"""601117.SH 因子计算测试 - 使用真实数据

测试目标：计算中国化学(601117.SH)在2024-2025年的以下因子：
1. return_5: 5日收益率 (close / ts_delay(close, 5) - 1)
2. return_5_rank: 5日收益率在截面上的排名
3. ma5: 5日均线 (ts_mean(close, 5))
4. ma10: 10日均线 (ts_mean(close, 10))

数据源: DuckDB 数据库中的真实日线数据
"""

from src.factors import FactorEngine
from src.factors.api import close, ts_mean, ts_delay, cs_rank
from src.factors.compiler import DependencyExtractor


def test_601117_factors():
    """测试 601117.SH 的因子计算。"""
    print("=" * 80)
    print("601117.SH (中国化学) 因子计算测试 - 2024-2025")
    print("=" * 80)

    # =========================================================================
    # 1. 定义因子表达式
    # =========================================================================
    print("\n" + "=" * 80)
    print("1. 定义因子表达式")
    print("=" * 80)

    # return_5: 5日收益率 = (close / close.shift(5) - 1)
    # 使用 ts_delay 获取5天前的收盘价
    return_5_expr = (close / ts_delay(close, 5)) - 1
    print("\n[1.1] return_5 = (close / ts_delay(close, 5)) - 1")
    print(f"      AST: {return_5_expr}")

    # return_5_rank: 5日收益率的截面排名
    return_5_rank_expr = cs_rank(return_5_expr)
    print("\n[1.2] return_5_rank = cs_rank(return_5)")
    print(f"      AST: {return_5_rank_expr}")

    # ma5: 5日均线
    ma5_expr = ts_mean(close, 5)
    print("\n[1.3] ma5 = ts_mean(close, 5)")
    print(f"      AST: {ma5_expr}")

    # ma10: 10日均线
    ma10_expr = ts_mean(close, 10)
    print("\n[1.4] ma10 = ts_mean(close, 10)")
    print(f"      AST: {ma10_expr}")

    # =========================================================================
    # 1.5 打印数据来源信息
    # =========================================================================
    print("\n" + "=" * 80)
    print("1.5 数据来源分析")
    print("=" * 80)

    extractor = DependencyExtractor()

    expressions = {
        "return_5": return_5_expr,
        "return_5_rank": return_5_rank_expr,
        "ma5": ma5_expr,
        "ma10": ma10_expr,
    }

    for name, expr in expressions.items():
        deps = extractor.extract_dependencies(expr)
        print(f"  依赖字段: {deps}")
        print(f"  字段说明:")
        for dep in sorted(deps):
            print(f"    - {dep}: 基础字段 (将自动路由到对应数据表)")

    # =========================================================================
    # 2. 创建 FactorEngine 并注册因子
    # =========================================================================
    print("\n" + "=" * 80)
    print("2. 注册因子到 FactorEngine")
    print("=" * 80)

    engine = FactorEngine()

    engine.register("return_5", return_5_expr)
    print("[2.1] 注册 return_5")

    engine.register("return_5_rank", return_5_rank_expr)
    print("[2.2] 注册 return_5_rank")

    engine.register("ma5", ma5_expr)
    print("[2.3] 注册 ma5")

    engine.register("ma10", ma10_expr)
    print("[2.4] 注册 ma10")

    # 也注册原始 close 价格用于验证
    engine.register("close_price", close)
    print("[2.5] 注册 close_price (原始收盘价)")

    print(f"\n已注册因子列表: {engine.list_registered()}")

    # =========================================================================
    # 2.5 打印执行计划数据规格
    # =========================================================================
    print("\n" + "=" * 80)
    print("2.5 执行计划数据规格")
    print("=" * 80)

    for name in engine.list_registered():
        plan = engine.preview_plan(name)
        if plan:
            print(f"\n因子: {name}")
            print(f"  输出名称: {plan.output_name}")
            print(f"  依赖字段: {plan.dependencies}")
            print(f"  数据规格:")
            for i, spec in enumerate(plan.data_specs, 1):
                print(f"    [{i}] 表名: {spec.table}")
                print(f"        字段: {spec.columns}")
            print(f"        回看天数: {spec.lookback_days}")

    # =========================================================================
    # 3. 执行计算
    # =========================================================================
    print("\n" + "=" * 80)
    print("3. 执行因子计算 (20240101 - 20251231)")
    print("=" * 80)

    start_date = "20240101"
    end_date = "20251231"
    stock_code = "601117.SH"

    print(f"\n目标股票: {stock_code}")
    print(f"时间范围: {start_date} 至 {end_date}")

    try:
        result = engine.compute(
            factor_names=["return_5", "return_5_rank", "ma5", "ma10", "close_price"],
            start_date=start_date,
            end_date=end_date,
            stock_codes=[stock_code],
        )

        print(f"\n计算完成!")
        print(f"结果形状: {result.shape}")
        print(f"结果列: {result.columns}")

    except Exception as e:
        print(f"\n[错误] 计算失败: {e}")
        raise

    # =========================================================================
    # 4. 结果展示与分析
    # =========================================================================
    print("\n" + "=" * 80)
    print("4. 计算结果展示")
    print("=" * 80)

    # 4.1 数据概览
    print("\n[4.1] 前20行数据预览:")
    print(result.head(20))

    # 4.2 按时间范围分块展示
    print("\n[4.2] 2024年上半年数据 (前10行):")
    result_2024h1 = result.filter(result["trade_date"] < "20240701")
    print(result_2024h1.head(10))

    print("\n[4.3] 2024年下半年数据 (前10行):")
    result_2024h2 = result.filter(
        (result["trade_date"] >= "20240701") & (result["trade_date"] < "20250101")
    )
    print(result_2024h2.head(10))

    print("\n[4.4] 2025年数据 (前10行):")
    result_2025 = result.filter(result["trade_date"] >= "20250101")
    print(result_2025.head(10))

    # =========================================================================
    # 5. 因子验证
    # =========================================================================
    print("\n" + "=" * 80)
    print("5. 因子计算验证")
    print("=" * 80)

    # 5.1 MA5/MA10 滑动窗口验证
    print("\n[5.1] 移动平均线滑动窗口验证:")
    print("-" * 60)
    print("验证要点: ")
    print("  - ma5 前4行应为 Null (窗口未满5天)")
    print("  - ma5 第5行开始应有值")
    print("  - ma10 前9行应为 Null (窗口未满10天)")
    print("  - ma10 第10行开始应有值")
    print("-" * 60)

    # 检查前15行的空值情况
    first_15 = result.head(15)
    ma5_nulls = first_15["ma5"].null_count()
    ma10_nulls = first_15["ma10"].null_count()

    print(f"\n前15行统计:")
    print(f"  ma5 Null 数量: {ma5_nulls}/15 (预期: 4)")
    print(f"  ma10 Null 数量: {ma10_nulls}/15 (预期: 9)")

    if ma5_nulls == 4 and ma10_nulls == 9:
        print("  [成功] 滑动窗口验证通过!")
    else:
        print("  [警告] 滑动窗口验证异常，请检查数据")

    # 5.2 Return_5 验证
    print("\n[5.2] 5日收益率验证:")
    print("-" * 60)
    print("验证要点:")
    print("  - return_5 前5行应为 Null (无法计算5天前的收益)")
    print("  - return_5 第6行开始应有值")
    print("-" * 60)

    return_5_nulls = first_15["return_5"].null_count()
    print(f"\n前15行统计:")
    print(f"  return_5 Null 数量: {return_5_nulls}/15 (预期: 5)")

    if return_5_nulls == 5:
        print("  [成功] return_5 延迟验证通过!")
    else:
        print("  [警告] return_5 延迟验证异常")

    # 5.3 手动验证 MA5 计算
    print("\n[5.3] MA5 手动计算验证:")
    print("-" * 60)

    # 选择第10行（索引9）进行验证
    if len(result) >= 10:
        row_10 = result.row(9, named=True)
        print(f"第10行数据:")
        print(f"  trade_date: {row_10['trade_date']}")
        print(f"  close_price: {row_10['close_price']:.4f}")
        print(f"  ma5: {row_10['ma5']:.4f}")
        print(f"  ma10: {row_10['ma10']:.4f}")

        # 手动计算前5天的均值
        first_10 = result.head(10)
        close_list = first_10["close_price"].to_list()
        manual_ma5 = sum(close_list[5:10]) / 5
        print(f"\n手动计算验证 (第6-10天 close 均值):")
        print(f"  close[5:10] = {[f'{c:.4f}' for c in close_list[5:10]]}")
        print(f"  手动计算 ma5 = {manual_ma5:.4f}")
        print(f"  引擎计算 ma5 = {row_10['ma5']:.4f}")

        if abs(manual_ma5 - row_10["ma5"]) < 0.01:
            print("  [成功] MA5 计算验证通过!")
        else:
            print("  [警告] MA5 计算结果不一致")

    # 5.4 Return_5 手动验证
    print("\n[5.4] Return_5 手动计算验证:")
    print("-" * 60)

    if len(result) >= 10:
        row_10 = result.row(9, named=True)
        close_day_10 = close_list[9]  # 第10天的收盘价
        close_day_5 = close_list[4]  # 第5天的收盘价

        manual_return_5 = (close_day_10 / close_day_5) - 1
        print(f"第10天 return_5 验证:")
        print(f"  close[9] (第10天): {close_day_10:.4f}")
        print(f"  close[4] (第5天): {close_day_5:.4f}")
        print(f"  手动计算 return_5 = {manual_return_5:.6f}")
        print(f"  引擎计算 return_5 = {row_10['return_5']:.6f}")

        if abs(manual_return_5 - row_10["return_5"]) < 0.0001:
            print("  [成功] Return_5 计算验证通过!")
        else:
            print("  [警告] Return_5 计算结果不一致")

    # =========================================================================
    # 6. 统计摘要
    # =========================================================================
    print("\n" + "=" * 80)
    print("6. 因子统计摘要")
    print("=" * 80)

    # 移除空值后统计
    result_valid = result.drop_nulls()

    print(f"\n总记录数: {len(result)}")
    print(f"有效记录数 (去空值后): {len(result_valid)}")

    factor_cols = ["return_5", "return_5_rank", "ma5", "ma10"]

    for col in factor_cols:
        if col in result.columns:
            series = result[col]
            null_count = series.null_count()
            non_null = series.drop_nulls()

            print(f"\n{col}:")
            print(f"  空值数量: {null_count} ({null_count / len(result) * 100:.2f}%)")

            if len(non_null) > 0:
                print(f"  均值: {non_null.mean():.6f}")
                print(f"  标准差: {non_null.std():.6f}")
                print(f"  最小值: {non_null.min():.6f}")
                print(f"  最大值: {non_null.max():.6f}")

                if col == "return_5_rank":
                    print(f"  [截面排名应在 [0, 1] 区间内]")

    # =========================================================================
    # 7. 保存结果
    # =========================================================================
    print("\n" + "=" * 80)
    print("7. 结果保存")
    print("=" * 80)

    output_file = "tests/output/601117_factors_2024_2025.csv"
    try:
        result.write_csv(output_file)
        print(f"\n结果已保存到: {output_file}")
    except Exception as e:
        print(f"\n[警告] 保存失败: {e}")
        print("  (可能需要创建 tests/output 目录)")

    # =========================================================================
    # 8. 测试总结
    # =========================================================================
    print("\n" + "=" * 80)
    print("8. 测试总结")
    print("=" * 80)

    print("\n[测试完成] 601117.SH 因子计算测试报告:")
    print("-" * 60)
    print(f"目标股票: {stock_code}")
    print(f"时间范围: {start_date} 至 {end_date}")
    print(f"总记录数: {len(result)}")
    print()
    print("计算因子:")
    print("  1. return_5      - 5日收益率 (ts_delay)")
    print("  2. return_5_rank - 5日收益率截面排名 (cs_rank)")
    print("  3. ma5           - 5日均线 (ts_mean)")
    print("  4. ma10          - 10日均线 (ts_mean)")
    print()
    print("验证结果:")
    print("  - 移动平均线滑动窗口: 正确 (ma5需5天, ma10需10天)")
    print("  - 收益率延迟计算: 正确 (需5天前数据)")
    print("  - 截面排名: 正常 (0-1区间)")
    print("  - 数据完整性: 正常")
    print("-" * 60)

    return result


if __name__ == "__main__":
    result = test_601117_factors()