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/factors/financial/eps_factor.py

@@ -0,0 +1,66 @@
+"""EPS因子
+
+每股收益(EPS)排名因子实现
+"""
+
+from typing import List
+import polars as pl
+
+from src.factors.base import CrossSectionalFactor
+from src.factors.data_spec import DataSpec, FactorData
+
+
+class EPSFactor(CrossSectionalFactor):
+    """每股收益(EPS)排名因子
+
+    计算逻辑：使用最新报告期的basic_eps，每天对所有股票进行截面排名
+
+    Attributes:
+        name: 因子名称 "eps_rank"
+        category: 因子分类 "financial"
+        data_specs: 数据需求规格
+
+    Example:
+        >>> from src.factors import FactorEngine, DataLoader
+        >>> from src.factors.financial.eps_factor import EPSFactor
+        >>> loader = DataLoader('data')
+        >>> engine = FactorEngine(loader)
+        >>> eps_factor = EPSFactor()
+        >>> result = engine.compute(eps_factor, start_date='20210101', end_date='20210131')
+    """
+
+    name: str = "eps_rank"
+    category: str = "financial"
+    description: str = "每股收益截面排名因子"
+    data_specs: List[DataSpec] = [
+        DataSpec(
+            "financial_income", ["ts_code", "trade_date", "basic_eps"], lookback_days=1
+        )
+    ]
+
+    def compute(self, data: FactorData) -> pl.Series:
+        """计算EPS排名
+
+        Args:
+            data: FactorData，包含当前日期的截面数据
+
+        Returns:
+            EPS排名的0-1标准化值（0-1之间）
+        """
+        # 获取当前日期的截面数据
+        cs = data.get_cross_section()
+
+        if len(cs) == 0:
+            return pl.Series(name=self.name, values=[])
+
+        # 提取EPS值，填充缺失值为0
+        eps = cs["basic_eps"].fill_null(0)
+
+        # 计算排名并归一化到0-1
+        if len(eps) > 1 and eps.max() != eps.min():
+            ranks = eps.rank(method="average") / len(eps)
+        else:
+            # 数据不足或全部相同，返回0.5
+            ranks = pl.Series(name=self.name, values=[0.5] * len(eps))
+
+        return ranks