feat(models): 实现机器学习模型训练框架

- 添加核心抽象：Processor、Model、Splitter、Metric 基类
- 实现阶段感知机制（TRAIN/TEST/ALL），防止数据泄露
- 内置 8 个数据处理器和 3 种时序划分策略
- 支持 LightGBM、CatBoost 模型
- PluginRegistry 装饰器注册，插件式架构
- 22 个单元测试

src/models/__init__.py

@@ -0,0 +1,86 @@
+"""ProStock 模型训练框架
+
+组件化、低耦合、插件式的机器学习训练框架。
+
+示例:
+    >>> from src.models import (
+    ...     PluginRegistry, ProcessingPipeline,
+    ...     PipelineStage, BaseProcessor
+    ... )
+
+    >>> # 获取注册的处理器
+    >>> scaler_class = PluginRegistry.get_processor("standard_scaler")
+    >>> scaler = scaler_class()
+
+    >>> # 创建处理流水线
+    >>> pipeline = ProcessingPipeline([
+    ...     PluginRegistry.get_processor("dropna")(),
+    ...     PluginRegistry.get_processor("winsorizer")(lower=0.01, upper=0.99),
+    ...     PluginRegistry.get_processor("standard_scaler")(),
+    ... ])
+"""
+
+# 导入核心抽象类和划分策略
+from src.models.core import (
+    PipelineStage,
+    TaskType,
+    BaseProcessor,
+    BaseModel,
+    BaseSplitter,
+    BaseMetric,
+    TimeSeriesSplit,
+    WalkForwardSplit,
+    ExpandingWindowSplit,
+)
+
+# 导入注册中心
+from src.models.registry import PluginRegistry
+
+# 导入处理流水线
+from src.models.pipeline import ProcessingPipeline
+
+# 导入并注册内置处理器
+from src.models.processors.processors import (
+    DropNAProcessor,
+    FillNAProcessor,
+    Winsorizer,
+    StandardScaler,
+    MinMaxScaler,
+    RankTransformer,
+    Neutralizer,
+)
+
+# 导入并注册内置模型
+from src.models.models.models import (
+    LightGBMModel,
+    CatBoostModel,
+)
+
+__all__ = [
+    # 核心抽象
+    "PipelineStage",
+    "TaskType",
+    "BaseProcessor",
+    "BaseModel",
+    "BaseSplitter",
+    "BaseMetric",
+    # 划分策略
+    "TimeSeriesSplit",
+    "WalkForwardSplit",
+    "ExpandingWindowSplit",
+    # 注册中心
+    "PluginRegistry",
+    # 处理流水线
+    "ProcessingPipeline",
+    # 处理器
+    "DropNAProcessor",
+    "FillNAProcessor",
+    "Winsorizer",
+    "StandardScaler",
+    "MinMaxScaler",
+    "RankTransformer",
+    "Neutralizer",
+    # 模型
+    "LightGBMModel",
+    "CatBoostModel",
+]