feat(factorminer): 新增 LocalFactorEvaluator 集成到评估管线

- 新增 LocalFactorEvaluator 类封装 FactorEngine，提供 (M,T) 矩阵输出
- evaluate_factors_with_evaluator() 支持新评估方式
- ValidationPipeline 优先使用 evaluator 计算信号
- 新增测试文件验证功能

tests/test_factorminer_pipeline_integration.py

@@ -0,0 +1,130 @@
+"""Tests for Factorminer pipeline integration with LocalFactorEvaluator."""
+
+from __future__ import annotations
+
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+import pytest
+
+from src.factorminer.core.factor_library import FactorLibrary
+from src.factorminer.core.library_io import import_from_paper
+from src.factorminer.evaluation.local_engine import LocalFactorEvaluator
+from src.factorminer.evaluation.pipeline import (
+    PipelineConfig,
+    ValidationPipeline,
+    run_evaluation_pipeline,
+)
+from src.factorminer.evaluation.runtime import (
+    evaluate_factors_with_evaluator,
+)
+
+
+class TestLocalFactorEvaluatorIntegration:
+    """测试 LocalFactorEvaluator 与评估管线的集成。"""
+
+    @pytest.fixture
+    def evaluator(self) -> LocalFactorEvaluator:
+        """创建评估器 fixture。"""
+        return LocalFactorEvaluator(
+            start_date="20200101",
+            end_date="20200131",
+            stock_codes=None,
+        )
+
+    @pytest.fixture
+    def returns_matrix(self) -> np.ndarray:
+        """创建模拟收益率矩阵 fixture。"""
+        M, T = 100, 20
+        rng = np.random.default_rng(42)
+        return rng.standard_normal((M, T))
+
+    @pytest.fixture
+    def splits(self) -> Dict[str, object]:
+        """创建模拟分割 fixture。"""
+
+        class MockSplit:
+            def __init__(self, indices: np.ndarray, returns: np.ndarray):
+                self.indices = indices
+                self.returns = returns
+                self.target_returns = {}
+
+        T = 20
+        indices = np.arange(T)
+        rng = np.random.default_rng(42)
+        returns = rng.standard_normal((100, T))
+
+        return {
+            "train": MockSplit(indices[:15], returns[:, :15]),
+            "val": MockSplit(indices[15:], returns[:, 15:]),
+        }
+
+    def test_evaluate_factors_with_evaluator_deprecated_path(
+        self,
+        evaluator: LocalFactorEvaluator,
+        returns_matrix: np.ndarray,
+        splits: Dict[str, object],
+    ) -> None:
+        """测试 evaluate_factors_with_evaluator 在有 evaluator 时的行为。"""
+
+        # 模拟一个因子对象
+        class MockFactor:
+            def __init__(self, id: str, name: str, formula: str, category: str):
+                self.id = id
+                self.name = name
+                self.formula = formula
+                self.category = category
+
+        factors = [
+            MockFactor("f1", "close", "close", "price"),
+            MockFactor("f2", "# TODO: unsupported", "unsupported", "test"),
+        ]
+
+        try:
+            artifacts = evaluate_factors_with_evaluator(
+                factors=factors,
+                evaluator=evaluator,
+                returns=returns_matrix,
+                splits=splits,
+            )
+            # 验证返回结果结构
+            assert len(artifacts) == 2
+            assert artifacts[0].name == "close"
+            assert artifacts[1].name == "# TODO: unsupported"
+            # unsupported 因子应该被标记
+            assert artifacts[1].error == "Unsupported operator in formula"
+        except Exception as e:
+            # FactorEngine 可能因为数据不存在而失败
+            pytest.skip(f"FactorEngine 数据不存在: {e}")
+
+    def test_evaluate_factors_fallback_legacy(
+        self,
+        returns_matrix: np.ndarray,
+        splits: Dict[str, object],
+    ) -> None:
+        """测试 evaluator=None 时回退到 legacy 方式。"""
+
+        class MockFactor:
+            def __init__(self, id: str, name: str, formula: str, category: str):
+                self.id = id
+                self.name = name
+                self.formula = formula
+                self.category = category
+
+        factors = [
+            MockFactor("f1", "test", "close", "price"),
+        ]
+
+        # evaluator=None 应该回退到 legacy
+        artifacts = evaluate_factors_with_evaluator(
+            factors=factors,
+            evaluator=None,
+            returns=returns_matrix,
+            splits=splits,
+        )
+        # Legacy 方式会尝试 compute_tree_signals 但 data_dict 为空
+        assert len(artifacts) == 1
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])