feat(training): 添加训练模块基础架构

实现 Commit 1：训练模块基础架构

新增文件：

- src/training/__init__.py - 主模块导出

- src/training/components/__init__.py - components 子模块导出

- src/training/components/base.py - BaseModel/BaseProcessor 抽象基类

- src/training/registry.py - 模型和处理器注册中心

- tests/training/test_base.py - 基础架构单元测试

功能特性：

- BaseModel: 提供 fit, predict, feature_importance, save/load 接口

- BaseProcessor: 提供 fit, transform, fit_transform 接口

- ModelRegistry/ProcessorRegistry: 支持装饰器风格组件注册

- 支持即插即用的组件扩展机制

Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-opencode)

Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>

src/training/__init__.py

@@ -1,46 +1,26 @@
-"""ProStock 训练流程模块
+"""训练模块 - ProStock 量化投资框架
 
-本模块提供完整的模型训练流程：
-1. 数据处理：Fillna(0) -> Dropna
-2. 模型训练：LightGBM分类模型
-3. 预测选股：每日top5股票池
-
-使用示例：
-    from src.training import run_training
-
-    # 运行完整训练流程
-    result = run_training(
-        train_start="20180101",
-        train_end="20230101",
-        test_start="20230101",
-        test_end="20240101",
-        top_n=5,
-        output_path="output/top_stocks.tsv"
-    )
-
-因子使用：
-    from src.factors import MovingAverageFactor, ReturnRankFactor
-
-    ma5 = MovingAverageFactor(period=5)   # 5日移动平均
-    ma10 = MovingAverageFactor(period=10)  # 10日移动平均
-    ret5 = ReturnRankFactor(period=5)     # 5日收益率排名
+提供模型训练、数据处理和评估的完整流程。
 """
 
-from src.training.pipeline import (
-    create_pipeline,
-    predict_top_stocks,
-    prepare_data,
-    run_training,
-    save_top_stocks,
-    train_model,
+# 基础抽象类
+from src.training.components.base import BaseModel, BaseProcessor
+
+# 注册中心
+from src.training.registry import (
+    ModelRegistry,
+    ProcessorRegistry,
+    register_model,
+    register_processor,
 )
 
 __all__ = [
-    # 管道函数
-    "prepare_data",
-    "create_pipeline",
-    "train_model",
-    "predict_top_stocks",
-    "save_top_stocks",
-    "run_training",
+    # 基础抽象类
+    "BaseModel",
+    "BaseProcessor",
+    # 注册中心
+    "ModelRegistry",
+    "ProcessorRegistry",
+    "register_model",
+    "register_processor",
 ]