refactor: 代码审查修复 - 日期过滤、性能优化、数据泄露防护

- 修复 data_loader.py 财务数据日期过滤，支持按范围加载
- 优化 MADClipper 使用窗口函数替代 join，提升性能
- 修复训练日期边界问题，添加1天间隔避免数据泄露
- 新增 .gitignore 规则忽略训练输出目录

src/pipeline/processors/__init__.py

@@ -8,6 +8,7 @@ from src.pipeline.processors.processors import (
     MinMaxScaler,
     RankTransformer,
     Neutralizer,
+    MADClipper,
 )
 
 __all__ = [
@@ -18,4 +19,5 @@ __all__ = [
     "MinMaxScaler",
     "RankTransformer",
     "Neutralizer",
+    "MADClipper",
 ]

src/pipeline/processors/processors.py

@@ -3,9 +3,8 @@
 提供常用的数据预处理和转换处理器。
 """
 
-from typing import List, Optional, Dict, Any
+from typing import List, Optional
 import polars as pl
-import numpy as np
 
 from src.pipeline.core import BaseProcessor, PipelineStage
 from src.pipeline.registry import PluginRegistry
@@ -227,6 +226,64 @@ class Neutralizer(BaseProcessor):
         return result
 
 
+@PluginRegistry.register_processor("mad_clipper")
+class MADClipper(BaseProcessor):
+    """MAD去极值处理器 - 基于每日截面的中位数绝对偏差去除极值
+
+    使用3倍MAD作为阈值，比标准差方法更稳健，对异常值不敏感。
+    阈值范围: [median - n*MAD, median + n*MAD]
+    """
+
+    stage = PipelineStage.TRAIN
+
+    def __init__(
+        self,
+        columns: Optional[List[str]] = None,
+        n_mad: float = 3.0,
+    ):
+        super().__init__(columns)
+        self.n_mad = n_mad
+
+    def fit(self, data: pl.DataFrame) -> "MADClipper":
+        cols = _get_numeric_columns(data, self.columns)
+        bounds = {}
+
+        for col in cols:
+            # 按日期分组计算每个截面的 median 和 MAD
+            daily_stats = data.group_by("trade_date").agg(
+                pl.col(col).median().alias("median"),
+                (pl.col(col) - pl.col(col).median()).abs().median().alias("mad"),
+            )
+            bounds[col] = daily_stats
+
+        self._fitted_params = {"bounds": bounds, "columns": cols}
+        self._is_fitted = True
+        return self
+
+    def transform(self, data: pl.DataFrame) -> pl.DataFrame:
+        """使用窗口函数进行MAD去极值，避免join操作提升性能"""
+        result = data
+        bounds = self._fitted_params.get("bounds", {})
+
+        for col in bounds.keys():
+            if col not in result.columns:
+                continue
+
+            # 使用窗口函数直接计算每个截面的median和MAD，避免join
+            # 1. 计算每个日期截面的median
+            median = pl.col(col).median().over("trade_date")
+            # 2. 计算每个日期截面的MAD
+            mad = (pl.col(col) - median).abs().median().over("trade_date")
+
+            # 3. 计算上下界并clip
+            lower = median - self.n_mad * mad
+            upper = median + self.n_mad * mad
+
+            result = result.with_columns(pl.col(col).clip(lower, upper).alias(col))
+
+        return result
+
+
 __all__ = [
     "DropNAProcessor",
     "FillNAProcessor",
@@ -235,4 +292,5 @@ __all__ = [
     "MinMaxScaler",
     "RankTransformer",
     "Neutralizer",
+    "MADClipper",
 ]