feat(training): 新增 TabM 排序学习模型支持并优化训练流程

- 新增 TabMRankModel、TabMRankTask 及配套损失函数与配置
- 将 DataQualityAnalyzer 从 experiment 迁移至 training 模块
- 调整数据处理器移除过度的 NaN/null 硬填充逻辑
- 优化 RankTask 评估指标使用分位数标签替代原始收益率
- 更新实验脚本处理器顺序与模型超参数配置

src/experiment/regression.py

@@ -52,36 +52,36 @@ TRAINING_TYPE = "regression"
 
 # 排除的因子列表
 EXCLUDED_FACTORS = [
-    'GTJA_alpha016',
-    'volatility_20',
-    'current_ratio',
-    'GTJA_alpha001',
-    'GTJA_alpha141',
-    'GTJA_alpha129',
-    'GTJA_alpha164',
-    'amivest_liq_20',
-    'GTJA_alpha012',
-    'debt_to_equity',
-    'turnover_deviation',
-    'GTJA_alpha073',
-    'GTJA_alpha043',
-    'GTJA_alpha032',
-    'GTJA_alpha028',
-    'GTJA_alpha090',
-    'GTJA_alpha108',
-    'GTJA_alpha105',
-    'GTJA_alpha091',
-    'GTJA_alpha119',
-    'GTJA_alpha104',
-    'GTJA_alpha163',
-    'GTJA_alpha157',
-    'cost_skewness',
-    'GTJA_alpha176',
-    'chip_transition',
-    'amount_skewness_20',
-    'GTJA_alpha148',
-    'mean_median_dev',
-    'downside_illiq_20',
+    # 'GTJA_alpha016',
+    # 'volatility_20',
+    # 'current_ratio',
+    # 'GTJA_alpha001',
+    # 'GTJA_alpha141',
+    # 'GTJA_alpha129',
+    # 'GTJA_alpha164',
+    # 'amivest_liq_20',
+    # 'GTJA_alpha012',
+    # 'debt_to_equity',
+    # 'turnover_deviation',
+    # 'GTJA_alpha073',
+    # 'GTJA_alpha043',
+    # 'GTJA_alpha032',
+    # 'GTJA_alpha028',
+    # 'GTJA_alpha090',
+    # 'GTJA_alpha108',
+    # 'GTJA_alpha105',
+    # 'GTJA_alpha091',
+    # 'GTJA_alpha119',
+    # 'GTJA_alpha104',
+    # 'GTJA_alpha163',
+    # 'GTJA_alpha157',
+    # 'cost_skewness',
+    # 'GTJA_alpha176',
+    # 'chip_transition',
+    # 'amount_skewness_20',
+    # 'GTJA_alpha148',
+    # 'mean_median_dev',
+    # 'downside_illiq_20',
 ]
 
 # 模型参数配置
@@ -153,15 +153,15 @@ def main():
     pipeline = DataPipeline(
         factor_manager=factor_manager,
         processor_configs=[
-            (NullFiller, {"strategy": "mean"}),
             (Winsorizer, {"lower": 0.01, "upper": 0.99}),
+            (NullFiller, {"strategy": "mean"}),
             (StandardScaler, {}),
             # (CrossSectionalStandardScaler, {}),
         ],
         label_processor_configs=[
             # 对 label 进行缩尾处理（去除极端收益率）
             (Winsorizer, {"lower": 0.05, "upper": 0.95}),
-            # (StandardScaler, {}),
+            (StandardScaler, {}),
         ],
         filters=[STFilter(data_router=engine.router)],
         stock_pool_filter_func=stock_pool_filter,