refactor(factorminer): 禁用 npz 信号缓存并将库 I/O 对接本地 DSL

- 为 Factor 数据类新增 metadata 字段，用于标记未实现算子（unsupported）
- save_library 废弃 save_signals 参数，内部强制忽略，仅持久化 JSON 元数据，不再写入 .npz
- load_library 删除 .npz 恢复逻辑；加载时自动将 # TODO 公式的 unsupported 标记设为 True
- import_from_paper() 直接基于已本地化的 PAPER_FACTORS 构建库，并同步标记 TODO 公式
- 新增 tests/test_factorminer_library_io.py，覆盖序列化、加载及 paper factors 导入

tests/test_factorminer_library_io.py

@@ -0,0 +1,157 @@
+"""Tests for library I/O and paper factor imports."""
+
+import json
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from src.factorminer.core.factor_library import Factor, FactorLibrary
+from src.factorminer.core.library_io import (
+    import_from_paper,
+    load_library,
+    save_library,
+)
+
+
+class TestSaveLoadLibrary:
+    """测试 FactorLibrary 的序列化与反序列化."""
+
+    def test_save_library_ignores_save_signals(self, tmp_path: Path) -> None:
+        """save_signals=True 也不应生成 .npz 文件."""
+        library = FactorLibrary()
+        factor = Factor(
+            id=0,
+            name="test_factor",
+            formula="close / ts_delay(close, 1) - 1",
+            category="Momentum",
+            ic_mean=0.05,
+            icir=0.5,
+            ic_win_rate=0.55,
+            max_correlation=0.1,
+            batch_number=1,
+        )
+        # 即使给一个信号矩阵，也不应保存
+        factor.signals = np.ones((10, 20))
+        library.admit_factor(factor)
+
+        base_path = tmp_path / "test_lib"
+        save_library(library, str(base_path), save_signals=True)
+
+        assert (base_path.with_suffix(".json")).exists()
+        assert not (Path(str(base_path) + "_signals.npz")).exists()
+
+    def test_load_library_restores_metadata_and_unsupported(
+        self, tmp_path: Path
+    ) -> None:
+        """加载 JSON 后应恢复 metadata，并对 # TODO 公式标记 unsupported."""
+        library = FactorLibrary()
+        f1 = Factor(
+            id=0,
+            name="ok_factor",
+            formula="cs_rank(close)",
+            category="Test",
+            ic_mean=0.0,
+            icir=0.0,
+            ic_win_rate=0.0,
+            max_correlation=0.0,
+            batch_number=0,
+            metadata={"author": "ai"},
+        )
+        f2 = Factor(
+            id=0,
+            name="todo_factor",
+            formula="# TODO: Neg(CsRank(Decay(close, 10)))",
+            category="Test",
+            ic_mean=0.0,
+            icir=0.0,
+            ic_win_rate=0.0,
+            max_correlation=0.0,
+            batch_number=0,
+        )
+        library.admit_factor(f1)
+        library.admit_factor(f2)
+
+        base_path = tmp_path / "meta_lib"
+        save_library(library, str(base_path))
+
+        loaded = load_library(str(base_path))
+        assert loaded.size == 2
+
+        f1_loaded = loaded.get_factor(1)
+        assert f1_loaded.metadata.get("author") == "ai"
+        assert not f1_loaded.metadata.get("unsupported", False)
+
+        f2_loaded = loaded.get_factor(2)
+        assert f2_loaded.metadata.get("unsupported") is True
+
+    def test_factor_round_trip_with_metadata(self) -> None:
+        """Factor.to_dict / from_dict 应正确传递 metadata."""
+        factor = Factor(
+            id=1,
+            name="round_trip",
+            formula="ts_mean(close, 20)",
+            category="Momentum",
+            ic_mean=0.1,
+            icir=1.0,
+            ic_win_rate=0.6,
+            max_correlation=0.2,
+            batch_number=2,
+            metadata={"unsupported": True, "tags": ["test"]},
+        )
+        d = factor.to_dict()
+        restored = Factor.from_dict(d)
+        assert restored.metadata == factor.metadata
+
+
+class TestImportFromPaper:
+    """测试从内置 paper catalog 导入因子."""
+
+    def test_import_from_paper_includes_all_translated_factors(self) -> None:
+        """内置 PAPER_FACTORS 应全部成功导入."""
+        library = import_from_paper()
+        assert library.size > 0
+        # 当前 catalog 中已有因子应全部被 admit
+        for factor in library.list_factors():
+            assert factor.id > 0
+            assert factor.name
+            assert factor.formula
+            assert factor.category
+
+    def test_import_from_paper_marks_todo_as_unsupported(self, tmp_path: Path) -> None:
+        """对 # TODO 公式应在 metadata 中标记 unsupported."""
+        custom_path = tmp_path / "custom_factors.json"
+        custom_data = [
+            {
+                "name": "Normal Factor",
+                "formula": "cs_rank(close)",
+                "category": "Test",
+            },
+            {
+                "name": "Unsupported Factor",
+                "formula": "# TODO: Neg(CsRank(Decay(close, 10)))",
+                "category": "Test",
+            },
+        ]
+        custom_path.write_text(json.dumps(custom_data), encoding="utf-8")
+
+        library = import_from_paper(str(custom_path))
+        assert library.size == 2
+
+        normal = library.list_factors()[0]
+        todo = library.list_factors()[1]
+
+        assert normal.metadata.get("unsupported") is None
+        assert todo.metadata.get("unsupported") is True
+
+    def test_import_from_paper_path_override(self, tmp_path: Path) -> None:
+        """通过 path 参数加载外部 JSON 列表."""
+        custom_path = tmp_path / "override.json"
+        custom_data = [
+            {"name": "custom_1", "formula": "open + close", "category": "Custom"},
+        ]
+        custom_path.write_text(json.dumps(custom_data), encoding="utf-8")
+
+        library = import_from_paper(str(custom_path))
+        assert library.size == 1
+        assert library.list_factors()[0].name == "custom_1"