feat: 引入 FactorMiner 开源量化因子挖掘项目

tests/test_factor_storage.py

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+"""FactorStorage 单元测试。"""
+
+import numpy as np
+import pandas as pd
+import polars as pl
+import pytest
+
+from src.data.factor_storage import FactorStorage
+
+
+@pytest.fixture
+def storage(tmp_path):
+    return FactorStorage(base_dir=tmp_path / "factor")
+
+
+@pytest.fixture
+def sample_df():
+    return pl.DataFrame(
+        {
+            "trade_date": ["20240101", "20240102", "20240103"],
+            "ts_code": ["000001.SZ", "000002.SZ", "000003.SZ"],
+            "test_factor": [1.0, 2.0, 3.0],
+        }
+    )
+
+
+def test_exists_and_save_load(storage, sample_df):
+    assert not storage.exists("test_factor")
+    storage.save("test_factor", sample_df)
+    assert storage.exists("test_factor")
+
+    loaded = storage.load("test_factor")
+    assert loaded.shape == (3, 3)
+    assert set(loaded.columns) == {"trade_date", "ts_code", "test_factor"}
+    assert loaded["test_factor"].to_list() == [1.0, 2.0, 3.0]
+
+
+def test_get_date_range(storage, sample_df):
+    storage.save("test_factor", sample_df)
+    dr = storage.get_date_range("test_factor")
+    assert dr == ("20240101", "20240103")
+
+    assert storage.get_date_range("missing") is None
+
+
+def test_load_with_date_filter(storage, sample_df):
+    storage.save("test_factor", sample_df)
+    loaded = storage.load("test_factor", start_date="20240102")
+    assert loaded.shape == (2, 3)
+    assert loaded["trade_date"].to_list() == ["20240102", "20240103"]
+
+
+def test_incremental_update(storage, sample_df):
+    # 第一次写入
+    storage.save("test_factor", sample_df)
+
+    # 第二次写入：新增日期 + 覆盖已有日期
+    new_df = pl.DataFrame(
+        {
+            "trade_date": ["20240103", "20240104"],
+            "ts_code": ["000003.SZ", "000004.SZ"],
+            "test_factor": [30.0, 4.0],
+        }
+    )
+    storage.save("test_factor", new_df)
+
+    loaded = storage.load("test_factor").sort(["trade_date", "ts_code"])
+    assert loaded.shape == (4, 3)
+    assert loaded["test_factor"].to_list() == [1.0, 2.0, 30.0, 4.0]
+
+
+def test_validate_pass_when_no_local_file(storage, sample_df):
+    passed, stats = storage.validate("test_factor", sample_df)
+    assert passed is True
+    assert stats == {}
+
+
+def test_validate_pass_with_identical_data(storage, sample_df):
+    storage.save("test_factor", sample_df)
+    passed, stats = storage.validate("test_factor", sample_df)
+    assert passed is True
+    assert stats["matched_rows"] == 3
+    assert stats["max_abs_diff"] == pytest.approx(0.0)
+    assert stats["mean_abs_diff"] == pytest.approx(0.0)
+
+
+def test_validate_fail_on_data_mismatch(storage, sample_df):
+    storage.save("test_factor", sample_df)
+
+    modified = sample_df.with_columns(
+        pl.when(pl.col("trade_date") == "20240101")
+        .then(pl.col("test_factor") + 1.0)
+        .otherwise(pl.col("test_factor"))
+        .alias("test_factor")
+    )
+    passed, stats = storage.validate("test_factor", modified, tolerance=1e-6)
+    assert passed is False
+    assert stats["matched_rows"] == 3
+    assert stats["max_abs_diff"] == pytest.approx(1.0)
+
+
+def test_validate_pass_with_non_overlapping_data(storage, sample_df):
+    storage.save("test_factor", sample_df)
+
+    non_overlap = pl.DataFrame(
+        {
+            "trade_date": ["20240105"],
+            "ts_code": ["000001.SZ"],
+            "test_factor": [99.0],
+        }
+    )
+    passed, stats = storage.validate("test_factor", non_overlap)
+    assert passed is True
+    assert stats == {}
+
+
+def test_save_preserves_column_order(storage):
+    df = pl.DataFrame(
+        {
+            "trade_date": ["20240101"],
+            "ts_code": ["000001.SZ"],
+            "my_factor": [1.5],
+        }
+    )
+    storage.save("my_factor", df)
+    pdf = pd.read_hdf(storage._file_path("my_factor"), key=storage._HDF_KEY)
+    assert list(pdf.columns) == ["trade_date", "ts_code", "my_factor"]  # type: ignore[attr-defined]