"""
因子对比验证测试

验证新框架的 MomentumFactor 与现有实现输出一致
"""

import sys
import pandas as pd
import numpy as np
from pathlib import Path

# 添加项目根目录
project_root = Path(__file__).parent.parent.parent
sys.path.insert(0, str(project_root))


def test_momentum_factor_parity():
    """验证新因子与旧因子输出一致"""
    
    print("=" * 60)
    print("  MomentumFactor 对比测试")
    print("=" * 60)
    
    # 1. 加载测试数据
    print("\n1. 加载测试数据...")
    test_data_path = project_root / 'data' / 'index_history_data'
    
    # 使用纳指100数据测试
    import glob
    ndx_files = glob.glob(str(test_data_path / '*NDX*'))
    if ndx_files:
        test_file = ndx_files[0]
        data = pd.read_csv(test_file, index_col=0, parse_dates=True)
        print(f"   ✓ 加载 {test_file}")
        print(f"   数据范围: {data.index[0]} ~ {data.index[-1]}")
        print(f"   数据长度: {len(data)} 条")
    else:
        print("   ⚠ 未找到测试数据，使用模拟数据")
        # 生成模拟数据
        np.random.seed(42)
        dates = pd.date_range('2020-01-01', periods=500, freq='B')
        prices = 100 * np.cumprod(1 + np.random.randn(500) * 0.02)
        data = pd.DataFrame({
            'close': prices,
            'open': prices * 0.99,
            'high': prices * 1.01,
            'low': prices * 0.98,
            'volume': np.random.randint(1000000, 10000000, 500)
        }, index=dates)
    
    # 2. 计算旧因子
    print("\n2. 计算旧因子（strategies/shared/factors/momentum.py）...")
    from strategies.shared.factors.momentum import MomentumFactor as OldMomentum
    
    old_factor = OldMomentum(n_days=25, weighted=True, crash_filter=True)
    old_result = old_factor.compute(data)
    print(f"   ✓ 旧因子计算完成")
    print(f"   结果范围: {old_result.min():.4f} ~ {old_result.max():.4f}")
    print(f"   NaN 数量: {old_result.isna().sum()}")
    
    # 3. 计算新因子
    print("\n3. 计算新因子（framework_v2/shared/factors/momentum.py）...")
    from framework_v2.shared.factors.momentum import MomentumFactor as NewMomentum
    
    new_factor = NewMomentum(n_days=25, weighted=True, crash_filter=True)
    new_result = new_factor.compute(data)
    print(f"   ✓ 新因子计算完成")
    print(f"   结果范围: {new_result.min():.4f} ~ {new_result.max():.4f}")
    print(f"   NaN 数量: {new_result.isna().sum()}")
    
    # 4. 对比结果
    print("\n4. 对比结果...")
    
    # 检查索引是否一致
    if not old_result.index.equals(new_result.index):
        print("   ✗ 索引不一致")
        return False
    print("   ✓ 索引一致")
    
    # 检查数值差异
    diff = (old_result - new_result).abs()
    max_diff = diff.max()
    mean_diff = diff.mean()
    
    print(f"   最大差异: {max_diff:.6e}")
    print(f"   平均差异: {mean_diff:.6e}")
    
    # 允许浮点数精度误差（1e-10）
    tolerance = 1e-10
    if max_diff < tolerance:
        print(f"   ✓ 差异在容差范围内 (< {tolerance:.0e})")
        print("\n" + "=" * 60)
        print("  ✓ 测试通过：新旧因子输出完全一致！")
        print("=" * 60)
        return True
    else:
        print(f"   ✗ 差异超出容差范围")
        print("\n" + "=" * 60)
        print("  ✗ 测试失败：新旧因子输出不一致")
        print("=" * 60)
        
        # 打印前10个差异点
        diff_nonzero = diff[diff > tolerance]
        if len(diff_nonzero) > 0:
            print(f"\n   前10个差异点:")
            for date, val in diff_nonzero.head(10).items():
                old_val = old_result.loc[date]
                new_val = new_result.loc[date]
                print(f"   {date}: 旧={old_val:.6f}, 新={new_val:.6f}, 差异={val:.6e}")
        
        return False


if __name__ == '__main__':
    success = test_momentum_factor_parity()
    sys.exit(0 if success else 1)