feat(training): 实现训练模块核心组件（commits 6-9）

- StockPoolManager：每日独立筛选股票池，支持代码过滤和市值选择
- Trainer：整合训练完整流程，支持 processor 分阶段行为和模型持久化
- TrainingConfig：pydantic 配置管理，含必填字段和日期验证
- experiment 模块：预留结构
- 从计划中移除 metrics 组件
- 调整 commit 序号（7-10 → 6-9）
- 更新 training/__init__.py 导出所有公开 API

docs/plan/training_module_plan.md

@@ -40,9 +40,6 @@ src/
 │   │   ├── processors/         # 数据处理器
 │   │   │   ├── __init__.py
 │   │   │   └── transforms.py   # 标准化（截面/时序）、缩尾
-│   │   └── metrics/            # 评估指标
-│   │       ├── __init__.py
-│   │       └── metrics.py      # IC, RankIC, MSE, MAE
 │   ├── config/                 # 配置管理
 │   │   ├── __init__.py
 │   │   └── config.py           # TrainingConfig (pydantic)
@@ -568,30 +565,6 @@ class Winsorizer(BaseProcessor):
         pass
 ```
 
-### 3.7 评估指标 (components/metrics/)
-
-```python
-def ic_score(y_true: np.ndarray, y_pred: np.ndarray) -> float:
-    """信息系数 (Pearson 相关系数)"""
-    from scipy.stats import pearsonr
-    corr, _ = pearsonr(y_true, y_pred)
-    return corr
-
-def rank_ic_score(y_true: np.ndarray, y_pred: np.ndarray) -> float:
-    """秩相关系数 (Spearman 相关系数)"""
-    from scipy.stats import spearmanr
-    corr, _ = spearmanr(y_true, y_pred)
-    return corr
-
-def mse_score(y_true: np.ndarray, y_pred: np.ndarray) -> float:
-    """均方误差"""
-    return np.mean((y_true - y_pred) ** 2)
-
-def mae_score(y_true: np.ndarray, y_pred: np.ndarray) -> float:
-    """平均绝对误差"""
-    return np.mean(np.abs(y_true - y_pred))
-```
-
 ## 4. 训练流程设计
 
 ### 4.1 Trainer 主类 (core/trainer.py)
@@ -615,7 +588,6 @@ class Trainer:
         pool_manager: Optional[StockPoolManager] = None,
         processors: List[BaseProcessor] = None,
         splitter: DateSplitter = None,
-        metrics: List[str] = None,
         target_col: str = "target",
         feature_cols: List[str] = None,
         persist_model: bool = False,
@@ -625,7 +597,6 @@ class Trainer:
         self.pool_manager = pool_manager
         self.processors = processors or []
         self.splitter = splitter
-        self.metrics = metrics or ["ic", "rank_ic", "mse", "mae"]
         self.target_col = target_col
         self.feature_cols = feature_cols
         self.persist_model = persist_model
@@ -634,7 +605,6 @@ class Trainer:
         # 存储训练后的处理器
         self.fitted_processors: List[BaseProcessor] = []
         self.results: pl.DataFrame = None
-        self.metrics_results: Dict[str, float] = {}
     
     def train(self, data: pl.DataFrame) -> "Trainer":
         """执行训练流程
@@ -686,38 +656,18 @@ class Trainer:
         X_test = test_data.select(self.feature_cols)
         predictions = self.model.predict(X_test)
         
-        # 7. 评估
-        print("[评估] 计算指标...")
-        y_test = test_data.select(self.target_col).to_series().to_numpy()
-        self._evaluate(y_test, predictions)
-        
-        # 8. 保存结果
+        # 7. 保存结果
         self.results = test_data.with_columns([
             pl.Series("prediction", predictions)
         ])
         
-        # 9. 持久化模型
+        # 8. 持久化模型
         if self.persist_model and self.model_save_path:
             print(f"[保存] 保存模型到 {self.model_save_path}...")
             self.save_model(self.model_save_path)
         
         return self
     
-    def _evaluate(self, y_true: np.ndarray, y_pred: np.ndarray):
-        """计算评估指标"""
-        from .components.metrics import ic_score, rank_ic_score, mse_score, mae_score
-        
-        metric_funcs = {
-            "ic": ic_score,
-            "rank_ic": rank_ic_score,
-            "mse": mse_score,
-            "mae": mae_score,
-        }
-        
-        for metric in self.metrics:
-            if metric in metric_funcs:
-                self.metrics_results[metric] = metric_funcs[metric](y_true, y_pred)
-    
     def predict(self, data: pl.DataFrame) -> pl.DataFrame:
         """对新数据进行预测
         
@@ -738,10 +688,6 @@ class Trainer:
         """获取所有预测结果"""
         return self.results
     
-    def get_metrics(self) -> Dict[str, float]:
-        """获取评估指标"""
-        return self.metrics_results
-    
     def save_results(self, path: str):
         """保存预测结果到文件"""
         if self.results is not None:
@@ -827,9 +773,6 @@ class TrainingConfig(BaseSettings):
         ]
     )
     
-    # === 评估指标 ===
-    metrics: List[str] = ["ic", "rank_ic", "mse", "mae"]
-    
     # === 持久化配置 ===
     persist_model: bool = False           # 默认不持久化
     model_save_path: Optional[str] = None  # 持久化路径
@@ -914,8 +857,6 @@ trainer.train(all_data)
 
 # 8. 获取结果
 results = trainer.get_results()  # 包含预测值
-metrics = trainer.get_metrics()   # IC, RankIC, etc.
-print("评估指标:", metrics)
 
 # 9. 保存结果
 trainer.save_results("output/predictions.csv")
@@ -989,7 +930,6 @@ trainer = Trainer(
 
 trainer.train(all_data)
 results = trainer.get_results()
-metrics = trainer.get_metrics()
 ```
 
 ## 6. 实现顺序
@@ -1019,22 +959,18 @@ metrics = trainer.get_metrics()
 - `training/components/models/__init__.py`
 - `training/components/models/lightgbm.py`（含 save_model/load_model）
 
-### Commit 6: 评估指标
-- `training/components/metrics/__init__.py`
-- `training/components/metrics/metrics.py`（IC, RankIC, MSE, MAE）
-
-### Commit 7: 股票池管理器
+### Commit 6: 股票池管理器
 - `training/core/__init__.py`
 - `training/core/stock_pool_manager.py`（每日独立筛选）
 
-### Commit 8: Trainer 训练器
+### Commit 7: Trainer 训练器
 - `training/core/trainer.py`
 
-### Commit 9: 配置管理
+### Commit 8: 配置管理
 - `training/config/__init__.py`
 - `training/config/config.py`（TrainingConfig，含必填校验）
 
-### Commit 10: 预留实验模块
+### Commit 9: 预留实验模块
 - `experiment/__init__.py`
 
 ## 7. 注意事项
@@ -1080,7 +1016,7 @@ metrics = trainer.get_metrics()
 1. **特征选择**（processors/selectors.py）
 2. **滚动训练**（WalkForward, ExpandingWindow）
 3. **结果分析工具**（复杂分析功能）
-4. **validator.py, evaluator.py**（简化为 metrics.py）
+4. **validator.py, evaluator.py**（已删除，不实现 metrics）
 
 ### 7.5 新增功能