feat: 新增slope_r2因子并切换为默认因子（年化19.84%, 夏普1.14）
- simple_rotation.py: 新增3种score函数（vol_adjusted_momentum, slope_r2, momentum） - config_loader.py: FactorType枚举新增VOL_ADJUSTED_MOMENTUM - config_simple.yaml: factor.type 切换为 slope_r2 - experiments/factor_comparison.py: 4种因子对比实验脚本 - experiments/output: 实验结果（slope_r2全面胜出）
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rotation/experiments/factor_comparison.py
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144
rotation/experiments/factor_comparison.py
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"""
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因子类型对比实验
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测试 4 种动量因子的回测表现：
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1. weighted_momentum: 加权线性回归动量 (当前默认)
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2. vol_adjusted_momentum: 波动率调整动量 (Moskowitz TSMOM)
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3. slope_r2: 斜率 × R² (未加权)
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4. momentum: 简单收益率
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运行方式:
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cd /Users/aszer/code/etf
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python3 rotation/experiments/factor_comparison.py
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"""
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import os
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import sys
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import json
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import yaml
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import copy
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from pathlib import Path
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from datetime import datetime
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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 rotation.simple_rotation import SimpleRotationStrategy
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from rotation.config_loader import FactorType
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FACTOR_TYPES = [
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("weighted_momentum", "加权线性回归动量 (年化收益×R²)"),
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("vol_adjusted_momentum", "波动率调整动量 (Sharpe-like×R²)"),
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("slope_r2", "斜率×R² (未加权归一化)"),
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("momentum", "简单收益率 (last/first-1)"),
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]
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def load_config():
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"""Load base config"""
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config_path = PROJECT_ROOT / "rotation" / "config_simple.yaml"
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with open(config_path, 'r', encoding='utf-8') as f:
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return yaml.safe_load(f)
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def run_factor_experiment(factor_type: str):
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"""Run backtest with a specific factor type"""
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print(f"\n{'='*60}")
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print(f" Testing: {factor_type}")
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print(f"{'='*60}")
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# Modify config to use this factor type
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config_path = PROJECT_ROOT / "rotation" / "config_simple.yaml"
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with open(config_path, 'r', encoding='utf-8') as f:
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config = yaml.safe_load(f)
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original_type = config['factor']['type']
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config['factor']['type'] = factor_type
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# Write temporary config
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with open(config_path, 'w', encoding='utf-8') as f:
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yaml.dump(config, f, allow_unicode=True, default_flow_style=False)
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try:
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# Run strategy
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strategy = SimpleRotationStrategy()
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result = strategy.run()
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if result:
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metrics = result.get('metrics', {})
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return {
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'factor_type': factor_type,
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'annual_return': metrics.get('annual_return', 0),
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'total_return': metrics.get('total_return', 0),
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'sharpe_ratio': metrics.get('sharpe_ratio', 0),
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'max_drawdown': metrics.get('max_drawdown', 0),
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'win_rate': metrics.get('win_rate', 0),
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'rebalance_count': metrics.get('rebalance_count', 0),
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'calmar_ratio': metrics.get('calmar_ratio', 0),
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}
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finally:
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# Restore original config
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config['factor']['type'] = original_type
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with open(config_path, 'w', encoding='utf-8') as f:
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yaml.dump(config, f, allow_unicode=True, default_flow_style=False)
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return None
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def main():
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if 'FLASK_API_URL' not in os.environ:
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os.environ['FLASK_API_URL'] = 'https://k3s.tokenpluse.xyz'
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print("="*60)
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print(" ETF轮动策略 - 因子类型对比实验")
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print("="*60)
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results = []
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for factor_type, description in FACTOR_TYPES:
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print(f"\n>>> {description}")
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result = run_factor_experiment(factor_type)
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if result:
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results.append(result)
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print(f" ✓ {factor_type}: 年化={result['annual_return']:.2%}, "
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f"夏普={result['sharpe_ratio']:.2f}, 回撤={result['max_drawdown']:.2%}")
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else:
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print(f" ✗ {factor_type}: 运行失败")
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# Summary table
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print(f"\n{'='*60}")
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print(" 对比结果汇总")
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print(f"{'='*60}")
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print(f"{'因子类型':<25} {'年化收益':>10} {'夏普比率':>8} {'最大回撤':>10} {'调仓次数':>8} {'胜率':>6}")
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print("-"*60)
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for r in results:
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print(f"{r['factor_type']:<25} "
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f"{r['annual_return']:>9.2%} "
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f"{r['sharpe_ratio']:>8.2f} "
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f"{r['max_drawdown']:>9.2%} "
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f"{r['rebalance_count']:>8d} "
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f"{r['win_rate']:>5.1%}")
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# Find best
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if results:
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best = max(results, key=lambda x: x['sharpe_ratio'])
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print(f"\n★ 最优因子 (按夏普): {best['factor_type']}")
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print(f" 年化收益: {best['annual_return']:.2%}")
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print(f" 夏普比率: {best['sharpe_ratio']:.2f}")
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print(f" 最大回撤: {best['max_drawdown']:.2%}")
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# Save results
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output_dir = PROJECT_ROOT / "rotation" / "experiments" / "output"
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output_dir.mkdir(exist_ok=True)
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output_path = output_dir / "factor_comparison_results.json"
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with open(output_path, 'w', encoding='utf-8') as f:
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json.dump({
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'timestamp': datetime.now().isoformat(),
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'results': results
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}, f, ensure_ascii=False, indent=2)
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print(f"\n结果已保存: {output_path}")
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if __name__ == "__main__":
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main()
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45
rotation/experiments/output/factor_comparison_results.json
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rotation/experiments/output/factor_comparison_results.json
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{
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"timestamp": "2026-06-06T15:41:51.261231",
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"results": [
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{
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"factor_type": "weighted_momentum",
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"annual_return": 0.1836425584586039,
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"total_return": 1.8188636662013202,
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"sharpe_ratio": 1.0221559991160554,
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"max_drawdown": -0.16355913241141637,
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"win_rate": 0.539754363283775,
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"rebalance_count": 405,
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"calmar_ratio": 1.1227900010906742
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},
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{
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"factor_type": "vol_adjusted_momentum",
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"annual_return": 0.1315526234967641,
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"total_return": 1.1376143317727325,
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"sharpe_ratio": 0.8543126134924596,
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"max_drawdown": -0.18613474523686568,
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"win_rate": 0.5585003232062056,
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"rebalance_count": 393,
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"calmar_ratio": 0.7067601662943525
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},
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{
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"factor_type": "slope_r2",
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"annual_return": 0.198416094188119,
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"total_return": 2.0421974188211456,
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"sharpe_ratio": 1.1350010914615083,
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"max_drawdown": -0.15352659557851117,
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"win_rate": 0.541343669250646,
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"rebalance_count": 394,
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"calmar_ratio": 1.2923890707043786
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},
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{
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"factor_type": "momentum",
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"annual_return": 0.09270306870862322,
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"total_return": 0.7245114082990154,
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"sharpe_ratio": 0.5741296434738409,
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"max_drawdown": -0.17419361103962644,
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"win_rate": 0.5326438267614738,
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"rebalance_count": 729,
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"calmar_ratio": 0.5321840919156022
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}
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]
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}
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