refactor: 归档旧代码，保留新框架结构
归档内容： - core/ (数据源、因子计算、通用工具) → archive/legacy_core/ - strategies/rotation/engine.py, portfolio.py, report.py → archive/legacy_core/ - scripts/ (run_rotation, daily_scheduler) → archive/legacy_scripts/ - examples/ → archive/legacy_examples/ - tests/ (实验、对比测试) → archive/legacy_tests/ - 单独文件 (fetch_*.py, 动量.py, 全球市场.py等) → archive/single_files/ 保留新结构： - framework/ (抽象接口) - strategies/shared/ (定制组件) - strategies/rotation/strategy.py (新策略) - 外层配置: .env, .dockerignore, build-and-push.sh, hk_ecs.pem, README.md, requirements.txt - Docker相关: Dockerfile, Dockerfile_base, docker-compose.yml 更新README反映新框架架构
This commit is contained in:
208
archive/legacy_tests/tests/experiments/ab_test_sea_etf.py
Normal file
208
archive/legacy_tests/tests/experiments/ab_test_sea_etf.py
Normal file
@@ -0,0 +1,208 @@
|
||||
"""
|
||||
A/B测试：添加东南亚科技ETF的影响（受限测试）
|
||||
对比：
|
||||
- A组（对照组）：当前配置（无东南亚）
|
||||
- B组（实验组）：添加东南亚科技ETF
|
||||
|
||||
限制说明：
|
||||
- 东南亚科技ETF（513730.SH）2023年12月上市，数据仅约2年
|
||||
- 新交所泛东南亚科技指数在YFinance中暂无数据
|
||||
- 本次测试使用ETF价格作为信号源（非最佳实践，仅作参考）
|
||||
- 回测时间范围将被缩短
|
||||
|
||||
核心问题：新兴市场ETF流动性是否优于印度LOF
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
|
||||
|
||||
from strategies.rotation.engine import RotationStrategy
|
||||
import pandas as pd
|
||||
import yaml
|
||||
|
||||
|
||||
def create_config_with_sea(base_config: dict) -> dict:
|
||||
"""在基础配置上添加东南亚科技"""
|
||||
config = base_config.copy()
|
||||
config['code_list'] = base_config['code_list'].copy()
|
||||
|
||||
# 添加东南亚科技（新大类）
|
||||
# 注意：由于指数数据不可用，使用ETF价格作为信号源
|
||||
# 513730.SH 同时作为指数代码和ETF代码
|
||||
config['code_list']['513730.SH'] = {
|
||||
'name': '东南亚科技',
|
||||
'etf': '513730.SH', # 华泰柏瑞东南亚科技ETF
|
||||
'market': 'SEA' # 东南亚大类
|
||||
}
|
||||
|
||||
return config
|
||||
|
||||
|
||||
def run_backtest(config: dict, label: str) -> dict:
|
||||
"""运行回测并返回关键指标"""
|
||||
print(f"\n{'='*60}")
|
||||
print(f" {label}")
|
||||
print(f"{'='*60}")
|
||||
|
||||
strategy = RotationStrategy(config)
|
||||
result = strategy.run()
|
||||
|
||||
if result is None or len(result) == 0:
|
||||
return None
|
||||
|
||||
# 计算指标
|
||||
strategy_nav = result['轮动策略净值']
|
||||
strategy_ret = result['轮动策略日收益率']
|
||||
|
||||
total_return = strategy_nav.iloc[-1] - 1
|
||||
days = len(result)
|
||||
years = days / 250
|
||||
cagr = (strategy_nav.iloc[-1] ** (1/years)) - 1 if years > 0 else 0
|
||||
excess_ret = strategy_ret.mean() * 250
|
||||
vol = strategy_ret.std() * (250 ** 0.5)
|
||||
sharpe = excess_ret / vol if vol > 0 else 0
|
||||
rolling_max = strategy_nav.cummax()
|
||||
drawdown = (strategy_nav - rolling_max) / rolling_max
|
||||
max_dd = drawdown.min()
|
||||
calmar = cagr / abs(max_dd) if max_dd < 0 else 0
|
||||
win_rate = (strategy_ret > 0).sum() / len(strategy_ret)
|
||||
|
||||
# 统计大类数量
|
||||
markets = set()
|
||||
for code_info in config['code_list'].values():
|
||||
markets.add(code_info.get('market', 'A'))
|
||||
|
||||
metrics = {
|
||||
'label': label,
|
||||
'大类数量': len(markets),
|
||||
'回测天数': days,
|
||||
'回测年数': years,
|
||||
'累计收益': total_return,
|
||||
'CAGR': cagr,
|
||||
'Sharpe': sharpe,
|
||||
'MaxDD': max_dd,
|
||||
'Calmar': calmar,
|
||||
'日胜率': win_rate,
|
||||
}
|
||||
|
||||
print(f"\n大类数量: {metrics['大类数量']}")
|
||||
print(f"回测天数: {metrics['回测天数']}")
|
||||
print(f"回测年数: {metrics['回测年数']:.2f}")
|
||||
print(f"累计收益: {metrics['累计收益']:.2%}")
|
||||
print(f"CAGR: {metrics['CAGR']:.2%}")
|
||||
print(f"Sharpe: {metrics['Sharpe']:.2f}")
|
||||
print(f"MaxDD: {metrics['MaxDD']:.2%}")
|
||||
print(f"Calmar: {metrics['Calmar']:.2f}")
|
||||
print(f"日胜率: {metrics['日胜率']:.2%}")
|
||||
|
||||
return metrics
|
||||
|
||||
|
||||
def compare_results(a_metrics: dict, b_metrics: dict):
|
||||
"""对比两组结果"""
|
||||
print(f"\n{'='*60}")
|
||||
print(f" 对比结果")
|
||||
print(f"{'='*60}")
|
||||
|
||||
print(f"\n{'指标':<15} {'A组(无东南亚)':<15} {'B组(有东南亚)':<15} {'差异':<15}")
|
||||
print("-" * 60)
|
||||
|
||||
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']
|
||||
|
||||
for key in metrics_keys:
|
||||
a_val = a_metrics.get(key, 0)
|
||||
b_val = b_metrics.get(key, 0)
|
||||
|
||||
diff = b_val - a_val
|
||||
if key in ['累计收益', 'CAGR', 'MaxDD', '日胜率']:
|
||||
a_str = f"{a_val:.2%}"
|
||||
b_str = f"{b_val:.2%}"
|
||||
diff_str = f"{diff*100:+.2f}%"
|
||||
elif key in ['大类数量', '回测天数']:
|
||||
a_str = str(int(a_val))
|
||||
b_str = str(int(b_val))
|
||||
diff_str = f"+{int(diff)}" if diff > 0 else str(int(diff))
|
||||
else:
|
||||
a_str = f"{a_val:.2f}"
|
||||
b_str = f"{b_val:.2f}"
|
||||
diff_str = f"{diff:+.2f}"
|
||||
|
||||
print(f"{key:<15} {a_str:<15} {b_str:<15} {diff_str:<15}")
|
||||
|
||||
print("-" * 60)
|
||||
|
||||
print(f"\n【限制说明】")
|
||||
print(f" ⚠ 本次测试数据量受限（东南亚ETF仅2年数据）")
|
||||
print(f" ⚠ 使用ETF价格作为信号源（指数数据暂不可用）")
|
||||
print(f" ⚠ 结果仅供参考，不建议直接用于决策")
|
||||
|
||||
print(f"\n【关键发现】")
|
||||
|
||||
if b_metrics['大类数量'] > a_metrics['大类数量']:
|
||||
print(f" ✓ 大类数量增加 {b_metrics['大类数量'] - a_metrics['大类数量']}")
|
||||
|
||||
if b_metrics['累计收益'] > a_metrics['累计收益']:
|
||||
print(f" ✓ 累计收益提升 {b_metrics['累计收益'] - a_metrics['累计收益']:.2%}")
|
||||
else:
|
||||
print(f" ✗ 累计收益下降 {a_metrics['累计收益'] - b_metrics['累计收益']:.2%}")
|
||||
|
||||
if b_metrics['Sharpe'] > a_metrics['Sharpe']:
|
||||
print(f" ✓ Sharpe改善 {b_metrics['Sharpe'] - a_metrics['Sharpe']:.2f}")
|
||||
else:
|
||||
print(f" ✗ Sharpe下降 {a_metrics['Sharpe'] - b_metrics['Sharpe']:.2f}")
|
||||
|
||||
print(f"\n【策略建议】")
|
||||
print(f" 建议：等待东南亚科技ETF积累更多数据后再测试")
|
||||
print(f" 原因：")
|
||||
print(f" 1. 数据量不足（仅{b_metrics['回测年数']:.1f}年）")
|
||||
print(f" 2. 指数信号源暂不可用")
|
||||
print(f" 3. ETF价格作为信号源存在溢价干扰")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
config_path = Path(__file__).parent.parent.parent / 'config' / 'strategies' / 'rotation.yaml'
|
||||
with open(config_path, 'r') as f:
|
||||
base_config = yaml.safe_load(f)
|
||||
|
||||
# 设置回测结束日期
|
||||
from datetime import datetime
|
||||
base_config['end_date'] = datetime.now().strftime('%Y-%m-%d')
|
||||
|
||||
# ⚠ 重要：由于东南亚ETF数据从2023年12月开始
|
||||
# 需要调整start_date以匹配数据可用范围
|
||||
# 本次测试将使用较短的时间窗口
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f" A/B测试：添加东南亚科技ETF（受限测试）")
|
||||
print(f"{'='*60}")
|
||||
print(f"\n⚠ 限制说明：")
|
||||
print(f" - 东南亚科技ETF（513730.SH）2023年12月上市")
|
||||
print(f" - 数据仅约2年，回测时间范围受限")
|
||||
print(f" - 指数数据暂不可用，使用ETF价格作为信号源")
|
||||
print(f" - 结果仅供参考，不建议直接用于决策")
|
||||
|
||||
# A组：当前配置（使用较短时间窗口）
|
||||
config_a = base_config.copy()
|
||||
config_a['start_date'] = '2024-01-01' # 调整为东南亚ETF有数据的起始时间
|
||||
a_metrics = run_backtest(config_a, "A组: 当前配置（2024年起）")
|
||||
|
||||
# B组：添加东南亚科技
|
||||
config_b = create_config_with_sea(base_config)
|
||||
config_b['start_date'] = '2024-01-01'
|
||||
b_metrics = run_backtest(config_b, "B组: 添加东南亚科技")
|
||||
|
||||
# 对比
|
||||
if a_metrics and b_metrics:
|
||||
compare_results(a_metrics, b_metrics)
|
||||
|
||||
# 保存结果
|
||||
results_df = pd.DataFrame([a_metrics, b_metrics])
|
||||
results_path = Path(__file__).parent.parent.parent / 'results' / 'ab_test_sea_etf.csv'
|
||||
results_df.to_csv(results_path, index=False)
|
||||
print(f"\n对比结果已保存: {results_path}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user