
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ITERATIONS = [
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{
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"label": "1. 原始基准 (原始池+简单评分+固定窗口)",
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"config": {
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"code_list": ORIGINAL_POOL,
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"factor_type": "slope_r2",
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"auto_day": False,
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"n_days": 25,
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"diversified": False
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}
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},
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{
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"label": "2. 标的池优化 (精选池+简单评分+固定窗口)",
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"config": {
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"code_list": FINAL_POOL,
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"factor_type": "slope_r2",
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"auto_day": False,
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"n_days": 25,
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"diversified": True # 开启跨大类分散
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}
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},
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{
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"label": "3. 评分公式优化 (精选池+加权评分+固定窗口)",
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"config": {
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"code_list": FINAL_POOL,
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"factor_type": "weighted_momentum",
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"auto_day": False,
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"n_days": 25,
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"diversified": True
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}
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},
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{
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"label": "4. 终极版本 (精选池+加权评分+动态窗口)",
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"config": {
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"code_list": FINAL_POOL,
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"factor_type": "weighted_momentum",
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"auto_day": True,
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"n_days": 25, # 提供默认窗口作为 fallback
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"min_days": 20,
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"max_days": 60,
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"diversified": True
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}
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}
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]
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COMMON_CONFIG = {
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"start_date": "2019-01-01",
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"end_date": datetime.now().strftime('%Y-%m-%d'),
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"select_num": 3,
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"rebalance_days": 1,
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"rebalance_threshold": 0.0,
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"trade_cost": 0.001,
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"premium_control": {"enabled": True, "default_threshold": 0.10},
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"use_cache": True,
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"ssh_tunnel": {"enabled": True, "host": "8.218.167.69", "port": 22, "username": "root", "key_path": "hk_ecs.pem", "local_port": 1080}
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}
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def run_experiment():
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results = []
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for i, item in enumerate(ITERATIONS):
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print(f"\n{'='*80}")
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print(f"运行实验 {item['label']}")
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print(f"{'='*80}")
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cfg = COMMON_CONFIG.copy()
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cfg.update(item['config'])
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strategy = RotationStrategy(cfg)
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try:
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res_df = strategy.run()
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# 计算指标
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nav = res_df['轮动策略净值']
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total_ret = nav.iloc[-1] - 1
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days = (nav.index[-1] - nav.index[0]).days
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cagr = (1 + total_ret)**(365.25/days) - 1
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daily_ret = res_df['轮动策略日收益率']
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sharpe = daily_ret.mean() / daily_ret.std() * np.sqrt(252) if daily_ret.std() > 0 else 0
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peak = nav.cummax()
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dd = (nav - peak) / peak
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max_dd = dd.min()
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results.append({
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"label": item['label'],
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"total_ret": total_ret,
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"cagr": cagr,
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"max_dd": max_dd,
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"sharpe": sharpe,
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"nav": nav
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})
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print(f"完成: CAGR={cagr:.2%}, MaxDD={max_dd:.2%}, Sharpe={sharpe:.2f}")
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except Exception as e:
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print(f"实验失败: {e}")
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import traceback
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traceback.print_exc()
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# ==================== 汇总报告 ====================
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print(f"\n\n{'='*100}")
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print(f"{'策略迭代对比报告':^100}")
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print(f"{'='*100}")
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print(f"{'版本':<40} | {'累计收益':>10} | {'年化(CAGR)':>10} | {'最大回撤':>10} | {'夏普比率':>8} | {'贡献增量':>10}")
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print(f"{'-'*100}")
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prev_cagr = 0
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for i, r in enumerate(results):
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delta = f"+{(r['cagr'] - prev_cagr)*100:>.2f}%" if i > 0 else "-"
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print(f"{r['label']:<40} | {r['total_ret']:>10.2%} | {r['cagr']:>10.2%} | {r['max_dd']:>10.2%} | {r['sharpe']:>8.2f} | {delta:>10}")
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prev_cagr = r['cagr']
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print(f"{'='*100}")
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# ==================== 绘图 ====================
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plt.figure(figsize=(15, 8))
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for r in results:
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plt.plot(r['nav'].index, r['nav'], label=r['label'], linewidth=1.5)
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plt.yscale('log')
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plt.title("策略迭代 A/B 对比 - 净值曲线 (对数坐标)", fontsize=14)
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plt.legend()
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plt.grid(True, alpha=0.3)
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output_path = Path(__file__).parent.parent / "results" / "ab_test_iterations.png"
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plt.savefig(output_path)
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print(f"\n对比图表已保存至: {output_path}")
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if __name__ == "__main__":
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run_experiment()
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