feat: expand alpha operator dispatch

src/quant_engine/alpha_factors.py

@@ -15,6 +15,7 @@ v1.2.0 Phase 0：5 个基础算子 + 5 个 alpha 公式（alpha001–alpha005）
 
 from __future__ import annotations
 
+from collections.abc import Callable
 from typing import Any
 
 import numpy as np
@@ -209,6 +210,367 @@ def indneutralize(series: pd.Series, groups: pd.Series) -> pd.Series:
     return series - series.groupby(groups).transform("mean")
 
 
+# ── Phase 1 operator contract ──────────────────────────
+
+# This is deliberately a small, stable surface for downstream research
+# orchestration.  The full alpha158 formula catalogue can continue to grow,
+# while callers use one validated dispatch entry point for the first ten
+# deterministic building blocks.
+ALPHA158_PHASE1_MAX_WINDOW = 252
+
+ALPHA158_PHASE1_OPERATOR_SPECS: dict[str, dict[str, Any]] = {
+    "rank": {
+        "name": "rank",
+        "formula": "rank(series)",
+        "inputs": ["series"],
+        "windowed": False,
+    },
+    "delta": {
+        "name": "delta",
+        "formula": "delta(series, window)",
+        "inputs": ["series"],
+        "windowed": True,
+    },
+    "ts_mean": {
+        "name": "ts_mean",
+        "formula": "ts_mean(series, window)",
+        "inputs": ["series"],
+        "windowed": True,
+    },
+    "ts_std": {
+        "name": "ts_std",
+        "formula": "ts_std(series, window)",
+        "inputs": ["series"],
+        "windowed": True,
+    },
+    "ts_rank": {
+        "name": "ts_rank",
+        "formula": "ts_rank(series, window)",
+        "inputs": ["series"],
+        "windowed": True,
+    },
+    "correlation": {
+        "name": "correlation",
+        "formula": "correlation(series, secondary, window)",
+        "inputs": ["series", "secondary"],
+        "windowed": True,
+    },
+    "ts_min": {
+        "name": "ts_min",
+        "formula": "ts_min(series, window)",
+        "inputs": ["series"],
+        "windowed": True,
+    },
+    "ts_max": {
+        "name": "ts_max",
+        "formula": "ts_max(series, window)",
+        "inputs": ["series"],
+        "windowed": True,
+    },
+    "ts_sum": {
+        "name": "ts_sum",
+        "formula": "ts_sum(series, window)",
+        "inputs": ["series"],
+        "windowed": True,
+    },
+    "decay_linear": {
+        "name": "decay_linear",
+        "formula": "decay_linear(series, window)",
+        "inputs": ["series"],
+        "windowed": True,
+    },
+}
+
+_PHASE1_OPERATOR_FUNCTIONS: dict[str, Callable[..., pd.Series]] = {
+    "rank": rank,
+    "delta": delta,
+    "ts_mean": ts_mean,
+    "ts_std": ts_std,
+    "ts_rank": ts_rank,
+    "correlation": correlation,
+    "ts_min": ts_min,
+    "ts_max": ts_max,
+    "ts_sum": ts_sum,
+    "decay_linear": decay_linear,
+}
+
+
+def list_phase1_operators() -> tuple[str, ...]:
+    """Return the deterministic Phase 1 operator names in stable order."""
+    return tuple(ALPHA158_PHASE1_OPERATOR_SPECS)
+
+
+def evaluate_phase1_operator(
+    name: str,
+    series: pd.Series,
+    secondary: pd.Series | None = None,
+    *,
+    window: int | None = None,
+) -> pd.Series:
+    """Evaluate one of the ten Phase 1 operators with a validated contract.
+
+    ``window`` is required for time-series operators and forbidden for the
+    cross-sectional ``rank`` operator.  Binary ``correlation`` also requires
+    a same-index secondary series so that callers cannot silently introduce
+    alignment-dependent results.
+    """
+    if name not in ALPHA158_PHASE1_OPERATOR_SPECS:
+        raise KeyError(f"operator {name!r} not registered")
+
+    is_windowed = bool(ALPHA158_PHASE1_OPERATOR_SPECS[name]["windowed"])
+    if is_windowed:
+        if isinstance(window, bool) or not isinstance(window, int) or window <= 0:
+            raise ValueError(f"window must be a positive integer for {name}")
+        if window > ALPHA158_PHASE1_MAX_WINDOW:
+            raise ValueError(
+                f"window exceeds maximum supported value {ALPHA158_PHASE1_MAX_WINDOW} for {name}"
+            )
+    if not is_windowed and window is not None:
+        raise ValueError(f"window is not supported for {name}")
+
+    if name == "correlation":
+        if secondary is None:
+            raise ValueError("secondary is required for correlation")
+        if not series.index.equals(secondary.index):
+            raise ValueError("secondary index must align with series")
+        return correlation(series, secondary, window)  # type: ignore[arg-type]
+
+    if secondary is not None:
+        raise ValueError(f"secondary is not supported for {name}")
+
+    operator = _PHASE1_OPERATOR_FUNCTIONS[name]
+    if name == "rank":
+        return operator(series)
+    return operator(series, window)
+
+
+# ── Phase 2 cumulative operator contract ──────────────────────────────
+
+# Phase 2 is cumulative: downstream callers can upgrade to one dispatch
+# surface covering every existing alpha158 building block, while Phase 1
+# names, metadata, ordering, and evaluation remain unchanged.
+ALPHA158_PHASE2_MAX_WINDOW = ALPHA158_PHASE1_MAX_WINDOW
+
+ALPHA158_PHASE2_OPERATOR_SPECS: dict[str, dict[str, Any]] = {
+    name: {
+        **spec,
+        "parameters": ["window"] if bool(spec["windowed"]) else [],
+    }
+    for name, spec in ALPHA158_PHASE1_OPERATOR_SPECS.items()
+}
+ALPHA158_PHASE2_OPERATOR_SPECS.update(
+    {
+        "ts_argmin": {
+            "name": "ts_argmin",
+            "formula": "ts_argmin(series, window)",
+            "inputs": ["series"],
+            "parameters": ["window"],
+            "windowed": True,
+        },
+        "ts_argmax": {
+            "name": "ts_argmax",
+            "formula": "ts_argmax(series, window)",
+            "inputs": ["series"],
+            "parameters": ["window"],
+            "windowed": True,
+        },
+        "product": {
+            "name": "product",
+            "formula": "product(series, window)",
+            "inputs": ["series"],
+            "parameters": ["window"],
+            "windowed": True,
+        },
+        "returns": {
+            "name": "returns",
+            "formula": "returns(series)",
+            "inputs": ["series"],
+            "parameters": [],
+            "windowed": False,
+        },
+        "scale": {
+            "name": "scale",
+            "formula": "scale(series)",
+            "inputs": ["series"],
+            "parameters": [],
+            "windowed": False,
+        },
+        "signed_power": {
+            "name": "signed_power",
+            "formula": "signed_power(series, exponent)",
+            "inputs": ["series"],
+            "parameters": ["exponent"],
+            "windowed": False,
+        },
+        "stddev": {
+            "name": "stddev",
+            "formula": "stddev(series, window)",
+            "inputs": ["series"],
+            "parameters": ["window"],
+            "windowed": True,
+        },
+        "covariance": {
+            "name": "covariance",
+            "formula": "covariance(series, secondary, window)",
+            "inputs": ["series", "secondary"],
+            "parameters": ["window"],
+            "windowed": True,
+        },
+        "log": {
+            "name": "log",
+            "formula": "log(series)",
+            "inputs": ["series"],
+            "parameters": [],
+            "windowed": False,
+        },
+        "abs_series": {
+            "name": "abs_series",
+            "formula": "abs_series(series)",
+            "inputs": ["series"],
+            "parameters": [],
+            "windowed": False,
+        },
+        "sign": {
+            "name": "sign",
+            "formula": "sign(series)",
+            "inputs": ["series"],
+            "parameters": [],
+            "windowed": False,
+        },
+        "max_pair": {
+            "name": "max_pair",
+            "formula": "max_pair(series, secondary)",
+            "inputs": ["series", "secondary"],
+            "parameters": [],
+            "windowed": False,
+        },
+        "min_pair": {
+            "name": "min_pair",
+            "formula": "min_pair(series, secondary)",
+            "inputs": ["series", "secondary"],
+            "parameters": [],
+            "windowed": False,
+        },
+        "indneutralize": {
+            "name": "indneutralize",
+            "formula": "indneutralize(series, groups)",
+            "inputs": ["series", "groups"],
+            "parameters": [],
+            "windowed": False,
+        },
+    }
+)
+
+_PHASE2_OPERATOR_FUNCTIONS: dict[str, Callable[..., pd.Series]] = {
+    **_PHASE1_OPERATOR_FUNCTIONS,
+    "ts_argmin": ts_argmin,
+    "ts_argmax": ts_argmax,
+    "product": product,
+    "returns": returns,
+    "scale": scale,
+    "signed_power": signed_power,
+    "stddev": stddev,
+    "covariance": covariance,
+    "log": log,
+    "abs_series": abs_series,
+    "sign": sign,
+    "max_pair": max_pair,
+    "min_pair": min_pair,
+    "indneutralize": indneutralize,
+}
+
+_PHASE2_WINDOWED_OPERATORS = frozenset(
+    name for name, spec in ALPHA158_PHASE2_OPERATOR_SPECS.items() if bool(spec["windowed"])
+)
+_PHASE2_BINARY_OPERATORS = frozenset({"correlation", "covariance", "max_pair", "min_pair"})
+
+
+def list_phase2_operators() -> tuple[str, ...]:
+    """Return all Phase 2 operator names in stable cumulative order."""
+    return tuple(ALPHA158_PHASE2_OPERATOR_SPECS)
+
+
+def _validate_phase2_window(name: str, window: int | None) -> int:
+    if isinstance(window, bool) or not isinstance(window, int) or window <= 0:
+        raise ValueError(f"window must be a positive integer for {name}")
+    if window > ALPHA158_PHASE2_MAX_WINDOW:
+        raise ValueError(
+            f"window exceeds maximum supported value {ALPHA158_PHASE2_MAX_WINDOW} for {name}"
+        )
+    return window
+
+
+def evaluate_phase2_operator(
+    name: str,
+    series: pd.Series,
+    secondary: pd.Series | None = None,
+    *,
+    window: int | None = None,
+    exponent: float | None = None,
+    groups: pd.Series | None = None,
+) -> pd.Series:
+    """Evaluate any existing alpha158 building block through a strict contract.
+
+    Phase 2 rejects implicit alignment, missing required arguments, unused
+    arguments, unbounded windows, and non-finite exponents before dispatch.
+    """
+    if name not in ALPHA158_PHASE2_OPERATOR_SPECS:
+        raise KeyError(f"operator {name!r} not registered")
+    if not isinstance(series, pd.Series):
+        raise TypeError("series must be a pandas Series")
+
+    validated_window: int | None = None
+    if name in _PHASE2_WINDOWED_OPERATORS:
+        validated_window = _validate_phase2_window(name, window)
+    elif window is not None:
+        raise ValueError(f"window is not supported for {name}")
+
+    if name in _PHASE2_BINARY_OPERATORS:
+        if secondary is None:
+            raise ValueError(f"secondary is required for {name}")
+        if not isinstance(secondary, pd.Series):
+            raise TypeError("secondary must be a pandas Series")
+        if not series.index.equals(secondary.index):
+            raise ValueError("secondary index must align with series")
+    elif secondary is not None:
+        raise ValueError(f"secondary is not supported for {name}")
+
+    validated_exponent: float | None = None
+    if name == "signed_power":
+        if (
+            isinstance(exponent, bool)
+            or not isinstance(exponent, (int, float))
+            or not np.isfinite(exponent)
+        ):
+            raise ValueError("exponent must be a finite number for signed_power")
+        validated_exponent = float(exponent)
+    elif exponent is not None:
+        raise ValueError(f"exponent is not supported for {name}")
+
+    if name == "indneutralize":
+        if groups is None:
+            raise ValueError("groups is required for indneutralize")
+        if not isinstance(groups, pd.Series):
+            raise TypeError("groups must be a pandas Series")
+        if not series.index.equals(groups.index):
+            raise ValueError("groups index must align with series")
+    elif groups is not None:
+        raise ValueError(f"groups is not supported for {name}")
+
+    operator = _PHASE2_OPERATOR_FUNCTIONS[name]
+    if name == "signed_power":
+        return operator(series, validated_exponent)
+    if name == "indneutralize":
+        return operator(series, groups)
+    if name in {"correlation", "covariance"}:
+        return operator(series, secondary, validated_window)
+    if name in {"max_pair", "min_pair"}:
+        return operator(series, secondary)
+    if validated_window is not None:
+        return operator(series, validated_window)
+    return operator(series)
+
+
 # ── 组合算子（alpha158 公式样本） ─────────────────────────
 
 
@@ -2755,6 +3117,14 @@ __all__ = [
     "max_pair",
     "min_pair",
     "indneutralize",
+    "ALPHA158_PHASE1_MAX_WINDOW",
+    "ALPHA158_PHASE1_OPERATOR_SPECS",
+    "list_phase1_operators",
+    "evaluate_phase1_operator",
+    "ALPHA158_PHASE2_MAX_WINDOW",
+    "ALPHA158_PHASE2_OPERATOR_SPECS",
+    "list_phase2_operators",
+    "evaluate_phase2_operator",
     "alpha_001",
     "alpha_002",
     "alpha_003",

tests/test_alpha_factors.py

@@ -8,6 +8,8 @@ import pytest
 
 from quant_engine.alpha_factors import (
     ALPHA158_REGISTRY,
+    ALPHA158_PHASE1_OPERATOR_SPECS,
+    ALPHA158_PHASE2_OPERATOR_SPECS,
     alpha_001,
     alpha_002,
     alpha_003,
@@ -166,6 +168,10 @@ from quant_engine.alpha_factors import (
     alpha_156,
     alpha_157,
     alpha_158,
+    evaluate_phase1_operator,
+    evaluate_phase2_operator,
+    list_phase1_operators,
+    list_phase2_operators,
     correlation,
     covariance,
     decay_linear,
@@ -1232,3 +1238,228 @@ def test_parse_alpha_formula_round_trip_jsonb():
     serialized = json.dumps(parsed)
     assert isinstance(serialized, str)
     assert "ts_rank" in serialized
+
+
+# ── v1.2.0 Phase 1: deterministic operator dispatch contract ──────────────
+
+
+def test_phase1_operator_catalog_is_explicit_and_serializable():
+    """Phase 1 exposes a stable, JSON-friendly catalog for downstream callers."""
+    import json
+
+    expected = {
+        "rank",
+        "delta",
+        "ts_mean",
+        "ts_std",
+        "ts_rank",
+        "correlation",
+        "ts_min",
+        "ts_max",
+        "ts_sum",
+        "decay_linear",
+    }
+    assert set(list_phase1_operators()) == expected
+    assert set(ALPHA158_PHASE1_OPERATOR_SPECS) == expected
+    json.dumps(ALPHA158_PHASE1_OPERATOR_SPECS)
+    for name, spec in ALPHA158_PHASE1_OPERATOR_SPECS.items():
+        assert spec["name"] == name
+        assert isinstance(spec["inputs"], list)
+        assert isinstance(spec["formula"], str)
+
+
+def test_phase1_unary_operators_preserve_index_and_are_deterministic():
+    values = pd.Series([1.0, 2.0, 3.0, 4.0], index=["a", "b", "c", "d"])
+
+    first = evaluate_phase1_operator("rank", values)
+    second = evaluate_phase1_operator("rank", values)
+
+    pd.testing.assert_series_equal(first, second)
+    assert first.index.equals(values.index)
+    assert first.iloc[-1] == pytest.approx(1.0)
+
+
+@pytest.mark.parametrize(
+    ("name", "window"),
+    [
+        ("delta", 2),
+        ("ts_mean", 2),
+        ("ts_std", 2),
+        ("ts_rank", 2),
+        ("ts_min", 2),
+        ("ts_max", 2),
+        ("ts_sum", 2),
+        ("decay_linear", 2),
+    ],
+)
+def test_phase1_windowed_operators_require_explicit_window(name: str, window: int):
+    values = pd.Series([1.0, 2.0, 3.0, 4.0])
+
+    result = evaluate_phase1_operator(name, values, window=window)
+
+    assert result.index.equals(values.index)
+    with pytest.raises(ValueError, match="window"):
+        evaluate_phase1_operator(name, values)
+    with pytest.raises(ValueError, match="positive integer"):
+        evaluate_phase1_operator(name, values, window=1.5)  # type: ignore[arg-type]
+
+
+def test_phase1_binary_correlation_requires_aligned_secondary_input():
+    values = pd.Series([1.0, 2.0, 3.0, 4.0])
+    other = pd.Series([4.0, 3.0, 2.0, 1.0])
+
+    result = evaluate_phase1_operator("correlation", values, other, window=2)
+
+    assert result.iloc[-1] == pytest.approx(-1.0)
+    with pytest.raises(ValueError, match="secondary"):
+        evaluate_phase1_operator("correlation", values, window=2)
+
+
+def test_phase1_dispatch_rejects_unknown_or_unused_arguments():
+    values = pd.Series([1.0, 2.0, 3.0])
+
+    with pytest.raises(KeyError, match="not registered"):
+        evaluate_phase1_operator("unknown", values)
+    with pytest.raises(ValueError, match="window"):
+        evaluate_phase1_operator("rank", values, window=2)
+    with pytest.raises(ValueError, match="secondary"):
+        evaluate_phase1_operator("rank", values, values)
+
+
+def test_phase1_dispatch_rejects_window_above_supported_limit():
+    values = pd.Series([1.0, 2.0, 3.0])
+
+    with pytest.raises(ValueError, match="maximum"):
+        evaluate_phase1_operator("ts_mean", values, window=2**63)
+
+
+# ── v1.2.0 Phase 2: cumulative deterministic operator contract ─────────────
+
+
+def test_phase2_operator_catalog_is_cumulative_stable_and_serializable():
+    """Phase 2 exposes all existing building blocks without changing Phase 1."""
+    import json
+
+    phase1 = list_phase1_operators()
+    expected_phase2 = (
+        *phase1,
+        "ts_argmin",
+        "ts_argmax",
+        "product",
+        "returns",
+        "scale",
+        "signed_power",
+        "stddev",
+        "covariance",
+        "log",
+        "abs_series",
+        "sign",
+        "max_pair",
+        "min_pair",
+        "indneutralize",
+    )
+
+    assert list_phase2_operators() == expected_phase2
+    assert tuple(ALPHA158_PHASE2_OPERATOR_SPECS) == expected_phase2
+    assert tuple(ALPHA158_PHASE1_OPERATOR_SPECS) == phase1
+    json.dumps(ALPHA158_PHASE2_OPERATOR_SPECS)
+    for name, spec in ALPHA158_PHASE2_OPERATOR_SPECS.items():
+        assert spec["name"] == name
+        assert isinstance(spec["inputs"], list)
+        assert isinstance(spec["parameters"], list)
+        assert isinstance(spec["formula"], str)
+
+
+@pytest.mark.parametrize("name", ["ts_argmin", "ts_argmax", "product", "stddev"])
+def test_phase2_windowed_unary_dispatch_is_deterministic(name: str):
+    values = pd.Series([3.0, 1.0, 4.0, 2.0], index=["a", "b", "c", "d"])
+
+    first = evaluate_phase2_operator(name, values, window=3)
+    second = evaluate_phase2_operator(name, values, window=3)
+
+    pd.testing.assert_series_equal(first, second)
+    assert first.index.equals(values.index)
+    with pytest.raises(ValueError, match="window"):
+        evaluate_phase2_operator(name, values)
+
+
+@pytest.mark.parametrize("name", ["returns", "scale", "log", "abs_series", "sign"])
+def test_phase2_unary_dispatch_rejects_unused_arguments(name: str):
+    values = pd.Series([1.0, 2.0, 4.0], index=["a", "b", "c"])
+
+    result = evaluate_phase2_operator(name, values)
+
+    assert result.index.equals(values.index)
+    with pytest.raises(ValueError, match="window"):
+        evaluate_phase2_operator(name, values, window=2)
+    with pytest.raises(ValueError, match="secondary"):
+        evaluate_phase2_operator(name, values, secondary=values)
+
+
+@pytest.mark.parametrize(
+    ("name", "window"),
+    [("correlation", 2), ("covariance", 2), ("max_pair", None), ("min_pair", None)],
+)
+def test_phase2_binary_dispatch_requires_aligned_secondary(name: str, window: int | None):
+    values = pd.Series([1.0, 2.0, 3.0], index=["a", "b", "c"])
+    secondary = pd.Series([3.0, 2.0, 1.0], index=values.index)
+
+    result = evaluate_phase2_operator(name, values, secondary=secondary, window=window)
+
+    assert result.index.equals(values.index)
+    with pytest.raises(ValueError, match="secondary is required"):
+        evaluate_phase2_operator(name, values, window=window)
+    with pytest.raises(ValueError, match="secondary index"):
+        evaluate_phase2_operator(
+            name,
+            values,
+            secondary=secondary.rename(index={"c": "z"}),
+            window=window,
+        )
+
+
+def test_phase2_signed_power_requires_finite_numeric_exponent():
+    values = pd.Series([-4.0, 0.0, 9.0])
+
+    result = evaluate_phase2_operator("signed_power", values, exponent=0.5)
+
+    pd.testing.assert_series_equal(result, pd.Series([-2.0, 0.0, 3.0]))
+    for exponent in (None, True, float("inf"), float("nan"), "2"):
+        with pytest.raises(ValueError, match="exponent"):
+            evaluate_phase2_operator(  # type: ignore[arg-type]
+                "signed_power",
+                values,
+                exponent=exponent,
+            )
+
+
+def test_phase2_indneutralize_requires_aligned_groups():
+    values = pd.Series([1.0, 3.0, 10.0, 14.0], index=["a", "b", "c", "d"])
+    groups = pd.Series(["x", "x", "y", "y"], index=values.index)
+
+    result = evaluate_phase2_operator("indneutralize", values, groups=groups)
+
+    pd.testing.assert_series_equal(result, pd.Series([-1.0, 1.0, -2.0, 2.0], index=values.index))
+    with pytest.raises(ValueError, match="groups is required"):
+        evaluate_phase2_operator("indneutralize", values)
+    with pytest.raises(ValueError, match="groups index"):
+        evaluate_phase2_operator(
+            "indneutralize",
+            values,
+            groups=groups.rename(index={"d": "z"}),
+        )
+
+
+def test_phase2_dispatch_validates_primary_series_and_unused_parameters():
+    values = pd.Series([1.0, 2.0, 3.0])
+
+    with pytest.raises(TypeError, match="series must be a pandas Series"):
+        evaluate_phase2_operator("rank", [1.0, 2.0, 3.0])  # type: ignore[arg-type]
+    with pytest.raises(KeyError, match="not registered"):
+        evaluate_phase2_operator("unknown", values)
+    with pytest.raises(ValueError, match="exponent"):
+        evaluate_phase2_operator("rank", values, exponent=2.0)
+    with pytest.raises(ValueError, match="groups"):
+        evaluate_phase2_operator("rank", values, groups=pd.Series(["x", "x", "x"]))
+    with pytest.raises(ValueError, match="maximum"):
+        evaluate_phase2_operator("product", values, window=253)