feat(training): TabM模型量化交易优化

- 新增 CrossSectionSampler 支持截面数据采样（按交易日批处理）
- 新增 EnsembleQuantLoss (Huber + IC) 替代 MSE 作为损失函数
- 重构 TabMModel 支持量化场景：Rank IC 作为验证指标、CosineAnnealingLR学习率调度、梯度裁剪
- 支持 date_col 参数和特征对齐
- 更新实验配置 batch_size 2048 和 weight_decay 等超参数

tests/training/test_cross_section_sampler.py

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+"""截面数据采样器单元测试"""
+
+import numpy as np
+import pytest
+import torch
+from torch.utils.data import TensorDataset
+
+from src.training.components.models.cross_section_sampler import CrossSectionSampler
+
+
+class TestCrossSectionSampler:
+    """截面采样器单元测试"""
+
+    def test_basic_functionality(self):
+        """测试基本功能：按日期分组"""
+        dates = np.array(["20240101", "20240101", "20240102", "20240102", "20240103"])
+        sampler = CrossSectionSampler(dates, shuffle_days=False)
+
+        # 应该有3个日期批次
+        assert len(sampler) == 3
+
+        # 获取所有批次
+        batches = list(sampler)
+
+        # 验证每个批次包含同一天的数据
+        for batch in batches:
+            batch_dates = [dates[i] for i in batch]
+            assert len(set(batch_dates)) == 1, "批次内日期不一致"
+
+    def test_shuffle_days(self):
+        """测试日期打乱功能"""
+        np.random.seed(42)
+        dates = np.array(["20240101"] * 5 + ["20240102"] * 5 + ["20240103"] * 5)
+
+        # 多次采样，验证日期顺序会变化
+        orders = []
+        for _ in range(10):
+            batches = list(CrossSectionSampler(dates, shuffle_days=True))
+            date_order = [dates[batch[0]] for batch in batches]
+            orders.append(tuple(date_order))
+
+        # 应该有不同的顺序出现
+        assert len(set(orders)) > 1, "日期顺序未被打乱"
+
+    def test_internal_shuffle(self):
+        """测试截面内股票顺序打乱"""
+        np.random.seed(42)
+        dates = np.array(["20240101"] * 10)
+
+        # 多次获取同一批次
+        indices_list = []
+        for _ in range(5):
+            sampler = CrossSectionSampler(dates, shuffle_days=False)
+            batch = next(iter(sampler))
+            indices_list.append(list(batch))
+
+        # 应该有不同顺序
+        assert len(set(tuple(x) for x in indices_list)) > 1, "截面内顺序未被打乱"
+
+    def test_with_dataloader(self):
+        """测试与 DataLoader 集成"""
+        dates = np.array(["20240101", "20240101", "20240102", "20240102"])
+        X = torch.randn(4, 5)
+        y = torch.randn(4)
+
+        dataset = TensorDataset(X, y)
+        sampler = CrossSectionSampler(dates, shuffle_days=False)
+        loader = torch.utils.data.DataLoader(dataset, batch_sampler=sampler)
+
+        batches = list(loader)
+        assert len(batches) == 2  # 2个日期
+
+        for bx, by in batches:
+            assert bx.shape[0] == 2  # 每个日期2个样本
+            assert by.shape[0] == 2
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])