fix: bound phase1 operator windows

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 +210,140 @@ 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)
+
+
 # ── 组合算子（alpha158 公式样本） ─────────────────────────
 
 
@@ -2755,6 +2890,10 @@ __all__ = [
     "max_pair",
     "min_pair",
     "indneutralize",
+    "ALPHA158_PHASE1_MAX_WINDOW",
+    "ALPHA158_PHASE1_OPERATOR_SPECS",
+    "list_phase1_operators",
+    "evaluate_phase1_operator",
     "alpha_001",
     "alpha_002",
     "alpha_003",

tests/test_alpha_factors.py

@@ -8,6 +8,7 @@ import pytest
 
 from quant_engine.alpha_factors import (
     ALPHA158_REGISTRY,
+    ALPHA158_PHASE1_OPERATOR_SPECS,
     alpha_001,
     alpha_002,
     alpha_003,
@@ -166,6 +167,8 @@ from quant_engine.alpha_factors import (
     alpha_156,
     alpha_157,
     alpha_158,
+    evaluate_phase1_operator,
+    list_phase1_operators,
     correlation,
     covariance,
     decay_linear,
@@ -1232,3 +1235,96 @@ 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)