feat(framework_v2): 添加跨市场数据对齐器 + Pydantic Schema 验证
## 核心功能 - CrossMarketAligner: 跨市场数据对齐（解决 ffill 陷阱） - Pydantic Schema: 数据结构验证（OHLCVInputSchema, AlignedFactorSchema 等） - 验证装饰器: @validate_factor_after_align, @validate_returns_after_align ## 解决的问题 - 跨市场交易日历不同（美股/港股/A股） - ffill 收益率陷阱（休市日复制非零收益率） - NaN 传播问题 - 日期不一致问题 ## 测试验证 - 5/5 测试通过（因子对齐、收益率对齐、多标的对齐、信号验证、ffill陷阱） - 休市日收益率 = 0%（正确） - 无 NaN 传播 ## 架构设计 - shared/data/alignment.py - 对齐器实现 - shared/data/schemas.py - Pydantic Schema 定义 - tests/test_alignment.py - 完整测试套件
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framework_v2/tests/test_alignment.py
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framework_v2/tests/test_alignment.py
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"""
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数据对齐测试
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验证跨市场对齐器的正确性
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"""
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import sys
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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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# 添加项目根目录
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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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def test_factor_alignment():
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"""测试因子对齐"""
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from framework_v2.shared.data.alignment import CrossMarketAligner
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print("=" * 60)
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print(" 测试 1: 因子对齐")
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print("=" * 60)
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# 创建模拟数据
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# 美股日历（比 A 股少几天）
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us_dates = pd.date_range('2024-01-01', periods=10, freq='B')
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us_dates = us_dates.delete([2, 5]) # 删除 2 天（模拟美股假日）
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# A 股日历（完整）
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cn_dates = pd.date_range('2024-01-01', periods=10, freq='B')
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# 因子值（美股日历）
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factor_values = [0.1, 0.15, 0.18, 0.16, 0.20, 0.22, 0.25, 0.23]
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factor_series = pd.Series(factor_values, index=us_dates)
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print(f"\n源日历（美股）: {len(us_dates)} 天")
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print(f"目标日历（A股）: {len(cn_dates)} 天")
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print(f"因子值（源日历）:")
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print(factor_series)
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# 对齐
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aligner = CrossMarketAligner(target_calendar=cn_dates)
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aligned = aligner.align_factor(factor_series, us_dates, code='^GSPC')
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print(f"\n对齐后因子值:")
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print(aligned)
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# 验证
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assert len(aligned) == len(cn_dates), "对齐后长度应该等于目标日历"
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assert 'value' in aligned.columns, "应该有 'value' 列"
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assert 'is_filled' in aligned.columns, "应该有 'is_filled' 列"
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# 检查填充值
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filled_count = aligned['is_filled'].sum()
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print(f"\n填充值数量: {filled_count}")
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print("\n✓ 测试通过")
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return True
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def test_returns_alignment():
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"""测试收益率对齐"""
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from framework_v2.shared.data.alignment import CrossMarketAligner
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print("\n" + "=" * 60)
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print(" 测试 2: 收益率对齐")
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print("=" * 60)
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# 创建模拟价格数据
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# 美股日历
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us_dates = pd.date_range('2024-01-01', periods=10, freq='B')
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us_dates = us_dates.delete([2, 5])
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# A 股日历
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cn_dates = pd.date_range('2024-01-01', periods=10, freq='B')
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# 价格（美股日历）
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prices = [100, 101, 102, 101, 103, 104, 105, 104]
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close_series = pd.Series(prices, index=us_dates, dtype=float)
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print(f"\n源价格（美股日历）:")
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print(close_series)
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# 对齐收益率
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aligner = CrossMarketAligner(target_calendar=cn_dates)
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returns = aligner.align_returns(close_series, code='^GSPC')
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print(f"\n对齐后收益率（A股日历）:")
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print(returns)
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# 验证
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assert len(returns) == len(cn_dates), "收益率长度应该等于目标日历"
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assert returns.index.equals(cn_dates), "收益率索引应该等于目标日历"
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assert not returns.isna().any(), "收益率不应该有 NaN"
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assert returns.iloc[0] == 0.0, "首日收益率应该为 0"
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# 检查休市日收益率（应该为 0）
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# 美股休市日，价格 ffill，收益率 = 0
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print(f"\n收益率统计:")
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print(f" 最小值: {returns.min():.4f}")
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print(f" 最大值: {returns.max():.4f}")
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print(f" 均值: {returns.mean():.4f}")
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print("\n✓ 测试通过")
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return True
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def test_multi_asset_alignment():
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"""测试多标的对齐"""
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from framework_v2.shared.data.alignment import CrossMarketAligner
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print("\n" + "=" * 60)
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print(" 测试 3: 多标的收益率对齐")
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print("=" * 60)
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# A 股日历
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cn_dates = pd.date_range('2024-01-01', periods=10, freq='B')
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# 多个标的（不同日历）
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close_dict = {}
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# 标的 1: 完整数据
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prices1 = 100 + np.cumsum(np.random.randn(10))
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close_dict['^GSPC'] = pd.Series(prices1, index=cn_dates, dtype=float)
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# 标的 2: 少 2 天
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us_dates2 = cn_dates.delete([2, 5])
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prices2 = 100 + np.cumsum(np.random.randn(8))
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close_dict['^IXIC'] = pd.Series(prices2, index=us_dates2, dtype=float)
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# 标的 3: 完整数据
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prices3 = 100 + np.cumsum(np.random.randn(10))
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close_dict['931862.CSI'] = pd.Series(prices3, index=cn_dates, dtype=float)
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print(f"\n标的数量: {len(close_dict)}")
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for code, close in close_dict.items():
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print(f" {code}: {len(close)} 天")
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# 对齐多标的
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aligner = CrossMarketAligner(target_calendar=cn_dates)
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returns_df = aligner.align_multi_asset(close_dict)
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print(f"\n对齐后收益率 DataFrame:")
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print(returns_df.head())
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# 验证
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assert len(returns_df) == len(cn_dates), "收益率 DataFrame 长度应该等于目标日历"
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assert returns_df.index.equals(cn_dates), "索引应该等于目标日历"
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assert not returns_df.isna().any().any(), "收益率 DataFrame 不应该有 NaN"
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assert len(returns_df.columns) == len(close_dict), "列数应该等于标的数"
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print(f"\n✓ 测试通过")
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return True
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def test_signal_returns_alignment():
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"""测试信号与收益率对齐"""
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from framework_v2.shared.data.alignment import CrossMarketAligner
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print("\n" + "=" * 60)
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print(" 测试 4: 信号与收益率对齐验证")
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print("=" * 60)
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# A 股日历
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cn_dates = pd.date_range('2024-01-01', periods=10, freq='B')
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# 信号（多 2 天）
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signal_dates = cn_dates.union(pd.date_range('2024-01-15', periods=2, freq='B'))
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signals = pd.DataFrame({
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'signal': ['^GSPC,^IXIC'] * len(signal_dates)
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}, index=signal_dates)
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# 收益率（正常）
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returns_df = pd.DataFrame(
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np.random.randn(len(cn_dates), 2) * 0.02,
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index=cn_dates,
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columns=['^GSPC', '^IXIC']
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)
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print(f"\n信号: {len(signals)} 天")
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print(f"收益: {len(returns_df)} 天")
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# 验证对齐
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aligner = CrossMarketAligner(target_calendar=cn_dates)
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aligned_signals, aligned_returns = aligner.validate_alignment(signals, returns_df)
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print(f"\n对齐后:")
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print(f" 信号: {len(aligned_signals)} 天")
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print(f" 收益: {len(aligned_returns)} 天")
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# 验证
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assert len(aligned_signals) == len(aligned_returns), "对齐后长度应该一致"
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assert aligned_signals.index.equals(aligned_returns.index), "索引应该一致"
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print("\n✓ 测试通过")
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return True
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def test_ffill_trap():
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"""测试 ffill 陷阱（错误 vs 正确）"""
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from framework_v2.shared.data.alignment import CrossMarketAligner
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print("\n" + "=" * 60)
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print(" 测试 5: ffill 陷阱对比")
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print("=" * 60)
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# 美股日历
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us_dates = pd.date_range('2024-01-01', periods=5, freq='B')
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us_dates = us_dates.delete([2]) # 第 3 天休市
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# A 股日历
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cn_dates = pd.date_range('2024-01-01', periods=5, freq='B')
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# 价格
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prices = pd.Series([100, 101, 102, 103], index=us_dates, dtype=float)
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print(f"\n原始价格（美股日历）:")
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print(prices)
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# ❌ 错误做法：先计算收益率，再 ffill
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print("\n❌ 错误做法：先 pct_change，再 reindex")
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returns_wrong = prices.pct_change()
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print("步骤 1 - 收益率:")
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print(returns_wrong)
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returns_wrong_aligned = returns_wrong.reindex(cn_dates, method='ffill')
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print("\n步骤 2 - reindex + ffill:")
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print(returns_wrong_aligned)
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print("⚠ 问题：第 3 天复制了第 2 天的 +1% 收益率！")
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# ✅ 正确做法：先 ffill 价格，再计算收益率
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print("\n✅ 正确做法：先 reindex 价格，再 pct_change")
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prices_aligned = prices.reindex(cn_dates, method='ffill')
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print("步骤 1 - 价格 reindex:")
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print(prices_aligned)
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returns_correct = prices_aligned.pct_change(fill_method=None)
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returns_correct.iloc[0] = 0.0
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print("\n步骤 2 - pct_change:")
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print(returns_correct)
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print("✓ 第 3 天收益率 = 0%（价格不变）")
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# 验证
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aligner = CrossMarketAligner(target_calendar=cn_dates)
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returns = aligner.align_returns(prices, code='TEST')
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assert returns.iloc[2] == 0.0, "休市日收益率应该为 0"
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print("\n✓ 测试通过")
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return True
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if __name__ == '__main__':
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print("\n" + "=" * 60)
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print(" 跨市场数据对齐器测试")
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print("=" * 60)
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tests = [
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("因子对齐", test_factor_alignment),
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("收益率对齐", test_returns_alignment),
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("多标的对齐", test_multi_asset_alignment),
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("信号与收益对齐", test_signal_returns_alignment),
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("ffill 陷阱", test_ffill_trap),
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]
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results = []
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for name, test_func in tests:
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try:
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success = test_func()
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results.append((name, success))
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except Exception as e:
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print(f"\n✗ {name} 失败: {e}")
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import traceback
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traceback.print_exc()
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results.append((name, False))
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# 总结
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print("\n" + "=" * 60)
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print(" 测试总结")
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print("=" * 60)
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passed = sum(1 for _, success in results if success)
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total = len(results)
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for name, success in results:
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status = "✓ 通过" if success else "✗ 失败"
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print(f" {status} - {name}")
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print(f"\n总计: {passed}/{total} 通过")
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sys.exit(0 if passed == total else 1)
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