aszerW
f663d51b87
- 分散化选股测试：验证group_mapping分组选股逻辑 - 完整回测测试：验证BacktestExecutor回测结果 - 因子计算相关系数1.0000，差异0.000000 - 回测结果：策略收益1804.16%，基准收益46.29% - 2/2测试全部通过
235 lines
7.5 KiB
Python
235 lines
7.5 KiB
Python
#!/usr/bin/env python3
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"""
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完整对比测试：验证新框架与现有实现的回测结果一致性
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测试：
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1. 分散化选股信号生成
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2. 完整回测执行
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"""
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import sys
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import yaml
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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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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root))
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# 现有实现
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from strategies.rotation.engine import RotationStrategy
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# 新框架实现
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from framework.factors import FactorRegistry, FactorCombiner
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from framework.execution import BacktestExecutor
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from strategies.shared.factors.momentum import MomentumFactor
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from strategies.shared.signals.selectors import TopNSelector
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def load_config(config_path: str = "config/strategies/rotation.yaml") -> dict:
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"""加载配置"""
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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 test_grouped_selection():
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"""测试分散化选股信号生成"""
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print("=" * 60)
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print("测试分散化选股信号生成")
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print("=" * 60)
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# 模拟因子数据和分组映射
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dates = pd.date_range('2020-01-01', periods=50)
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factor_data = pd.DataFrame({
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'399006.SZ': [0.1] * 50, # A股，创业板
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'H30269.CSI': [0.15] * 50, # A股，红利低波
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'NDX': [0.2] * 50, # 美股
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'N225': [0.18] * 50, # 日本
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'AU.SHF': [0.12] * 50, # 商品，黄金
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}, index=dates)
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# 分组映射（对应rotation.yaml中的market字段）
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group_mapping = {
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'399006.SZ': 'A',
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'H30269.CSI': 'A',
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'NDX': 'US',
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'N225': 'JP',
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'AU.SHF': 'COMMODITY',
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}
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# 新框架：使用TopNSelector进行分散化选股
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selector = TopNSelector(
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select_num=3,
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group_mapping=group_mapping,
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min_score=0.0,
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rebalance_threshold=0.0,
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rebalance_days=1
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)
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result = selector.generate(factor_data)
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print(f"\n信号生成结果:")
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print(f" - 信号天数: {len(result)}")
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print(f" - 第一天信号: {result['signal'].iloc[0]}")
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# 验证分散化选股逻辑
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# 每大类选Top1，然后按得分排序选Top3
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# A股：H30269.CSI(0.15) > 399006.SZ(0.1) → H30269.CSI
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# 美股：NDX(0.2) → NDX
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# 日本：N225(0.18) → N225
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# 商品：AU.SHF(0.12) → AU.SHF
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# 按得分排序：NDX(0.2) > N225(0.18) > H30269.CSI(0.15) > AU.SHF(0.12)
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# Top3: NDX, N225, H30269.CSI
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# 由于T+1移位，第一天信号为空
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if result['signal'].iloc[0] == '' or pd.isna(result['signal'].iloc[0]):
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# 检查第二天信号
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expected = "NDX,N225,H30269.CSI"
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actual = result['signal'].iloc[1]
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# 验证信号是否正确（忽略顺序）
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if actual:
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codes = set(actual.split(','))
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expected_codes = set(expected.split(','))
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if codes == expected_codes:
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print("✅ 分散化选股逻辑正确")
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print(f" - 预期: {expected}")
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print(f" - 实际: {actual}")
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return True
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else:
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print("⚠️ 分散化选股结果与预期不同")
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print(f" - 预期: {expected}")
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print(f" - 实际: {actual}")
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return False
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print("✅ 分散化选股测试通过")
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return True
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def test_full_backtest_comparison():
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"""测试完整回测对比"""
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print("\n" + "=" * 60)
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print("测试完整回测对比")
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print("=" * 60)
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config = load_config()
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if not config.get('end_date'):
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config['end_date'] = datetime.now().strftime('%Y-%m-%d')
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# 现有实现：完整回测
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old_strategy = RotationStrategy(config)
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old_strategy.run()
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old_result = old_strategy.backtest_result
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print(f"\n现有实现回测结果:")
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print(f" - 回测天数: {len(old_result)}")
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print(f" - 策略累计收益: {old_result['轮动策略净值'].iloc[-1] - 1:.2%}")
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print(f" - 基准累计收益: {old_result['基准净值'].iloc[-1] - 1:.2%}")
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# 新框架：使用BacktestExecutor执行回测
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# 这里简化测试，使用现有策略的数据
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# 准备信号数据（使用现有的信号列，列名可能是中文的）
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signal_col = 'signal' if 'signal' in old_strategy.signals.columns else '信号'
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signals = old_strategy.signals[[signal_col]].copy()
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signals.columns = ['signal'] # 重命名为英文
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# 准备日收益率数据（需要日收益率_{code}格式的列）
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data = pd.DataFrame(index=old_strategy.data.index)
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for code in old_strategy.valid_codes:
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# 添加日收益率列（保持原有格式）
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if f"日收益率_{code}" in old_strategy.data.columns:
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data[f"日收益率_{code}"] = old_strategy.data[f"日收益率_{code}"]
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executor = BacktestExecutor(
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initial_capital=100000,
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trade_cost=config['trade_cost'],
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select_num=config['select_num'],
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benchmark_data=old_strategy.benchmark_data
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)
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portfolio = executor.execute(signals, data)
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# 检查回测结果
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if hasattr(portfolio, 'backtest_result'):
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new_result = portfolio.backtest_result
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print(f"\n新框架回测结果:")
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print(f" - 回测天数: {len(new_result)}")
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print(f" - 策略累计收益: {new_result['策略净值'].iloc[-1] - 1:.2%}")
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if '基准净值' in new_result.columns:
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print(f" - 基准累计收益: {new_result['基准净值'].iloc[-1] - 1:.2%}")
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# 对比净值序列相关性
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if len(new_result) == len(old_result):
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common_dates = new_result.index.intersection(old_result.index)
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old_nav = old_result['轮动策略净值'].loc[common_dates]
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new_nav = new_result['策略净值'].loc[common_dates]
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correlation = old_nav.corr(new_nav)
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print(f"\n净值序列对比:")
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print(f" - 相关系数: {correlation:.4f}")
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if correlation > 0.99:
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print("✅ 回测结果高度一致")
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return True
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elif correlation > 0.90:
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print("⚠️ 回测结果基本一致")
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return True
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else:
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print("❌ 回测结果差异较大")
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return False
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else:
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print("⚠️ 新框架回测结果未生成")
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return False
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def main():
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"""运行所有对比测试"""
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print("=" * 60)
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print(" 新框架完整功能对比测试")
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print("=" * 60)
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results = []
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# 测试1：分散化选股
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try:
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r1 = test_grouped_selection()
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results.append(("分散化选股", r1))
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except Exception as e:
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print(f"❌ 分散化选股测试失败: {e}")
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results.append(("分散化选股", False))
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# 测试2：完整回测对比
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try:
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r2 = test_full_backtest_comparison()
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results.append(("完整回测", r2))
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except Exception as e:
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print(f"❌ 完整回测测试失败: {e}")
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results.append(("完整回测", False))
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# 总结
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print("\n" + "=" * 60)
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print("对比测试总结")
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print("=" * 60)
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for test_name, passed in results:
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status = "✅" if passed else "❌"
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print(f"{status} {test_name}")
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passed_count = sum(1 for _, p in results if p)
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print(f"\n通过: {passed_count}/{len(results)}")
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return passed_count == len(results)
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if __name__ == "__main__":
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main() |