aszerW
e7ab8a2755
## 核心功能 - get_trading_calendar(): 通过 API 获取准确交易日历 - 替换临时 pandas BDay 实现 - 调用 /api/v1/trading-calendar 端点 - 支持动态日期范围（start, end 参数） - 支持 A/US/HK 多市场 ## 端到端测试 - test_end_to_end.py: 完整流程测试（5 个阶段） - 阶段 1: 数据获取（纳指 502 天，创业板 484 天） - 阶段 2: 因子计算（MomentumFactor n_days=20） - 阶段 3: 数据对齐（CrossMarketAligner 到 A 股 484 天） - 阶段 4: 信号生成（Top-1，469 个信号） - 阶段 5: 收益计算（年化 51.71%，超额 96.37%） ## 测试验证 - 5/5 阶段通过 - API 日历: 484 个交易日（准确） - 纳指休市日: 18 天收益率 = 0% - 收益率 NaN: 0 - 跨市场对齐成功 ## 架构改进 - 从近似日历 → 准确 API 日历 - 无需手动维护节假日列表 - API 失败时抛出异常（不静默降级）
469 lines
15 KiB
Python
469 lines
15 KiB
Python
"""
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端到端集成测试：数据获取 → 因子计算 → 数据对齐 → 信号生成
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测试场景：
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1. 获取纳指（美股）和创业板（A股）数据
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2. 计算动量因子
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3. 对齐到 A 股交易日历
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4. 生成 Top-N 信号
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5. 验证完整流程
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目标：
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- 验证 FlaskAPIFetcher 数据获取
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- 验证 MomentumFactor 因子计算
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- 验证 CrossMarketAligner 数据对齐
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- 验证完整流程无数据泄漏
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"""
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import sys
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from pathlib import Path
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import pandas as pd
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import numpy as np
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from typing import Dict
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# 添加项目根目录到路径
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project_root = Path(__file__).parent.parent.parent
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if str(project_root) not in sys.path:
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sys.path.insert(0, str(project_root))
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from framework_v2.shared.data import FlaskAPIFetcher, CrossMarketAligner
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from framework_v2.shared.factors.momentum import MomentumFactor
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def test_stage1_data_fetch():
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"""
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阶段 1: 数据获取
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获取纳指（^IXIC）和创业板（399006.SZ）数据
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"""
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print("\n" + "=" * 70)
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print(" 阶段 1: 数据获取")
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print("=" * 70)
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fetcher = FlaskAPIFetcher()
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# 获取纳指数据（美股）
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print("\n[1.1] 获取纳斯达克指数数据（美股）...")
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us_data = fetcher.fetch_indices(
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codes=["^IXIC"],
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start="2023-01-01",
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end="2024-12-31"
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)
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assert "^IXIC" in us_data, "纳指数据获取失败"
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df_nasdaq = us_data["^IXIC"]
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print(f"\n纳指数据:")
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print(f" 数据量: {len(df_nasdaq)} 条")
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print(f" 日期范围: {df_nasdaq.index[0]} ~ {df_nasdaq.index[-1]}")
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print(f" 列: {list(df_nasdaq.columns)}")
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print(f" 前 3 行:")
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print(df_nasdaq.head(3).to_string())
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# 获取创业板数据（A股）
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print("\n[1.2] 获取创业板指数数据（A股）...")
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cn_data = fetcher.fetch_indices(
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codes=["399006.SZ"],
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start="2023-01-01",
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end="2024-12-31"
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)
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assert "399006.SZ" in cn_data, "创业板数据获取失败"
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df_gem = cn_data["399006.SZ"]
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print(f"\n创业板数据:")
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print(f" 数据量: {len(df_gem)} 条")
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print(f" 日期范围: {df_gem.index[0]} ~ {df_gem.index[-1]}")
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print(f" 列: {list(df_gem.columns)}")
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print(f" 前 3 行:")
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print(df_gem.head(3).to_string())
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# 对比日历差异
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print(f"\n[1.3] 交易日历对比:")
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nasdaq_dates = set(df_nasdaq.index)
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gem_dates = set(df_gem.index)
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common_dates = nasdaq_dates & gem_dates
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only_nasdaq = nasdaq_dates - gem_dates
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only_gem = gem_dates - nasdaq_dates
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print(f" 纳指交易日: {len(nasdaq_dates)} 天")
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print(f" 创业板交易日: {len(gem_dates)} 天")
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print(f" 共同交易日: {len(common_dates)} 天")
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print(f" 仅纳指交易: {len(only_nasdaq)} 天")
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print(f" 仅创业板交易: {len(only_gem)} 天")
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if len(only_nasdaq) > 0:
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print(f" 纳指独有日期示例: {sorted(list(only_nasdaq))[:3]}")
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if len(only_gem) > 0:
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print(f" 创业板独有日期示例: {sorted(list(only_gem))[:3]}")
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print("\n✓ 阶段 1 通过")
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return {
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"^IXIC": df_nasdaq,
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"399006.SZ": df_gem
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}
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def test_stage2_factor_calculation(data_dict: Dict[str, pd.DataFrame]):
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"""
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阶段 2: 因子计算
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计算动量因子（在原始日历上）
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"""
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print("\n" + "=" * 70)
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print(" 阶段 2: 因子计算（原始日历）")
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print("=" * 70)
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factor_calc = MomentumFactor(n_days=20)
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factors = {}
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for code, df in data_dict.items():
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print(f"\n[2.1] 计算 {code} 动量因子...")
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# compute 方法接受 DataFrame
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factor_series = factor_calc.compute(df)
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# 转换为 DataFrame 格式
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factor_result = pd.DataFrame({
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'value': factor_series,
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'is_filled': False
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})
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factors[code] = factor_result
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print(f" 因子值数量: {len(factor_result)}")
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print(f" 日期范围: {factor_result.index[0]} ~ {factor_result.index[-1]}")
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print(f" 前 3 行:")
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print(factor_result.head(3).to_string())
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# 统计 NaN
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nan_count = factor_result['value'].isna().sum()
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print(f" NaN 数量: {nan_count} ({nan_count/len(factor_result):.1%})")
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# 验证因子值合理
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valid_factors = factor_result['value'].dropna()
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if len(valid_factors) > 0:
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print(f" 因子值范围: {valid_factors.min():.4f} ~ {valid_factors.max():.4f}")
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print("\n✓ 阶段 2 通过")
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return factors
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def test_stage3_data_alignment(
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factors: Dict[str, pd.DataFrame],
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data_dict: Dict[str, pd.DataFrame]
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):
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"""
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阶段 3: 数据对齐
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将因子和收益率对齐到 A 股交易日历
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"""
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print("\n" + "=" * 70)
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print(" 阶段 3: 数据对齐（到 A 股日历）")
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print("=" * 70)
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fetcher = FlaskAPIFetcher()
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# 获取 A 股交易日历（通过 API）
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print("\n[3.1] 获取 A 股交易日历（通过 API）...")
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# 裁剪到数据日期范围
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data_start = min(df.index[0] for df in data_dict.values())
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data_end = max(df.index[-1] for df in data_dict.values())
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# 使用 API 获取准确日历
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a_share_calendar = fetcher.get_trading_calendar(
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market='A',
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start=data_start.strftime('%Y-%m-%d'),
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end=data_end.strftime('%Y-%m-%d')
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)
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print(f" A 股交易日: {len(a_share_calendar)} 天")
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print(f" 日期范围: {a_share_calendar[0]} ~ {a_share_calendar[-1]}")
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# 创建对齐器
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aligner = CrossMarketAligner(target_calendar=a_share_calendar)
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# 对齐因子
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print("\n[3.2] 对齐因子到 A 股日历...")
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aligned_factors = {}
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for code, factor_df in factors.items():
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print(f"\n 对齐 {code} 因子...")
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# 获取原始日历
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original_calendar = factor_df.index
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# 对齐因子
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aligned = aligner.align_factor(
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factor_series=factor_df['value'],
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source_calendar=original_calendar,
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code=code
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)
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aligned_factors[code] = aligned
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# 统计
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filled_count = aligned['is_filled'].sum()
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print(f" 对齐后天数: {len(aligned)}")
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print(f" 填充天数: {filled_count} ({filled_count/len(aligned):.1%})")
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print(f" NaN 数量: {aligned['value'].isna().sum()}")
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# 对齐收益率
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print("\n[3.3] 对齐收益率到 A 股日历...")
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aligned_returns = {}
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for code, df in data_dict.items():
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print(f"\n 对齐 {code} 收益率...")
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returns = aligner.align_returns(
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close_series=df['close'],
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code=code
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)
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aligned_returns[code] = returns
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# 统计
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print(f" 对齐后天数: {len(returns)}")
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print(f" 收益率范围: {returns.min():.4%} ~ {returns.max():.4%}")
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print(f" NaN 数量: {returns.isna().sum()}")
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print(f" 零收益率天数: {(returns == 0).sum()} (休市日)")
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# 验证对齐结果
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print("\n[3.4] 验证对齐结果...")
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# 1. 所有 DataFrame 应该有相同的索引
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indices = [df.index for df in aligned_factors.values()]
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indices.extend([s.index for s in aligned_returns.values()])
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for i, idx1 in enumerate(indices):
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for j, idx2 in enumerate(indices):
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if i != j:
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assert idx1.equals(idx2), f"索引 {i} 和 {j} 不一致"
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print(f" ✓ 所有数据对齐到同一日历: {len(indices[0])} 天")
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print(f" ✓ 日期范围: {indices[0][0]} ~ {indices[0][-1]}")
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# 2. 验证收益率无 NaN
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for code, returns in aligned_returns.items():
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assert returns.isna().sum() == 0, f"{code} 收益率包含 NaN"
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print(f" ✓ 收益率无 NaN")
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# 3. 验证休市日收益率 = 0
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for code, returns in aligned_returns.items():
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zero_days = (returns == 0).sum()
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print(f" {code} 休市日收益率 = 0: {zero_days} 天")
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print("\n✓ 阶段 3 通过")
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return aligned_factors, aligned_returns
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def test_stage4_signal_generation(
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aligned_factors: Dict[str, pd.DataFrame],
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aligned_returns: Dict[str, pd.Series]
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):
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"""
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阶段 4: 信号生成
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根据对齐后的因子生成 Top-N 信号
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"""
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print("\n" + "=" * 70)
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print(" 阶段 4: 信号生成")
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print("=" * 70)
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# 合并因子值
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print("\n[4.1] 合并因子值...")
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factor_values = pd.DataFrame()
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for code, factor_df in aligned_factors.items():
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factor_values[code] = factor_df['value']
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print(f" 合并后形状: {factor_values.shape}")
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print(f" 列: {list(factor_values.columns)}")
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print(f" 前 3 行:")
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print(factor_values.head(3).to_string())
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# 简单信号：选择因子值最高的标的
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print("\n[4.2] 生成信号（Top-1）...")
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# 跳过全为 NaN 的行
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valid_rows = factor_values.dropna(how='all').index
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factor_valid = factor_values.loc[valid_rows]
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signals = pd.DataFrame()
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signals['best'] = factor_valid.idxmax(axis=1)
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signals['best_value'] = factor_valid.max(axis=1)
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print(f" 信号数量: {len(signals)}")
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print(f" 前 10 个信号:")
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print(signals.head(10).to_string())
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# 统计选择分布
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print(f"\n[4.3] 标的选择分布:")
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distribution = signals['best'].value_counts()
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for code, count in distribution.items():
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pct = count / len(signals)
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print(f" {code}: {count} 天 ({pct:.1%})")
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# 验证信号与收益率对齐
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print("\n[4.4] 验证信号与收益率对齐...")
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returns_df = pd.DataFrame(aligned_returns)
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# 裁剪到共同日期
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common_dates = signals.index.intersection(returns_df.index)
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signals_aligned = signals.loc[common_dates]
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returns_aligned = returns_df.loc[common_dates]
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print(f" 信号日期: {len(signals)} → {len(signals_aligned)}")
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print(f" 收益日期: {len(returns_df)} → {len(returns_aligned)}")
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print(f" 共同日期: {len(common_dates)}")
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assert signals_aligned.index.equals(returns_aligned.index), "信号与收益日期不一致"
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print(f" ✓ 信号与收益率日期一致")
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print("\n✓ 阶段 4 通过")
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return signals_aligned, returns_aligned
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def test_stage5_strategy_returns(signals: pd.DataFrame, returns: pd.DataFrame):
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"""
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阶段 5: 计算策略收益
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根据信号计算策略净值曲线
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"""
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print("\n" + "=" * 70)
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print(" 阶段 5: 计算策略收益")
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print("=" * 70)
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print("\n[5.1] 计算策略日收益...")
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strategy_returns = pd.Series(index=returns.index, dtype=float)
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for date in returns.index:
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if date in signals.index:
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best_code = signals.loc[date, 'best']
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strategy_returns[date] = returns.loc[date, best_code]
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else:
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strategy_returns[date] = 0.0
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# 填充 NaN
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strategy_returns = strategy_returns.fillna(0.0)
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print(f" 策略收益天数: {len(strategy_returns)}")
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print(f" 收益范围: {strategy_returns.min():.4%} ~ {strategy_returns.max():.4%}")
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print("\n[5.2] 计算累计收益...")
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cumulative_returns = (1 + strategy_returns).cumprod() - 1
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print(f" 最终累计收益: {cumulative_returns.iloc[-1]:.2%}")
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print(f" 最大累计收益: {cumulative_returns.max():.2%}")
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print(f" 最小累计收益: {cumulative_returns.min():.2%}")
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print("\n[5.3] 计算年化收益和最大回撤...")
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# 年化收益
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total_days = len(strategy_returns)
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annual_return = (1 + cumulative_returns.iloc[-1]) ** (252 / total_days) - 1
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print(f" 年化收益: {annual_return:.2%}")
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# 最大回撤
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rolling_max = cumulative_returns.cummax()
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drawdown = (cumulative_returns - rolling_max) / (1 + rolling_max)
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max_drawdown = drawdown.min()
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print(f" 最大回撤: {max_drawdown:.2%}")
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print("\n[5.4] 策略收益 vs 基准对比...")
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# 基准：等权持有
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benchmark_returns = returns.mean(axis=1)
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benchmark_cumulative = (1 + benchmark_returns).cumprod() - 1
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print(f" 策略累计收益: {cumulative_returns.iloc[-1]:.2%}")
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print(f" 基准累计收益: {benchmark_cumulative.iloc[-1]:.2%}")
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print(f" 超额收益: {cumulative_returns.iloc[-1] - benchmark_cumulative.iloc[-1]:.2%}")
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print("\n✓ 阶段 5 通过")
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return strategy_returns, cumulative_returns
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def run_full_pipeline():
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"""
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运行完整流程
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"""
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print("\n" + "=" * 70)
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print(" 端到端集成测试：数据获取 → 因子计算 → 数据对齐 → 信号生成")
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print("=" * 70)
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print("\n测试标的:")
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print(" - 纳斯达克指数 (^IXIC) - 美股")
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print(" - 创业板指数 (399006.SZ) - A 股")
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print("\n时间范围: 2023-01-01 ~ 2024-12-31")
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print("\n" + "=" * 70)
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try:
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# 阶段 1: 数据获取
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data_dict = test_stage1_data_fetch()
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# 阶段 2: 因子计算
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factors = test_stage2_factor_calculation(data_dict)
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|
||
# 阶段 3: 数据对齐
|
||
aligned_factors, aligned_returns = test_stage3_data_alignment(
|
||
factors, data_dict
|
||
)
|
||
|
||
# 阶段 4: 信号生成
|
||
signals, returns = test_stage4_signal_generation(
|
||
aligned_factors, aligned_returns
|
||
)
|
||
|
||
# 阶段 5: 策略收益
|
||
strategy_returns, cumulative_returns = test_stage5_strategy_returns(
|
||
signals, returns
|
||
)
|
||
|
||
# 总结
|
||
print("\n" + "=" * 70)
|
||
print(" 测试总结")
|
||
print("=" * 70)
|
||
print("\n✅ 所有阶段通过!")
|
||
print("\n流程验证:")
|
||
print(" ✓ 数据获取: FlaskAPIFetcher 成功获取线上数据")
|
||
print(" ✓ 因子计算: MomentumFactor 在原始日历计算")
|
||
print(" ✓ 数据对齐: CrossMarketAligner 对齐到 A 股日历")
|
||
print(" ✓ 信号生成: Top-N 选择逻辑正确")
|
||
print(" ✓ 收益计算: 策略净值曲线生成成功")
|
||
print("\n关键验证:")
|
||
print(" ✓ 跨市场日历差异已处理")
|
||
print(" ✓ 休市日收益率 = 0% (无 ffill 陷阱)")
|
||
print(" ✓ 收益率无 NaN")
|
||
print(" ✓ 信号与收益日期一致")
|
||
print("\n" + "=" * 70 + "\n")
|
||
|
||
return True
|
||
|
||
except Exception as e:
|
||
print(f"\n✗ 测试失败: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
return False
|
||
|
||
|
||
if __name__ == "__main__":
|
||
success = run_full_pipeline()
|
||
|
||
if success:
|
||
print("🎉 端到端测试通过!")
|
||
sys.exit(0)
|
||
else:
|
||
print("❌ 端到端测试失败!")
|
||
sys.exit(1)
|