test(debug): 添加因子回测一致性问题的调试测试套件

- 分析GTJA_alpha032等因子在不同LOOKBACK_DAYS下的差异来源
- 验证cs_rank嵌套和截面股票数量对结果的影响
- 测试ts_rank NaN处理和除法除零修复

tests/debug/test_bug_fixes.py

@@ -0,0 +1,146 @@
+"""
+验证 ts_rank 和除法 Bug 修复的测试
+"""
+
+import polars as pl
+import numpy as np
+from src.factors import FactorEngine
+
+
+def test_ts_rank_nan_handling():
+    """测试 ts_rank 正确处理 NaN 值，不应该将 NaN 转换为 0.0"""
+    print("=" * 60)
+    print("测试 ts_rank NaN 处理")
+    print("=" * 60)
+
+    # 创建包含 NaN 的测试数据
+    df = pl.DataFrame(
+        {
+            "trade_date": ["20240101", "20240101", "20240101", "20240101", "20240101"]
+            * 2,
+            "ts_code": ["000001.SZ"] * 5 + ["000002.SZ"] * 5,
+            "close": [
+                1.0,
+                2.0,
+                np.nan,
+                4.0,
+                5.0,  # 股票1：第3个是NaN
+                2.0,
+                3.0,
+                4.0,
+                5.0,
+                6.0,
+            ],  # 股票2：正常数据
+        }
+    )
+
+    print("输入数据:")
+    print(df)
+
+    # 使用引擎计算 ts_rank
+    engine = FactorEngine()
+    # 手动设置数据（这里用简单方式）
+
+    # 直接测试 translator
+    from src.factors.translator import PolarsTranslator
+    from src.factors.parser import FormulaParser
+    from src.factors.registry import FunctionRegistry
+
+    parser = FormulaParser(FunctionRegistry())
+    ast = parser.parse("ts_rank(close, 3)")
+
+    translator = PolarsTranslator()
+    expr = translator.translate(ast)
+
+    result = df.with_columns([expr.alias("ts_rank_result")])
+
+    print("\nts_rank 结果:")
+    print(result.select(["trade_date", "ts_code", "close", "ts_rank_result"]))
+
+    # 验证：NaN 输入应该产生 NaN 输出，而不是 0.0
+    stock1_data = result.filter(pl.col("ts_code") == "000001.SZ")
+    nan_input_row = stock1_data.filter(pl.col("close").is_nan())
+
+    if nan_input_row.height > 0:
+        rank_value = nan_input_row["ts_rank_result"][0]
+        if np.isnan(rank_value):
+            print("\n[PASS] NaN 输入正确产生了 NaN 输出")
+            return True
+        elif rank_value == 0.0:
+            print(f"\n[FAIL] NaN 输入被错误转换为 0.0！")
+            return False
+        else:
+            print(f"\n[FAIL] 意外的输出值: {rank_value}")
+            return False
+    else:
+        print("\n[SKIP] 没有找到 NaN 输入行")
+        return False
+
+
+def test_division_by_zero():
+    """测试除法处理除零情况，不应该产生 NaN/inf"""
+    print("\n" + "=" * 60)
+    print("测试除法除零处理")
+    print("=" * 60)
+
+    # 创建包含零的数据
+    df = pl.DataFrame(
+        {
+            "trade_date": ["20240101", "20240101", "20240101"],
+            "ts_code": ["000001.SZ", "000002.SZ", "000003.SZ"],
+            "numerator": [100.0, 100.0, 100.0],
+            "denominator": [10.0, 0.0, 5.0],  # 中间那个是0
+        }
+    )
+
+    print("输入数据:")
+    print(df)
+
+    # 直接测试 translator
+    from src.factors.translator import PolarsTranslator
+    from src.factors.parser import FormulaParser
+    from src.factors.registry import FunctionRegistry
+
+    parser = FormulaParser(FunctionRegistry())
+    ast = parser.parse("numerator / denominator")
+
+    translator = PolarsTranslator()
+    expr = translator.translate(ast)
+
+    result = df.with_columns([expr.alias("division_result")])
+
+    print("\n除法结果:")
+    print(result.select(["ts_code", "numerator", "denominator", "division_result"]))
+
+    # 验证：除零应该产生 Null，而不是 NaN/inf
+    zero_row = result.filter(pl.col("denominator") == 0.0)
+    if zero_row.height > 0:
+        div_value = zero_row["division_result"][0]
+        if div_value is None:
+            print("\n[PASS] 除零正确产生了 Null")
+            return True
+        elif np.isnan(div_value) or np.isinf(div_value):
+            print(f"\n[FAIL] 除零产生了 NaN/inf: {div_value}")
+            return False
+        else:
+            print(f"\n[?] 除零产生了: {div_value} (可能是其他有效值)")
+            return True  # 也可能是预期的行为
+    else:
+        print("\n[SKIP] 没有找到除零行")
+        return False
+
+
+if __name__ == "__main__":
+    test1_pass = test_ts_rank_nan_handling()
+    test2_pass = test_division_by_zero()
+
+    print("\n" + "=" * 60)
+    print("测试结果汇总")
+    print("=" * 60)
+    print(f"ts_rank NaN 处理: {'PASS' if test1_pass else 'FAIL'}")
+    print(f"除法除零处理: {'PASS' if test2_pass else 'FAIL'}")
+
+    if test1_pass and test2_pass:
+        print("\n所有测试通过！")
+    else:
+        print("\n部分测试失败，请检查实现")