Merge remote-tracking branch 'origin/main' into codex/research-artifact-contract-20260821

src/quant_engine/alpha_factors.py

@@ -15,7 +15,6 @@ 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
@@ -210,140 +209,6 @@ 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） ─────────────────────────
 
 
@@ -2890,10 +2755,6 @@ __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

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