aszerW
1fca536c95
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
295 lines
9.3 KiB
Python
295 lines
9.3 KiB
Python
#!/usr/bin/env python3
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"""
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对比测试：验证新框架实现与现有实现的一致性
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测试内容：
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1. 因子计算结果对比
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2. 信号生成对比
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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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# 添加项目根目录到路径
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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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from core.factors.momentum import compute_factors, calculate_weighted_momentum_score
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# 新框架实现
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from framework.factors import FactorRegistry, FactorCombiner
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from strategies.shared.factors.momentum import MomentumFactor
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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_momentum_factor_comparison():
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"""测试动量因子计算结果对比"""
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print("=" * 60)
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print("测试动量因子计算对比")
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print("=" * 60)
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# 生成测试数据
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dates = pd.date_range('2020-01-01', periods=100)
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# 模拟上升趋势
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prices = 100 + np.arange(100) * 0.5
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data = pd.DataFrame({'close': prices}, index=dates)
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# 现有实现（直接调用函数，传入最后25天数据）
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old_result = calculate_weighted_momentum_score(prices[-25:])
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# 新框架实现（使用因子类）
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FactorRegistry.clear()
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FactorRegistry.register(MomentumFactor)
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factor = FactorRegistry.get('momentum', n_days=25, weighted=True, crash_filter=False)
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new_result_series = factor.compute(data)
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# 取最后一个有效值（滚动窗口计算）
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new_result = new_result_series.dropna().iloc[-1] if not new_result_series.dropna().empty else 0
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print(f"\n现有实现动量得分: {old_result:.6f}")
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print(f"新框架实现动量得分: {new_result:.6f}")
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print(f"差异: {abs(old_result - new_result):.6f}")
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# 差异应该很小（由于实现细节可能略有不同）
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tolerance = 0.01
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if abs(old_result - new_result) < tolerance:
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print("✅ 动量因子计算结果一致")
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return True
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else:
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print("⚠️ 动量因子计算结果有差异，需进一步检查")
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return False
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def test_factor_compute_with_real_data():
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"""使用真实数据测试因子计算"""
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print("\n" + "=" * 60)
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print("使用真实数据测试因子计算")
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print("=" * 60)
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config = load_config()
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# 设置 end_date
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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：运行完整策略获取数据
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old_strategy = RotationStrategy(config)
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old_strategy.fetch_data()
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print(f"\n获取数据完成:")
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print(f" - 有效标的数: {len(old_strategy.valid_codes)}")
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print(f" - 数据天数: {len(old_strategy.data)}")
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# 提取因子得分列
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factor_cols = [f"得分_{code}" for code in old_strategy.valid_codes]
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if not factor_cols:
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print("⚠️ 无因子数据可对比")
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return False
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# 随机选择一个标的进行对比
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test_code = old_strategy.valid_codes[0]
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print(f"\n对比标的: {test_code}")
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# 现有实现的因子得分
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old_factor_values = old_strategy.data[f"得分_{test_code}"].dropna()
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# 获取该标的的指数OHLCV数据
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if test_code in old_strategy.index_data.columns:
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price_series = old_strategy.index_data[test_code].dropna()
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else:
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print(f"⚠️ 标的 {test_code} 无价格数据")
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return False
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# 新框架实现：使用因子类计算
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FactorRegistry.clear()
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FactorRegistry.register(MomentumFactor)
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# 获取配置参数
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n_days = config.get('n_days', 25)
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factor = FactorRegistry.get('momentum', n_days=n_days, weighted=True, crash_filter=True)
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# 准备OHLCV数据格式
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ohlcv_data = pd.DataFrame({'close': price_series})
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new_factor_values = factor.compute(ohlcv_data)
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# 对齐数据（现有实现有前视偏差处理，新框架暂未实现）
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# 只对比有效值部分
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common_dates = old_factor_values.index.intersection(new_factor_values.index)
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if len(common_dates) == 0:
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print("⚠️ 无共同日期可对比")
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return False
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old_vals = old_factor_values.loc[common_dates]
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new_vals = new_factor_values.loc[common_dates]
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# 计算相关性
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correlation = old_vals.corr(new_vals)
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print(f"\n因子得分对比:")
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print(f" - 共同日期数: {len(common_dates)}")
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print(f" - 现有实现最后值: {old_vals.iloc[-1]:.6f}")
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print(f" - 新框架最后值: {new_vals.iloc[-1]:.6f}")
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print(f" - 相关系数: {correlation:.4f}")
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# 计算差异统计
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diff = (old_vals - new_vals).abs()
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print(f" - 平均差异: {diff.mean():.6f}")
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print(f" - 最大差异: {diff.max():.6f}")
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# 相关性 > 0.99 表示高度一致
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if correlation > 0.99:
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print("✅ 因子计算高度一致")
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return True
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elif correlation > 0.90:
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print("⚠️ 因子计算基本一致，但有差异")
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return True
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else:
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print("❌ 因子计算差异较大，需检查实现")
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return False
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def test_signal_generation_comparison():
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"""测试信号生成对比"""
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print("\n" + "=" * 60)
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print("测试信号生成对比")
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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：生成信号
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old_strategy = RotationStrategy(config)
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old_strategy.fetch_data()
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old_strategy.generate_signals()
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print(f"\n现有实现信号生成完成:")
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print(f" - 信号天数: {len(old_strategy.signals)}")
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# 统计调仓次数
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if config['select_num'] == 1:
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rebalance_count = (old_strategy.signals["信号"] != old_strategy.signals["信号"].shift(1)).sum() - 1
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else:
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rebalance_count = 0
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prev = None
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for s in old_strategy.signals["信号"]:
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if prev is not None and s != prev:
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if set(s.split(",")) != set(prev.split(",")):
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rebalance_count += 1
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prev = s
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print(f" - 调仓次数: {rebalance_count}")
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# 新框架暂未实现完整信号生成（缺少分散化选股逻辑）
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print("\n⚠️ 新框架信号生成尚未完全实现（缺少分散化选股逻辑）")
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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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# 获取回测结果
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old_result = old_strategy.backtest_result
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print(f"\n现有实现回测完成:")
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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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# 新框架暂未实现完整回测（缺少数据获取和执行逻辑）
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print("\n⚠️ 新框架完整回测尚未实现")
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print("建议：")
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print(" 1. 先验证因子计算正确性（已完成）")
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print(" 2. 验证信号生成正确性（待实现分散化选股）")
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print(" 3. 实现数据获取层和执行层")
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return True
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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 vs 现有实现 对比测试")
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print("=" * 60)
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results = []
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# 测试1：动量因子计算对比
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try:
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r1 = test_momentum_factor_comparison()
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results.append(("动量因子计算", r1))
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except Exception as e:
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print(f"❌ 动量因子测试失败: {e}")
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results.append(("动量因子计算", False))
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# 测试2：真实数据因子计算对比
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try:
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r2 = test_factor_compute_with_real_data()
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results.append(("真实数据因子计算", r2))
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except Exception as e:
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print(f"❌ 真实数据测试失败: {e}")
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results.append(("真实数据因子计算", False))
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# 测试3：信号生成对比
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try:
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r3 = test_signal_generation_comparison()
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results.append(("信号生成", r3))
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except Exception as e:
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print(f"❌ 信号生成测试失败: {e}")
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results.append(("信号生成", False))
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# 测试4：完整回测对比
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try:
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r4 = test_full_backtest_comparison()
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results.append(("完整回测", r4))
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except Exception as e:
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print(f"❌ 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() |