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

@@ -0,0 +1,79 @@
+"""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"])

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"])