feat(factor): wire up builtin factor auto-registration and export

src/easy_tdx/factor/__init__.py

@@ -1,7 +1,18 @@
 # src/easy_tdx/factor/__init__.py
 """因子研究模块。"""
+from __future__ import annotations
 
 from easy_tdx.factor.base import FACTORY_REGISTRY, Factor, register_factor
 from easy_tdx.factor.engine import FactorEngine
 
-__all__ = ["Factor", "register_factor", "FACTORY_REGISTRY", "FactorEngine"]
+# 导入 builtin 触发自动注册
+from easy_tdx.factor.builtin import get_factor, list_factors  # noqa: F401
+
+__all__ = [
+    "Factor",
+    "register_factor",
+    "FACTORY_REGISTRY",
+    "FactorEngine",
+    "list_factors",
+    "get_factor",
+]

src/easy_tdx/factor/builtin/__init__.py

@@ -1,2 +1,41 @@
-"""内置因子包 — 导入即注册。"""
+"""内置因子库 — 导入子模块触发注册。"""
 from __future__ import annotations
+
+from easy_tdx.factor.base import FACTORY_REGISTRY, Factor
+
+# 导入所有子模块以触发 @register_factor 装饰器
+from easy_tdx.factor.builtin import (  # noqa: F401
+    chanlun,
+    momentum,
+    quality,
+    technical,
+    value,
+    volatility,
+    volume,
+)
+
+
+def list_factors() -> list[dict[str, str | tuple[str, ...]]]:
+    """返回所有已注册因子的元数据。"""
+    return [
+        {
+            "name": cls.name,
+            "category": cls.category,
+            "description": cls.description,
+            "inputs": cls.inputs,
+        }
+        for cls in FACTORY_REGISTRY.values()
+    ]
+
+
+def get_factor(name: str) -> type[Factor]:
+    """按名称获取因子类。
+
+    Raises:
+        ValueError: 因子不存在。
+    """
+    if name not in FACTORY_REGISTRY:
+        raise ValueError(
+            f"未知因子: {name!r}。可用因子: {sorted(FACTORY_REGISTRY.keys())}"
+        )
+    return FACTORY_REGISTRY[name]

tests/unit/test_factor_builtin.py

@@ -0,0 +1,208 @@
+"""Test built-in factor computation correctness."""
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from easy_tdx.factor.base import FACTORY_REGISTRY
+from easy_tdx.factor.builtin import get_factor, list_factors
+
+
+def _make_df(n: int = 120, seed: int = 42) -> pd.DataFrame:
+    """生成合成 OHLCV 数据（120 行，满足所有因子最小窗口）。"""
+    rng = np.random.default_rng(seed)
+    close = 10.0 + np.cumsum(rng.normal(0, 0.3, n))
+    close = np.maximum(close, 1.0)
+    high = close + rng.uniform(0, 0.3, n)
+    low = close - rng.uniform(0, 0.3, n)
+    low = np.maximum(low, 0.1)
+    open_ = low + rng.uniform(0, high - low, n)
+    vol = rng.integers(100_000, 10_000_000, n).astype(float)
+    amount = close * vol
+
+    dates = pd.date_range("2024-01-01", periods=n, freq="D")
+    return pd.DataFrame({
+        "datetime": dates,
+        "open": open_,
+        "high": high,
+        "low": low,
+        "close": close,
+        "vol": vol,
+        "amount": amount,
+    })
+
+
+# ── Auto-registration ──────────────────────────────────────────────
+
+
+class TestAutoRegistration:
+    def test_momentum_factors_registered(self):
+        assert "momentum_20d" in FACTORY_REGISTRY
+        assert "momentum_60d" in FACTORY_REGISTRY
+        assert "reversal_5d" in FACTORY_REGISTRY
+
+    def test_volatility_factors_registered(self):
+        assert "volatility_20d" in FACTORY_REGISTRY
+        assert "atr_14d" in FACTORY_REGISTRY
+        assert "turnover_rate" in FACTORY_REGISTRY
+
+    def test_quality_factors_registered(self):
+        assert "sharpe_20d" in FACTORY_REGISTRY
+        assert "max_drawdown_20d" in FACTORY_REGISTRY
+        assert "win_rate_20d" in FACTORY_REGISTRY
+
+    def test_volume_factors_registered(self):
+        assert "obv_trend" in FACTORY_REGISTRY
+        assert "vol_surge" in FACTORY_REGISTRY
+        assert "amount_ma_ratio" in FACTORY_REGISTRY
+
+    def test_technical_factors_registered(self):
+        assert "macd_hist_signal" in FACTORY_REGISTRY
+        assert "rsi_14" in FACTORY_REGISTRY
+        assert "boll_position" in FACTORY_REGISTRY
+
+    def test_chanlun_factors_registered(self):
+        assert "chanlun_bi_dir" in FACTORY_REGISTRY
+        assert "chanlun_mmd" in FACTORY_REGISTRY
+
+    def test_value_factors_registered(self):
+        assert "pe_ratio" in FACTORY_REGISTRY
+        assert "pb_ratio" in FACTORY_REGISTRY
+
+    def test_total_factor_count(self):
+        assert len(FACTORY_REGISTRY) >= 19
+
+
+# ── list_factors / get_factor ───────────────────────────────────────
+
+
+class TestListAndGetFactors:
+    def test_list_factors_returns_all(self):
+        factors = list_factors()
+        assert len(factors) >= 19
+        for f in factors:
+            assert "name" in f
+            assert "category" in f
+            assert "description" in f
+
+    def test_get_factor_existing(self):
+        cls = get_factor("momentum_20d")
+        assert cls.name == "momentum_20d"
+
+    def test_get_factor_nonexistent(self):
+        with pytest.raises(ValueError, match="未知因子"):
+            get_factor("nonexistent")
+
+
+# ── Momentum compute ───────────────────────────────────────────────
+
+
+class TestMomentumCompute:
+    def test_momentum_20d(self):
+        f = get_factor("momentum_20d")()
+        df = _make_df()
+        result = f.compute(df)
+        assert isinstance(result, pd.Series)
+        assert len(result) == len(df)
+        assert not np.isnan(result.iloc[20])
+
+    def test_momentum_60d(self):
+        f = get_factor("momentum_60d")()
+        df = _make_df()
+        result = f.compute(df)
+        assert not np.isnan(result.iloc[60])
+
+    def test_reversal_5d_is_negative_return(self):
+        f = get_factor("reversal_5d")()
+        df = _make_df()
+        result = f.compute(df)
+        expected = -df["close"].pct_change(5)
+        pd.testing.assert_series_equal(result, expected, check_names=False)
+
+
+# ── Volatility compute ─────────────────────────────────────────────
+
+
+class TestVolatilityCompute:
+    def test_volatility_20d(self):
+        f = get_factor("volatility_20d")()
+        df = _make_df()
+        result = f.compute(df)
+        assert result.iloc[20] > 0
+
+    def test_atr_14d(self):
+        f = get_factor("atr_14d")()
+        df = _make_df()
+        result = f.compute(df)
+        assert result.iloc[14] > 0
+
+    def test_turnover_rate(self):
+        f = get_factor("turnover_rate")()
+        df = _make_df()
+        result = f.compute(df)
+        assert result.iloc[40] > 0
+
+
+# ── Quality compute ────────────────────────────────────────────────
+
+
+class TestQualityCompute:
+    def test_sharpe_20d(self):
+        f = get_factor("sharpe_20d")()
+        df = _make_df()
+        result = f.compute(df)
+        assert len(result) == len(df)
+
+    def test_max_drawdown_20d(self):
+        f = get_factor("max_drawdown_20d")()
+        df = _make_df()
+        result = f.compute(df)
+        valid = result.dropna()
+        assert (valid <= 0).all()
+
+    def test_win_rate_20d(self):
+        f = get_factor("win_rate_20d")()
+        df = _make_df()
+        result = f.compute(df)
+        valid = result.dropna()
+        assert (valid >= 0).all()
+        assert (valid <= 1).all()
+
+
+# ── Volume compute ─────────────────────────────────────────────────
+
+
+class TestVolumeCompute:
+    def test_vol_surge(self):
+        f = get_factor("vol_surge")()
+        df = _make_df()
+        result = f.compute(df)
+        assert result.iloc[20] > 0
+
+    def test_amount_ma_ratio(self):
+        f = get_factor("amount_ma_ratio")()
+        df = _make_df()
+        result = f.compute(df)
+        assert len(result) == len(df)
+
+
+# ── Technical compute ──────────────────────────────────────────────
+
+
+class TestTechnicalCompute:
+    def test_rsi_14_range(self):
+        f = get_factor("rsi_14")()
+        df = _make_df()
+        result = f.compute(df)
+        valid = result.dropna()
+        assert (valid >= -1).all()
+        assert (valid <= 1).all()
+
+    def test_boll_position_range(self):
+        f = get_factor("boll_position")()
+        df = _make_df()
+        result = f.compute(df)
+        valid = result.dropna()
+        assert (valid >= 0).all()
+        assert (valid <= 1).all()