fix: 回测细节导出、交易日历测试和动量因子修复
修复项: - export_backtest_detail.py: 统一回测导出脚本的数据源调用逻辑 - test_trading_calendar.py: 交易日历功能测试 - verify_fix_result.py: 修复结果验证 - verify_mode_b.py: 模式 B 验证 策略修复: - momentum.py: 动量因子计算优化 - strategy.py: StrategyBase 数据获取修复（fetch_indices 返回 dict）
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tests/verify_mode_b.py
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208
tests/verify_mode_b.py
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#!/usr/bin/env python3
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"""
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验证 Mode B: 指数信号 → ETF收益
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文档预期结果:
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CAGR: 28.07%, 最大回撤: -13.34%, 夏普: 1.685, Calmar: 2.104
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"""
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import sys
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from pathlib import Path
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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root))
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from dotenv import load_dotenv
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load_dotenv()
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import pandas as pd
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import numpy as np
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import yaml
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from datetime import datetime
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from strategies.rotation.strategy import RotationStrategy
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def calculate_metrics(nav: pd.Series) -> dict:
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"""计算绩效指标"""
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start_date = nav.index[0]
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end_date = nav.index[-1]
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days = (end_date - start_date).days
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years = days / 365
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total_return = nav.iloc[-1] - 1
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cagr = (nav.iloc[-1] / nav.iloc[0]) ** (1/years) - 1
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daily_ret = nav.pct_change().dropna()
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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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drawdown = (nav - peak) / peak
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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 = (daily_ret > 0).sum() / len(daily_ret)
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return {
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'start_date': start_date.strftime('%Y-%m-%d'),
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'end_date': end_date.strftime('%Y-%m-%d'),
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'years': years,
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'days': len(nav),
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'total_return': total_return,
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'cagr': cagr,
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'max_dd': max_dd,
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'sharpe': sharpe,
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'calmar': calmar,
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'win_rate': win_rate
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}
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def run_mode_b_backtest(data: dict, signals: pd.DataFrame, valid_codes: list,
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etf_code_map: dict, a_share_dates: pd.DatetimeIndex,
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trade_cost: float, select_num: int) -> dict:
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"""
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Mode B: 使用ETF价格计算收益
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Args:
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data: 包含 etf_data 的数据字典
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signals: 指数生成的信号
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valid_codes: 指数代码列表
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etf_code_map: {指数代码: ETF代码} 映射
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a_share_dates: A股交易日历
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trade_cost: 交易成本
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select_num: 选股数量
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"""
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from framework.execution import BacktestExecutor
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etf_data = data.get('etf_data')
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if etf_data is None:
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print("❌ ETF数据不可用")
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return {'result': None}
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# 将信号对齐到 A 股日历
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if a_share_dates is not signals.index:
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signals = signals.reindex(a_share_dates, method='ffill').dropna(subset=[signals.columns[0]])
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# 使用ETF收盘价计算收益率
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returns_data = {}
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for code in valid_codes:
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etf_code = etf_code_map.get(code)
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if etf_code and etf_code in etf_data.columns:
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etf_close = etf_data[etf_code].dropna()
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# 对齐到A股日历
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etf_aligned = etf_close.reindex(a_share_dates, method='ffill')
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returns_aligned = etf_aligned.pct_change(fill_method=None)
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# 使用指数代码作为列名（与信号匹配）
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returns_data[f'日收益率_{code}'] = returns_aligned
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else:
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# 没有ETF映射的标的，回退使用指数数据
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index_data = data.get('index_data', {})
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if code in index_data and 'close' in index_data[code].columns:
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close_series = index_data[code]['close'].dropna()
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close_aligned = close_series.reindex(a_share_dates, method='ffill')
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returns_data[f'日收益率_{code}'] = close_aligned.pct_change(fill_method=None)
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returns_df = pd.DataFrame(returns_data)
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# 对齐日期
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common_dates = signals.index.intersection(returns_df.index)
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signals = signals.loc[common_dates]
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returns_df = returns_df.loc[common_dates]
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print(f" Mode B 对齐后日期: {len(common_dates)} 天")
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print(f" 使用ETF计算收益: {len([c for c in valid_codes if etf_code_map.get(c)])} 只")
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executor = BacktestExecutor(
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initial_capital=100000,
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trade_cost=trade_cost,
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select_num=select_num
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)
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portfolio = executor.execute(signals, returns_df)
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if hasattr(portfolio, 'backtest_result'):
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return {'result': portfolio.backtest_result, 'portfolio': portfolio}
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return {'result': None}
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def main():
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# 加载配置
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config_path = project_root / 'strategies/rotation/config.yaml'
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with open(config_path, 'r') as f:
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config = yaml.safe_load(f)
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# 设置回测区间
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config['start_date'] = '2020-01-02'
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config['end_date'] = '2026-05-19'
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print('='*70)
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print('Mode B 验证: 指数信号 → ETF收益')
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print('='*70)
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# 初始化策略
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strategy = RotationStrategy(config)
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# 获取数据
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print('\n获取数据...')
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data = strategy.get_data(use_flask_api=False)
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# 计算因子（使用指数数据）
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print('\n计算因子（指数信号）...')
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factor_df = strategy.compute_factors(data)
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# 生成信号
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print('\n生成信号...')
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signals = strategy.generate_signals(factor_df)
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# 执行 Mode B 回测
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print('\n执行 Mode B 回测（ETF收益）...')
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result_b = run_mode_b_backtest(
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data=data,
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signals=signals,
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valid_codes=data['valid_codes'],
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etf_code_map=data['etf_code_map'],
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a_share_dates=data.get('a_share_dates'),
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trade_cost=config.get('trade_cost', 0.001),
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select_num=config.get('select_num', 3)
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)
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if result_b.get('result') is None:
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print('❌ Mode B 回测未生成结果')
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return
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# 计算指标
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nav_b = result_b['result']['策略净值']
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metrics_b = calculate_metrics(nav_b)
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# 输出结果
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print('\n' + '='*70)
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print('Mode B 回测结果')
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print('='*70)
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print(f"回测区间: {metrics_b['start_date']} ~ {metrics_b['end_date']}")
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print(f"回测年数: {metrics_b['years']:.2f} 年")
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print(f"交易天数: {metrics_b['days']} 天")
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print('-'*70)
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print(f"CAGR: {metrics_b['cagr']:.2%}")
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print(f"最大回撤: {metrics_b['max_dd']:.2%}")
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print(f"夏普比率: {metrics_b['sharpe']:.3f}")
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print(f"Calmar比率: {metrics_b['calmar']:.3f}")
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print(f"日胜率: {metrics_b['win_rate']:.2%}")
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print(f"累计收益: {metrics_b['total_return']:.2%}")
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print('='*70)
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# 文档预期对比
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print('\n文档预期 (Mode B):')
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print(' CAGR: 28.07%, MaxDD -13.34%, Sharpe 1.685, Calmar 2.104')
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cagr_diff = abs(metrics_b['cagr'] - 0.2807)
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print(f'\nCAGR差异: {cagr_diff:.2%}')
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if cagr_diff < 0.05:
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print('✓ 结果与文档预期基本一致')
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else:
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print('⚠ 结果与文档预期有差异')
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return metrics_b
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if __name__ == '__main__':
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main()
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