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

src/experiment/learn_to_rank.py

@@ -41,7 +41,6 @@ from src.training.config import TrainingConfig
 from src.experiment.common import (
     SELECTED_FACTORS,
     FACTOR_DEFINITIONS,
-    EXCLUDED_FACTORS,
     get_label_factor,
     register_factors,
     prepare_data,
@@ -223,26 +222,53 @@ N_QUANTILES = 20  # 将 label 分为 20 组
 N_QUANTILES = 20  # 将 label 分为 20 组
 
 # LambdaRank 模型参数配置
+# MODEL_PARAMS = {
+#     "objective": "lambdarank",
+#     "metric": "ndcg",
+#     "ndcg_at": 15,
+#     "learning_rate": 0.001,
+#     "num_leaves": 32,
+#     "max_depth": 5,
+#     "min_data_in_leaf": 32,
+#     "n_estimators": 1000,
+#     "early_stopping_round": 150,
+#     "subsample": 0.6,
+#     "colsample_bytree": 0.6,
+#     "reg_alpha": 1,
+#     "reg_lambda": 3.0,
+#     "verbose": -1,
+#     "random_state": 42,
+#     "lambdarank_truncation_level": 30,
+#     "label_gain": [
+#         i for i in range(1, N_QUANTILES + 1)
+#     ],  # 如果收益率被分为了比如 5 档，建议用[0, 1, 3, 7, 15] 这种指数型 gain
+# }
+
 MODEL_PARAMS = {
     "objective": "lambdarank",
     "metric": "ndcg",
-    "ndcg_at": 15,
-    "learning_rate": 0.002,
-    "num_leaves": 63,
-    "max_depth": 5,
-    "min_data_in_leaf": 63,
+    "ndcg_at": 25,  # 根据你实际持仓数量调整，如果是前50只股票，改成50
+    "learning_rate": 0.1,  # 【修改】提高学习率，配合合理的早停
     "n_estimators": 1000,
-    "early_stopping_round": 150,
-    "subsample": 0.8,
-    "colsample_bytree": 0.8,
-    "reg_alpha": 0.5,
-    "reg_lambda": 1.0,
+    "early_stopping_round": 50,  # 【修改】验证集一旦不降，50轮内尽早停下
+    # --- 1. 防止过拟合的核心约束 ---
+    "max_depth": 4,  # 【修改】金融数据不需要太深，3~4 足够了
+    "num_leaves": 32,  # 【修改】大幅减少叶子数，避免过拟合 (2^4 = 16，取12限制生长)
+    "min_data_in_leaf": 256,  # 【修改】极度重要！强制每个叶子必须代表一个大的股票群体（如500只）
+    # --- 2. 随机采样（增加鲁棒性） ---
+    "subsample": 0.4,  # 每棵树使用 70% 的样本
+    "subsample_freq": 1,  # 每 1 轮进行一次 subsample
+    "colsample_bytree": 0.4,  # 【修改】降低特征采样率，迫使模型不要只依赖那几个头部 Alpha 因子，增加树的多样性
+    # --- 3. 正则化惩罚 ---
+    "reg_alpha": 10.0,  # 【修改】增加 L1 正则化，帮助剔除无效的 GTJA 噪音因子
+    "reg_lambda": 50.0,  # 【修改】增加 L2 正则化
+    # --- 4. Lambdarank 专属配置 ---
+    "lambdarank_truncation_level": 50,
+    "label_gain": [
+        i * i for i in range(1, N_QUANTILES + 1)
+    ],  # 如果收益率被分为了比如 5 档，建议用[0, 1, 3, 7, 15] 这种指数型 gain
     "verbose": -1,
     "random_state": 42,
-    "lambdarank_truncation_level": 30,
-    "label_gain": [
-        i for i in range(1, N_QUANTILES + 1)
-    ],  # 如果收益率被分为了比如 5 档，建议用[0, 1, 3, 7, 15] 这种指数型 gain
 }
 
 # 注意：stock_pool_filter, STOCK_FILTER_REQUIRED_COLUMNS, OUTPUT_DIR 等配置
@@ -258,6 +284,163 @@ print("=" * 80)
 print("\n[1] 创建 FactorEngine")
 engine = FactorEngine()
 
+EXCLUDED_FACTORS = ['volatility_5',
+'volume_ratio_5_20',
+'capital_retention_20',
+'volatility_squeeze_5_60',
+'drawdown_from_high_60',
+'ma_ratio_5_20',
+'bias_10',
+'high_low_ratio',
+'bbi_ratio',
+'volatility_20',
+'std_return_20',
+'sharpe_ratio_20',
+'ma_5',
+'max_ret_20',
+'CP',
+'net_profit_yoy',
+'debt_to_equity',
+'EP_rank',
+'turnover_rank',
+'return_5_rank',
+'ebit_rank',
+'BP',
+'EP',
+'amihud_illiq_20',
+'profit_margin',
+'return_5',
+'return_20',
+'kaufman_ER_20',
+'GTJA_alpha043',
+'GTJA_alpha042',
+'GTJA_alpha041',
+'GTJA_alpha040',
+'GTJA_alpha039',
+'GTJA_alpha037',
+'GTJA_alpha036',
+'GTJA_alpha035',
+'GTJA_alpha033',
+'GTJA_alpha032',
+'GTJA_alpha031',
+'GTJA_alpha028',
+'GTJA_alpha026',
+'GTJA_alpha027',
+'GTJA_alpha023',
+'GTJA_alpha024',
+'GTJA_alpha009',
+'GTJA_alpha011',
+'GTJA_alpha022',
+'GTJA_alpha020',
+'GTJA_alpha018',
+'GTJA_alpha019',
+'GTJA_alpha014',
+'GTJA_alpha013',
+'GTJA_alpha010',
+'GTJA_alpha001',
+'GTJA_alpha003',
+'GTJA_alpha002',
+'GTJA_alpha004',
+'GTJA_alpha005',
+'GTJA_alpha006',
+'GTJA_alpha008',
+'turnover_deviation',
+'turnover_cv_20',
+'roa',
+'GTJA_alpha073',
+'GTJA_alpha078',
+'GTJA_alpha077',
+'GTJA_alpha076',
+'GTJA_alpha067',
+'GTJA_alpha085',
+'GTJA_alpha084',
+'GTJA_alpha087',
+'GTJA_alpha088',
+'GTJA_alpha090',
+'GTJA_alpha083',
+'GTJA_alpha079',
+'GTJA_alpha080',
+'GTJA_alpha094',
+'GTJA_alpha092',
+'GTJA_alpha089',
+'GTJA_alpha095',
+'GTJA_alpha064',
+'GTJA_alpha065',
+'GTJA_alpha066',
+'GTJA_alpha063',
+'GTJA_alpha060',
+'GTJA_alpha058',
+'GTJA_alpha057',
+'GTJA_alpha056',
+'GTJA_alpha046',
+'GTJA_alpha044',
+'GTJA_alpha049',
+'GTJA_alpha050',
+'GTJA_alpha110',
+'GTJA_alpha107',
+'GTJA_alpha104',
+'GTJA_alpha106',
+'GTJA_alpha103',
+'GTJA_alpha100',
+'GTJA_alpha101',
+'GTJA_alpha102',
+'GTJA_alpha098',
+'GTJA_alpha097',
+'GTJA_alpha096',
+'GTJA_alpha099',
+'GTJA_alpha117',
+'GTJA_alpha118',
+'GTJA_alpha114',
+'GTJA_alpha111',
+'GTJA_alpha129',
+'GTJA_alpha130',
+'GTJA_alpha132',
+'GTJA_alpha131',
+'GTJA_alpha134',
+'GTJA_alpha135',
+'GTJA_alpha136',
+'GTJA_alpha112',
+'GTJA_alpha120',
+'GTJA_alpha119',
+'GTJA_alpha122',
+'GTJA_alpha124',
+'GTJA_alpha126',
+'GTJA_alpha127',
+'GTJA_alpha128',
+'GTJA_alpha115',
+'GTJA_alpha153',
+'GTJA_alpha152',
+'GTJA_alpha151',
+'GTJA_alpha150',
+'GTJA_alpha148',
+'GTJA_alpha142',
+'GTJA_alpha141',
+'GTJA_alpha139',
+'GTJA_alpha133',
+'GTJA_alpha161',
+'GTJA_alpha164',
+'GTJA_alpha162',
+'GTJA_alpha157',
+'GTJA_alpha156',
+'GTJA_alpha160',
+'GTJA_alpha155',
+'GTJA_alpha170',
+'GTJA_alpha169',
+'GTJA_alpha168',
+'GTJA_alpha166',
+'GTJA_alpha163',
+'GTJA_alpha176',
+'GTJA_alpha175',
+'GTJA_alpha174',
+'GTJA_alpha178',
+'GTJA_alpha177',
+'GTJA_alpha185',
+'GTJA_alpha180',
+'GTJA_alpha187',
+'GTJA_alpha188',
+'GTJA_alpha189',
+'GTJA_alpha191',]
+
 # 2. 使用 metadata 定义因子
 print("\n[2] 定义因子（从 metadata 注册）")
 feature_cols = register_factors(
@@ -538,14 +721,64 @@ print("-" * 40)
 for metric, value in ndcg_results.items():
     print(f"  {metric}: {value:.4f}")
 
-# 特征重要性
-print("\n特征重要性（Top 20）:")
-print("-" * 40)
-importance = model.feature_importance()
-if importance is not None:
-    top_features = importance.sort_values(ascending=False).head(20)
-    for i, (feature, score) in enumerate(top_features.items(), 1):
-        print(f"  {i:2d}. {feature:30s} {score:10.2f}")
+    # 特征重要性
+    print("\n特征重要性分析:")
+    print("=" * 80)
+    importance = model.feature_importance()
+    if importance is not None:
+        # 按重要性降序排列
+        importance_sorted = importance.sort_values(ascending=False)
+
+        # 计算总重要性和百分比
+        total_importance = importance_sorted.sum()
+        importance_pct = (importance_sorted / total_importance * 100).round(2)
+
+        # 找出贡献为0的特征
+        zero_importance_features = importance_sorted[
+            importance_sorted == 0
+        ].index.tolist()
+
+        # 打印所有特征重要性（带百分比）
+        print(f"\n所有特征重要性（共 {len(importance_sorted)} 个）:")
+        print("-" * 80)
+        print(f"{'排名':<6}{'特征名':<35}{'重要性':<15}{'占比':<10}")
+        print("-" * 80)
+        for i, (feature, score) in enumerate(importance_sorted.items(), 1):
+            pct = importance_pct[feature]
+            if score == 0:
+                marker = " [零贡献]"
+            elif pct >= 10:
+                marker = " [高贡献]"
+            elif pct >= 1:
+                marker = " [中贡献]"
+            else:
+                marker = " [低贡献]"
+            print(f"{i:<6}{feature:<35}{score:<15.2f}{pct:<8.2f}%{marker}")
+
+        # 打印贡献为0的特征
+        print("\n" + "=" * 80)
+        if zero_importance_features:
+            print(f"[警告] 贡献为0的特征（共 {len(zero_importance_features)} 个）:")
+            print("-" * 80)
+            for i, feature in enumerate(zero_importance_features, 1):
+                print(f"'{feature}',")
+        else:
+            print("[信息] 所有特征都有贡献（无零贡献特征）")
+
+        # 打印统计摘要
+        print("\n" + "=" * 80)
+        print("特征重要性统计摘要:")
+        print("-" * 80)
+        print(f"  特征总数: {len(importance_sorted)}")
+        print(
+            f"  有贡献特征数: {len(importance_sorted) - len(zero_importance_features)}"
+        )
+        print(f"  零贡献特征数: {len(zero_importance_features)}")
+        print(
+            f"  零贡献占比: {len(zero_importance_features) / len(importance_sorted) * 100:.1f}%"
+        )
+        print(f"  Top 10特征累计占比: {importance_pct.head(10).sum():.1f}%")
+        print(f"  Top 20特征累计占比: {importance_pct.head(20).sum():.1f}%")
 # %%
 # 确保输出目录存在
 os.makedirs(OUTPUT_DIR, exist_ok=True)