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_ensemble_quant_loss.py

@@ -0,0 +1,98 @@
+"""EnsembleQuantLoss 单元测试"""
+
+import numpy as np
+import pytest
+import torch
+import torch.nn as nn
+
+from src.training.components.models.ensemble_quant_loss import EnsembleQuantLoss
+
+
+class TestEnsembleQuantLoss:
+    """EnsembleQuantLoss 单元测试"""
+
+    def test_initialization(self):
+        """测试初始化"""
+        loss_fn = EnsembleQuantLoss(alpha=0.7, ensemble_size=16)
+
+        assert loss_fn.alpha == 0.7
+        assert loss_fn.ensemble_size == 16
+        assert isinstance(loss_fn.huber, nn.HuberLoss)
+
+    def test_output_shape(self):
+        """测试输出形状和类型"""
+        loss_fn = EnsembleQuantLoss(alpha=0.5, ensemble_size=4)
+
+        # 创建模拟数据: 20只股票, 4个集成成员
+        preds = torch.randn(20, 4)
+        target = torch.randn(20)
+
+        loss = loss_fn(preds, target)
+
+        # 验证输出是标量
+        assert loss.shape == torch.Size([])
+        assert isinstance(loss.item(), float)
+
+    def test_small_batch_fallback(self):
+        """测试小批次回退到 Huber"""
+        loss_fn = EnsembleQuantLoss(alpha=0.5, ensemble_size=4)
+
+        # 少于10只股票的批次
+        preds = torch.randn(5, 4)
+        target = torch.randn(5)
+
+        loss = loss_fn(preds, target)
+
+        # 应该正常返回loss
+        assert not torch.isnan(loss)
+        assert loss.item() > 0
+
+    def test_huber_component(self):
+        """测试 Huber 损失组件"""
+        loss_fn = EnsembleQuantLoss(alpha=1.0, ensemble_size=4)  # 纯 Huber
+
+        preds = torch.randn(50, 4)
+        target = torch.randn(50)
+
+        loss = loss_fn(preds, target)
+
+        # 手动计算期望的 Huber 损失
+        huber = nn.HuberLoss(reduction="mean")
+        expected_loss = 0
+        for i in range(4):
+            expected_loss += huber(preds[:, i], target)
+        expected_loss /= 4
+
+        assert torch.allclose(loss, expected_loss, rtol=1e-5)
+
+    def test_ic_component(self):
+        """测试 IC 损失组件"""
+        loss_fn = EnsembleQuantLoss(alpha=0.0, ensemble_size=1)  # 纯 IC
+
+        # 创建完全相关的预测和目标
+        target = torch.randn(50)
+        preds = target.unsqueeze(1)  # 完美相关
+
+        loss = loss_fn(preds, target)
+
+        # 完美相关时 IC=1，所以 IC loss = 0
+        # 但由于 std 计算和数值精度，可能不完全为0
+        assert loss.item() < 0.1
+
+    def test_gradient_flow(self):
+        """测试梯度可以正常回传"""
+        loss_fn = EnsembleQuantLoss(alpha=0.5, ensemble_size=4)
+
+        preds = torch.randn(50, 4, requires_grad=True)
+        target = torch.randn(50)
+
+        loss = loss_fn(preds, target)
+        loss.backward()
+
+        # 验证梯度存在且非零
+        assert preds.grad is not None
+        assert not torch.all(preds.grad == 0)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])