docs(handoff): record market lineage validation

feat(data): snapshot asset returns with stable lineage
docs: record covariance estimator decision
2026-08-24 10:44:29 +08:00 · 2026-08-24 10:42:34 +08:00 · 2026-08-21 23:49:06 +08:00 · 2026-08-21 23:47:29 +08:00 · 2026-08-21 23:45:40 +08:00 · 2026-08-21 23:41:21 +08:00
11 changed files with 10 additions and 2047 deletions
@@ -16,8 +16,6 @@ permissions:
 jobs:
  lite:
    runs-on: ubuntu-latest
    env:
      UV_PYTHON_DOWNLOADS: never
    steps:
      - uses: actions/checkout@524e936cd9e579adf00e308bfdf971aebc7de09e
        with:
@@ -28,18 +26,7 @@ jobs:
          if git ls-files .DS_Store | grep -q .; then echo "跟踪 .DS_Store"; exit 1; fi
          if git grep -n -I -E 'sk-[A-Za-z0-9]{20,}|AKIA[0-9A-Z]{16}|ghp_[A-Za-z0-9]{36}|xox[baprs]-[A-Za-z0-9-]{10,}' HEAD | grep -q .; then echo "检出疑似凭证"; exit 1; fi
          echo "Gitea 合规校验通过"
      - name: 验证并同步共享运行时
        run: |
          test "$(python3 --version)" = "Python 3.13.15"
          test "$(uv --version | cut -d' ' -f1-2)" = "uv 0.12.3"
          uv sync --locked --extra dev
          uv run --locked --no-sync python -c 'import sys; assert sys.version_info[:2] == (3, 13)'
      - name: 架构模块契约测试
-        run: |
+        run: python3 tests/governance/test_module_spec.py
          uv run --locked --no-sync python tests/governance/test_module_spec.py
          uv run --locked --no-sync python tests/governance/test_ci_contract.py
      - name: Syntax check
-        run: git ls-files -z '*.py' | xargs -0 uv run --locked --no-sync python -m py_compile
+        run: git ls-files -z '*.py' | xargs -0 python3 -m py_compile
@@ -1 +0,0 @@
 3.13
@@ -1,9 +0,0 @@
 profile: lite
 runtime_contract: v1
 language: python
 python_version: "3.13"
 python_manager: uv
 python_root: "."
 local_test_command: "python3 tests/governance/test_module_spec.py"
 requires_database: false
 integration_profile: none
@@ -45,8 +45,6 @@
 - `prepare_asset_return_snapshot` 从规范化长表行情生成不前向填充的 simple daily returns；
  显式 ingestion snapshot ID、源/字段/复权口径、价格值和缺失掩码共同形成
  `asset-returns-v1:<sha256>`，并把同一 ID 传给 covariance 与 run artifact。
 - artifact builder fail closed：每个 `CovarianceSnapshot.data_snapshot_id` 必须与 run 级
  `data_snapshot_id` 完全一致，禁止把其他行情快照的风险分解静默发布到当前研究运行。
 - shrinkage 适配器本轮不实现：scikit-learn 尚非声明依赖，未来只允许薄适配
  `LedoitWolf` / `OAS`，不复制公式、不依赖环境偶然安装状态。
@@ -7,7 +7,7 @@ name = "quant_engine"
 version = "0.1.0"
 description = "量化研究引擎 —— alpha 因子库 + 执行仿真 + 技术指标 + 数据适配 + 回测工具（v1.2.0 从 research_results 抽出）"
 readme = "README.md"
-requires-python = ">=3.13,<3.14"
+requires-python = ">=3.11"
 license = { text = "MIT" }
 authors = [
    { name = "researchhub team" },
@@ -39,7 +39,7 @@ where = ["src"]
 [tool.ruff]
 line-length = 100
-target-version = "py313"
+target-version = "py311"
 [tool.ruff.lint]
 select = ["E", "F", "W", "I", "N", "UP", "B", "A", "C4", "PT", "RUF"]
@@ -54,13 +54,10 @@ ignore = [
 ]
 [tool.mypy]
-python_version = "3.13"
+python_version = "3.11"
 strict = true
 ignore_missing_imports = true
 [tool.uv]
 index-url = "https://mirrors.cloud.tencent.com/pypi/simple"
 [tool.pytest.ini_options]
 testpaths = ["tests"]
 addopts = "-v --tb=short"
@@ -15,9 +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, Mapping
+from typing import Any
 from types import MappingProxyType
 from typing import Any, cast
 import numpy as np
 import pandas as pd
@@ -211,367 +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)
 # ── 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） ─────────────────────────
@@ -3093,452 +2730,6 @@ def parse_alpha_formula(formula_str: str) -> dict[str, Any]:
    return parsed
 # ── Phase 3 formula contract: frozen alpha001-alpha050 surface ──────────────
 # Formula functions remain the implementation source of truth.  This contract
 # freezes their callable surface separately from formula dependencies so that
 # historical compatibility-only arguments remain explicit without rewriting
 # formulas or changing direct-call APIs.
 ALPHA158_PHASE3_FORMULA_CONTRACT_VERSION = "1.0.0"
 ALPHA158_PHASE3_FORMULA_CATALOG_SHA256 = (
    "9a3360e5ee77a85a35d3c2fdab1efaa531fa0c263a2cb2bb5b965a1fb96fe1bd"
 )
 _PHASE3_FORMULA_FUNCTIONS: dict[str, Callable[..., pd.Series]] = {
    "alpha_001": alpha_001,
    "alpha_002": alpha_002,
    "alpha_003": alpha_003,
    "alpha_004": alpha_004,
    "alpha_005": alpha_005,
    "alpha_006": alpha_006,
    "alpha_007": alpha_007,
    "alpha_008": alpha_008,
    "alpha_009": alpha_009,
    "alpha_010": alpha_010,
    "alpha_011": alpha_011,
    "alpha_012": alpha_012,
    "alpha_013": alpha_013,
    "alpha_014": alpha_014,
    "alpha_015": alpha_015,
    "alpha_016": alpha_016,
    "alpha_017": alpha_017,
    "alpha_018": alpha_018,
    "alpha_019": alpha_019,
    "alpha_020": alpha_020,
    "alpha_021": alpha_021,
    "alpha_022": alpha_022,
    "alpha_023": alpha_023,
    "alpha_024": alpha_024,
    "alpha_025": alpha_025,
    "alpha_026": alpha_026,
    "alpha_027": alpha_027,
    "alpha_028": alpha_028,
    "alpha_029": alpha_029,
    "alpha_030": alpha_030,
    "alpha_031": alpha_031,
    "alpha_032": alpha_032,
    "alpha_033": alpha_033,
    "alpha_034": alpha_034,
    "alpha_035": alpha_035,
    "alpha_036": alpha_036,
    "alpha_037": alpha_037,
    "alpha_038": alpha_038,
    "alpha_039": alpha_039,
    "alpha_040": alpha_040,
    "alpha_041": alpha_041,
    "alpha_042": alpha_042,
    "alpha_043": alpha_043,
    "alpha_044": alpha_044,
    "alpha_045": alpha_045,
    "alpha_046": alpha_046,
    "alpha_047": alpha_047,
    "alpha_048": alpha_048,
    "alpha_049": alpha_049,
    "alpha_050": alpha_050,
 }
 _PHASE3_FORMULA_INPUT_OVERRIDES: dict[str, list[str]] = {
    "alpha_011": ["close", "high", "low"],
    "alpha_035": ["volume"],
    "alpha_036": ["close"],
    "alpha_040": ["high", "low"],
    "alpha_042": ["close"],
    "alpha_043": ["volume"],
 }
 _PHASE3_INPUT_CATEGORIES = {
    1: "single",
    2: "pair",
    3: "triple",
    4: "quadruple",
 }
 def _phase3_call_inputs(function: Callable[..., pd.Series]) -> list[str]:
    import inspect
    parameters = list(inspect.signature(function).parameters.values())
    if any(
        parameter.kind is not inspect.Parameter.POSITIONAL_OR_KEYWORD
        or parameter.default is not inspect.Parameter.empty
        for parameter in parameters
    ):
        raise RuntimeError(f"unsupported formula signature for {function.__name__}")
    return ["open" if parameter.name == "open_" else parameter.name for parameter in parameters]
 def _phase3_string_list(meta: dict[str, Any], field: str, alpha_id: str) -> list[str]:
    value = meta[field]
    if not isinstance(value, list) or not all(isinstance(item, str) for item in value):
        raise RuntimeError(f"{field} must be a list of strings for {alpha_id}")
    return list(value)
 def _build_phase3_formula_specs() -> dict[str, dict[str, Any]]:
    specs: dict[str, dict[str, Any]] = {}
    for alpha_id, function in _PHASE3_FORMULA_FUNCTIONS.items():
        meta = ALPHA158_REGISTRY[alpha_id]
        call_inputs = _phase3_call_inputs(function)
        formula_inputs = _PHASE3_FORMULA_INPUT_OVERRIDES.get(alpha_id, call_inputs)
        input_category = _PHASE3_INPUT_CATEGORIES.get(len(call_inputs))
        if input_category is None:
            raise RuntimeError(f"unsupported formula input count for {alpha_id}")
        specs[alpha_id] = {
            "name": alpha_id,
            "contract_version": ALPHA158_PHASE3_FORMULA_CONTRACT_VERSION,
            "formula": meta["formula"],
            "category": meta["category"],
            "complexity": meta["complexity"],
            "parameters": _phase3_string_list(meta, "params", alpha_id),
            "description": meta["description"],
            "references": _phase3_string_list(meta, "references", alpha_id),
            "call_inputs": list(call_inputs),
            "formula_inputs": list(formula_inputs),
            "input_category": input_category,
        }
    return specs
 def _freeze_phase3_formula_specs(
    specs: dict[str, dict[str, Any]],
 ) -> Mapping[str, Mapping[str, Any]]:
    frozen_specs: dict[str, Mapping[str, Any]] = {}
    for alpha_id, spec in specs.items():
        frozen_specs[alpha_id] = MappingProxyType(
            {field: tuple(value) if isinstance(value, list) else value for field, value in spec.items()}
        )
    return MappingProxyType(frozen_specs)
 ALPHA158_PHASE3_FORMULA_SPECS: Mapping[str, Mapping[str, Any]] = (
    _freeze_phase3_formula_specs(_build_phase3_formula_specs())
 )
 def list_phase3_formulas() -> tuple[str, ...]:
    """Return the frozen alpha001-alpha050 formula IDs in stable order."""
    return tuple(ALPHA158_PHASE3_FORMULA_SPECS)
 def evaluate_phase3_formula(name: str, **inputs: pd.Series) -> pd.Series:
    """Evaluate a Phase 3 formula with an exact, alignment-safe input contract."""
    if name not in ALPHA158_PHASE3_FORMULA_SPECS:
        raise KeyError(f"formula {name!r} not registered")
    spec = ALPHA158_PHASE3_FORMULA_SPECS[name]
    required_inputs = cast(tuple[str, ...], spec["call_inputs"])
    missing_inputs = [field for field in required_inputs if field not in inputs]
    unexpected_inputs = sorted(field for field in inputs if field not in required_inputs)
    if missing_inputs or unexpected_inputs:
        details: list[str] = []
        if missing_inputs:
            details.append(f"missing inputs {missing_inputs}")
        if unexpected_inputs:
            details.append(f"unexpected inputs {unexpected_inputs}")
        raise ValueError(f"invalid inputs for {name}: {'; '.join(details)}")
    for field in required_inputs:
        if not isinstance(inputs[field], pd.Series):
            raise TypeError(f"{field} must be a pandas Series")
    primary_field = required_inputs[0]
    primary = inputs[primary_field]
    for field in required_inputs[1:]:
        if not primary.index.equals(inputs[field].index):
            raise ValueError(f"{field} index must align with {primary_field}")
    function = _PHASE3_FORMULA_FUNCTIONS[name]
    return function(*(inputs[field] for field in required_inputs))
 # ── Phase 4 formula contract: frozen alpha051-alpha100 surface ──────────────
 # Phase 4 extends the versioned formula contract without mutating the Phase 3
 # catalogue, digest, dispatch surface, or the existing formula functions.
 ALPHA158_PHASE4_FORMULA_CONTRACT_VERSION = "1.0.0"
 ALPHA158_PHASE4_FORMULA_CATALOG_SHA256 = (
    "858daf5e2abab5063fc28fcf7c79936096e2c76dde582fc7bab78b3458f17054"
 )
 _PHASE4_FORMULA_FUNCTIONS: dict[str, Callable[..., pd.Series]] = {
    "alpha_051": alpha_051,
    "alpha_052": alpha_052,
    "alpha_053": alpha_053,
    "alpha_054": alpha_054,
    "alpha_055": alpha_055,
    "alpha_056": alpha_056,
    "alpha_057": alpha_057,
    "alpha_058": alpha_058,
    "alpha_059": alpha_059,
    "alpha_060": alpha_060,
    "alpha_061": alpha_061,
    "alpha_062": alpha_062,
    "alpha_063": alpha_063,
    "alpha_064": alpha_064,
    "alpha_065": alpha_065,
    "alpha_066": alpha_066,
    "alpha_067": alpha_067,
    "alpha_068": alpha_068,
    "alpha_069": alpha_069,
    "alpha_070": alpha_070,
    "alpha_071": alpha_071,
    "alpha_072": alpha_072,
    "alpha_073": alpha_073,
    "alpha_074": alpha_074,
    "alpha_075": alpha_075,
    "alpha_076": alpha_076,
    "alpha_077": alpha_077,
    "alpha_078": alpha_078,
    "alpha_079": alpha_079,
    "alpha_080": alpha_080,
    "alpha_081": alpha_081,
    "alpha_082": alpha_082,
    "alpha_083": alpha_083,
    "alpha_084": alpha_084,
    "alpha_085": alpha_085,
    "alpha_086": alpha_086,
    "alpha_087": alpha_087,
    "alpha_088": alpha_088,
    "alpha_089": alpha_089,
    "alpha_090": alpha_090,
    "alpha_091": alpha_091,
    "alpha_092": alpha_092,
    "alpha_093": alpha_093,
    "alpha_094": alpha_094,
    "alpha_095": alpha_095,
    "alpha_096": alpha_096,
    "alpha_097": alpha_097,
    "alpha_098": alpha_098,
    "alpha_099": alpha_099,
    "alpha_100": alpha_100,
 }
 _PHASE4_INPUT_CATEGORIES = {
    1: "single",
    2: "pair",
    3: "triple",
    4: "quadruple",
    5: "quintuple",
 }
 def _build_phase4_formula_specs() -> dict[str, dict[str, Any]]:
    specs: dict[str, dict[str, Any]] = {}
    for alpha_id, function in _PHASE4_FORMULA_FUNCTIONS.items():
        meta = ALPHA158_REGISTRY[alpha_id]
        call_inputs = _phase3_call_inputs(function)
        formula_inputs = _phase3_string_list(meta, "inputs", alpha_id)
        input_category = _PHASE4_INPUT_CATEGORIES.get(len(call_inputs))
        if input_category is None:
            raise RuntimeError(f"unsupported formula input count for {alpha_id}")
        specs[alpha_id] = {
            "name": alpha_id,
            "contract_version": ALPHA158_PHASE4_FORMULA_CONTRACT_VERSION,
            "formula": meta["formula"],
            "category": meta["category"],
            "complexity": meta["complexity"],
            "parameters": _phase3_string_list(meta, "params", alpha_id),
            "description": meta["description"],
            "references": _phase3_string_list(meta, "references", alpha_id),
            "call_inputs": list(call_inputs),
            "formula_inputs": formula_inputs,
            "input_category": input_category,
        }
    return specs
 ALPHA158_PHASE4_FORMULA_SPECS: Mapping[str, Mapping[str, Any]] = (
    _freeze_phase3_formula_specs(_build_phase4_formula_specs())
 )
 def list_phase4_formulas() -> tuple[str, ...]:
    """Return the frozen alpha051-alpha100 formula IDs in stable order."""
    return tuple(ALPHA158_PHASE4_FORMULA_SPECS)
 def evaluate_phase4_formula(name: str, **inputs: pd.Series) -> pd.Series:
    """Evaluate a Phase 4 formula with an exact, alignment-safe input contract."""
    if name not in ALPHA158_PHASE4_FORMULA_SPECS:
        raise KeyError(f"formula {name!r} not registered")
    spec = ALPHA158_PHASE4_FORMULA_SPECS[name]
    required_inputs = cast(tuple[str, ...], spec["call_inputs"])
    missing_inputs = [field for field in required_inputs if field not in inputs]
    unexpected_inputs = sorted(field for field in inputs if field not in required_inputs)
    if missing_inputs or unexpected_inputs:
        details: list[str] = []
        if missing_inputs:
            details.append(f"missing inputs {missing_inputs}")
        if unexpected_inputs:
            details.append(f"unexpected inputs {unexpected_inputs}")
        raise ValueError(f"invalid inputs for {name}: {'; '.join(details)}")
    for field in required_inputs:
        if not isinstance(inputs[field], pd.Series):
            raise TypeError(f"{field} must be a pandas Series")
    primary_field = required_inputs[0]
    primary = inputs[primary_field]
    for field in required_inputs[1:]:
        if not primary.index.equals(inputs[field].index):
            raise ValueError(f"{field} index must align with {primary_field}")
    function = _PHASE4_FORMULA_FUNCTIONS[name]
    return function(*(inputs[field] for field in required_inputs))
 # ── Phase 5 formula contract: frozen alpha101-alpha150 surface ──────────────
 # Phase 5 extends the versioned formula contract without mutating any earlier
 # catalogue, digest, dispatch surface, or existing formula implementation.
 ALPHA158_PHASE5_FORMULA_CONTRACT_VERSION = "1.0.0"
 ALPHA158_PHASE5_FORMULA_CATALOG_SHA256 = (
    "3368796169c9fbd39c4a34ea137e569964b15882fbf4de25124790d548db6533"
 )
 _PHASE5_FORMULA_FUNCTIONS: dict[str, Callable[..., pd.Series]] = {
    "alpha_101": alpha_101,
    "alpha_102": alpha_102,
    "alpha_103": alpha_103,
    "alpha_104": alpha_104,
    "alpha_105": alpha_105,
    "alpha_106": alpha_106,
    "alpha_107": alpha_107,
    "alpha_108": alpha_108,
    "alpha_109": alpha_109,
    "alpha_110": alpha_110,
    "alpha_111": alpha_111,
    "alpha_112": alpha_112,
    "alpha_113": alpha_113,
    "alpha_114": alpha_114,
    "alpha_115": alpha_115,
    "alpha_116": alpha_116,
    "alpha_117": alpha_117,
    "alpha_118": alpha_118,
    "alpha_119": alpha_119,
    "alpha_120": alpha_120,
    "alpha_121": alpha_121,
    "alpha_122": alpha_122,
    "alpha_123": alpha_123,
    "alpha_124": alpha_124,
    "alpha_125": alpha_125,
    "alpha_126": alpha_126,
    "alpha_127": alpha_127,
    "alpha_128": alpha_128,
    "alpha_129": alpha_129,
    "alpha_130": alpha_130,
    "alpha_131": alpha_131,
    "alpha_132": alpha_132,
    "alpha_133": alpha_133,
    "alpha_134": alpha_134,
    "alpha_135": alpha_135,
    "alpha_136": alpha_136,
    "alpha_137": alpha_137,
    "alpha_138": alpha_138,
    "alpha_139": alpha_139,
    "alpha_140": alpha_140,
    "alpha_141": alpha_141,
    "alpha_142": alpha_142,
    "alpha_143": alpha_143,
    "alpha_144": alpha_144,
    "alpha_145": alpha_145,
    "alpha_146": alpha_146,
    "alpha_147": alpha_147,
    "alpha_148": alpha_148,
    "alpha_149": alpha_149,
    "alpha_150": alpha_150,
 }
 def _build_phase5_formula_specs() -> dict[str, dict[str, Any]]:
    specs: dict[str, dict[str, Any]] = {}
    for alpha_id, function in _PHASE5_FORMULA_FUNCTIONS.items():
        meta = ALPHA158_REGISTRY[alpha_id]
        call_inputs = _phase3_call_inputs(function)
        formula_inputs = _phase3_string_list(meta, "inputs", alpha_id)
        input_category = _PHASE4_INPUT_CATEGORIES.get(len(call_inputs))
        if input_category is None:
            raise RuntimeError(f"unsupported formula input count for {alpha_id}")
        specs[alpha_id] = {
            "name": alpha_id,
            "contract_version": ALPHA158_PHASE5_FORMULA_CONTRACT_VERSION,
            "formula": meta["formula"],
            "category": meta["category"],
            "complexity": meta["complexity"],
            "parameters": _phase3_string_list(meta, "params", alpha_id),
            "description": meta["description"],
            "references": _phase3_string_list(meta, "references", alpha_id),
            "call_inputs": list(call_inputs),
            "formula_inputs": formula_inputs,
            "input_category": input_category,
        }
    return specs
 ALPHA158_PHASE5_FORMULA_SPECS: Mapping[str, Mapping[str, Any]] = (
    _freeze_phase3_formula_specs(_build_phase5_formula_specs())
 )
 def list_phase5_formulas() -> tuple[str, ...]:
    """Return the frozen alpha101-alpha150 formula IDs in stable order."""
    return tuple(ALPHA158_PHASE5_FORMULA_SPECS)
 def evaluate_phase5_formula(name: str, **inputs: pd.Series) -> pd.Series:
    """Evaluate a Phase 5 formula with an exact, alignment-safe input contract."""
    if name not in ALPHA158_PHASE5_FORMULA_SPECS:
        raise KeyError(f"formula {name!r} not registered")
    spec = ALPHA158_PHASE5_FORMULA_SPECS[name]
    required_inputs = cast(tuple[str, ...], spec["call_inputs"])
    missing_inputs = [field for field in required_inputs if field not in inputs]
    unexpected_inputs = sorted(field for field in inputs if field not in required_inputs)
    if missing_inputs or unexpected_inputs:
        details: list[str] = []
        if missing_inputs:
            details.append(f"missing inputs {missing_inputs}")
        if unexpected_inputs:
            details.append(f"unexpected inputs {unexpected_inputs}")
        raise ValueError(f"invalid inputs for {name}: {'; '.join(details)}")
    for field in required_inputs:
        if not isinstance(inputs[field], pd.Series):
            raise TypeError(f"{field} must be a pandas Series")
    primary_field = required_inputs[0]
    primary = inputs[primary_field]
    for field in required_inputs[1:]:
        if len(inputs[field]) != len(primary):
            raise ValueError(f"{field} length must match {primary_field}")
        if not primary.index.equals(inputs[field].index):
            raise ValueError(f"{field} index must align with {primary_field}")
    function = _PHASE5_FORMULA_FUNCTIONS[name]
    return function(*(inputs[field] for field in required_inputs))
 __all__ = [
    "rank",
    "delta",
@@ -3564,29 +2755,6 @@ __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",
    "ALPHA158_PHASE3_FORMULA_CONTRACT_VERSION",
    "ALPHA158_PHASE3_FORMULA_CATALOG_SHA256",
    "ALPHA158_PHASE3_FORMULA_SPECS",
    "list_phase3_formulas",
    "evaluate_phase3_formula",
    "ALPHA158_PHASE4_FORMULA_CONTRACT_VERSION",
    "ALPHA158_PHASE4_FORMULA_CATALOG_SHA256",
    "ALPHA158_PHASE4_FORMULA_SPECS",
    "list_phase4_formulas",
    "evaluate_phase4_formula",
    "ALPHA158_PHASE5_FORMULA_CONTRACT_VERSION",
    "ALPHA158_PHASE5_FORMULA_CATALOG_SHA256",
    "ALPHA158_PHASE5_FORMULA_SPECS",
    "list_phase5_formulas",
    "evaluate_phase5_formula",
    "alpha_001",
    "alpha_002",
    "alpha_003",
@@ -390,7 +390,6 @@ def _risk_trade_date(value: object) -> date:
 def _build_risk(
    result: FactorBacktestResult,
    run_id: str,
    data_snapshot_id: str,
    risk_snapshots: Mapping[object, CovarianceSnapshot] | None,
 ) -> pd.DataFrame:
    if risk_snapshots is None:
@@ -415,8 +414,6 @@ def _build_risk(
                f"covariance as_of_date {snapshot.as_of_date} must not be after trade date "
                f"{trade_date}"
            )
        if snapshot.data_snapshot_id != data_snapshot_id:
            raise ValueError("covariance snapshot data lineage differs from research run")
        normalized[trade_date] = snapshot
    weights_by_date = result.position_weights
@@ -575,6 +572,6 @@ def build_research_run_artifact(
        _positions=_build_positions(result, normalized_run_id),
        _attribution=attribution,
        _attribution_daily=attribution_daily,
-        _risk=_build_risk(result, normalized_run_id, normalized_snapshot, risk_snapshots),
+        _risk=_build_risk(result, normalized_run_id, risk_snapshots),
        _performance=_build_performance(result, normalized_run_id, benchmark_returns),
    )
@@ -9,22 +9,14 @@ ROOT = Path(__file__).resolve().parents[2]
 class CiContractTests(unittest.TestCase):
-    def test_ci_is_one_locked_shared_runtime_lite_gate(self) -> None:
+    def test_ci_is_one_dependency_free_lite_gate(self) -> None:
        workflow = (ROOT / ".gitea/workflows/ci.yml").read_text(encoding="utf-8")
        jobs = workflow.split("jobs:", 1)[1]
        self.assertEqual(re.findall(r"(?m)^  ([a-z][a-z0-9_-]*):\s*$", jobs), ["lite"])
        self.assertIn("actions/checkout@524e936cd9e579adf00e308bfdf971aebc7de09e", workflow)
        self.assertIn("persist-credentials: false", workflow)
-        self.assertIn("UV_PYTHON_DOWNLOADS: never", workflow)
+        self.assertIn("python3 tests/governance/test_module_spec.py", workflow)
-        self.assertIn('test "$(python3 --version)" = "Python 3.13.15"', workflow)
+        for forbidden in ("setup-python", "pip ", "curl ", "wget ", "docker pull"):
        self.assertIn(
            'test "$(uv --version | cut -d\' \' -f1-2)" = "uv 0.12.3"',
            workflow,
        )
        self.assertIn("uv sync --locked --extra dev", workflow)
        self.assertIn("uv run --locked --no-sync python", workflow)
        self.assertIn("tests/governance/test_ci_contract.py", workflow)
        for forbidden in ("setup-python", "setup-uv", "pip ", "curl ", "wget ", "docker pull"):
            self.assertNotIn(forbidden, workflow)
@@ -6,20 +6,8 @@ import numpy as np
 import pandas as pd
 import pytest
 import quant_engine.alpha_factors as alpha_factors_module
 from quant_engine.alpha_factors import (
    ALPHA158_REGISTRY,
    ALPHA158_PHASE1_OPERATOR_SPECS,
    ALPHA158_PHASE2_OPERATOR_SPECS,
    ALPHA158_PHASE3_FORMULA_CATALOG_SHA256,
    ALPHA158_PHASE3_FORMULA_CONTRACT_VERSION,
    ALPHA158_PHASE3_FORMULA_SPECS,
    ALPHA158_PHASE4_FORMULA_CATALOG_SHA256,
    ALPHA158_PHASE4_FORMULA_CONTRACT_VERSION,
    ALPHA158_PHASE4_FORMULA_SPECS,
    ALPHA158_PHASE5_FORMULA_CATALOG_SHA256,
    ALPHA158_PHASE5_FORMULA_CONTRACT_VERSION,
    ALPHA158_PHASE5_FORMULA_SPECS,
    alpha_001,
    alpha_002,
    alpha_003,
@@ -178,16 +166,6 @@ from quant_engine.alpha_factors import (
    alpha_156,
    alpha_157,
    alpha_158,
    evaluate_phase1_operator,
    evaluate_phase2_operator,
    evaluate_phase3_formula,
    evaluate_phase4_formula,
    evaluate_phase5_formula,
    list_phase1_operators,
    list_phase2_operators,
    list_phase3_formulas,
    list_phase4_formulas,
    list_phase5_formulas,
    correlation,
    covariance,
    decay_linear,
@@ -1254,711 +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)
 # ── 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)
 # ── Alpha158 Phase 3: versioned alpha001-alpha050 formula contract ──────────
 def _phase3_market_inputs() -> dict[str, pd.Series]:
    positions = np.arange(80, dtype=float)
    index = pd.RangeIndex(len(positions), name="row")
    open_ = pd.Series(100.0 + positions * 0.2 + np.sin(positions / 4.0), index=index)
    close = pd.Series(100.5 + positions * 0.18 + np.cos(positions / 5.0), index=index)
    high = pd.Series(np.maximum(open_, close) + 1.0, index=index)
    low = pd.Series(np.minimum(open_, close) - 1.0, index=index)
    volume = pd.Series(1_000.0 + positions**1.3 + 20.0 * np.sin(positions / 3.0), index=index)
    vwap = (open_ + close + high + low) / 4.0
    return {
        "open": open_,
        "close": close,
        "high": high,
        "low": low,
        "volume": volume,
        "vwap": vwap,
    }
 def test_phase3_formula_catalog_is_versioned_exact_and_content_addressed():
    import hashlib
    import json
    from collections import Counter
    expected_ids = tuple(f"alpha_{number:03d}" for number in range(1, 51))
    expected_fields = {
        "name",
        "contract_version",
        "formula",
        "category",
        "complexity",
        "parameters",
        "description",
        "references",
        "call_inputs",
        "formula_inputs",
        "input_category",
    }
    assert ALPHA158_PHASE3_FORMULA_CONTRACT_VERSION == "1.0.0"
    assert list_phase3_formulas() == expected_ids
    assert tuple(ALPHA158_PHASE3_FORMULA_SPECS) == expected_ids
    assert Counter(
        spec["input_category"] for spec in ALPHA158_PHASE3_FORMULA_SPECS.values()
    ) == {"single": 19, "pair": 26, "triple": 4, "quadruple": 1}
    for alpha_id, spec in ALPHA158_PHASE3_FORMULA_SPECS.items():
        assert set(spec) == expected_fields
        assert spec["name"] == alpha_id
        assert spec["contract_version"] == ALPHA158_PHASE3_FORMULA_CONTRACT_VERSION
        assert spec["formula"] == ALPHA158_REGISTRY[alpha_id]["formula"]
    serializable_specs = {
        alpha_id: {
            field: list(value) if isinstance(value, tuple) else value
            for field, value in spec.items()
        }
        for alpha_id, spec in ALPHA158_PHASE3_FORMULA_SPECS.items()
    }
    encoded = json.dumps(
        serializable_specs,
        sort_keys=True,
        separators=(",", ":"),
        ensure_ascii=False,
    ).encode()
    assert hashlib.sha256(encoded).hexdigest() == ALPHA158_PHASE3_FORMULA_CATALOG_SHA256
    assert ALPHA158_PHASE3_FORMULA_CATALOG_SHA256 == (
        "9a3360e5ee77a85a35d3c2fdab1efaa531fa0c263a2cb2bb5b965a1fb96fe1bd"
    )
 def test_phase3_formula_catalog_is_recursively_immutable():
    import operator
    with pytest.raises(TypeError):
        operator.setitem(ALPHA158_PHASE3_FORMULA_SPECS, "alpha_001", {})
    with pytest.raises(TypeError):
        operator.setitem(
            ALPHA158_PHASE3_FORMULA_SPECS["alpha_001"],
            "formula",
            "changed",
        )
    with pytest.raises(TypeError):
        operator.setitem(
            ALPHA158_PHASE3_FORMULA_SPECS["alpha_001"]["call_inputs"],
            0,
            "volume",
        )
 def test_phase3_catalog_freezes_callable_signatures_without_rewriting_formulas():
    import inspect
    legacy_formula_input_differences = {
        "alpha_011": ("close", "high", "low"),
        "alpha_035": ("volume",),
        "alpha_036": ("close",),
        "alpha_040": ("high", "low"),
        "alpha_042": ("close",),
        "alpha_043": ("volume",),
    }
    for alpha_id, spec in ALPHA158_PHASE3_FORMULA_SPECS.items():
        function = getattr(alpha_factors_module, alpha_id)
        signature_inputs = tuple(
            "open" if name == "open_" else name
            for name in inspect.signature(function).parameters
        )
        assert spec["call_inputs"] == signature_inputs
        assert spec["formula_inputs"] == legacy_formula_input_differences.get(
            alpha_id,
            signature_inputs,
        )
    assert ALPHA158_PHASE3_FORMULA_SPECS["alpha_035"]["call_inputs"] == (
        "close",
        "volume",
    )
    assert ALPHA158_REGISTRY["alpha_035"]["inputs"] == ["volume"]
 def test_phase3_dispatch_matches_all_existing_alpha001_alpha050_functions():
    inputs = _phase3_market_inputs()
    for alpha_id, spec in ALPHA158_PHASE3_FORMULA_SPECS.items():
        call_inputs = spec["call_inputs"]
        function = getattr(alpha_factors_module, alpha_id)
        expected = function(*(inputs[name] for name in call_inputs))
        actual = evaluate_phase3_formula(
            alpha_id,
            **{name: inputs[name] for name in reversed(call_inputs)},
        )
        pd.testing.assert_series_equal(actual, expected)
 def test_phase3_dispatch_rejects_unknown_missing_extra_and_non_series_inputs():
    inputs = _phase3_market_inputs()
    with pytest.raises(KeyError, match="not registered"):
        evaluate_phase3_formula("alpha_051", close=inputs["close"])
    with pytest.raises(ValueError, match=r"missing inputs.*volume"):
        evaluate_phase3_formula("alpha_005", close=inputs["close"])
    with pytest.raises(ValueError, match=r"unexpected inputs.*vwap"):
        evaluate_phase3_formula(
            "alpha_005",
            close=inputs["close"],
            volume=inputs["volume"],
            vwap=inputs["vwap"],
        )
    with pytest.raises(TypeError, match="close must be a pandas Series"):
        evaluate_phase3_formula(  # type: ignore[arg-type]
            "alpha_005",
            close=[1.0, 2.0],
            volume=inputs["volume"],
        )
 def test_phase3_dispatch_rejects_implicit_series_alignment():
    inputs = _phase3_market_inputs()
    misaligned_volume = inputs["volume"].rename(index={79: 80})
    with pytest.raises(ValueError, match="volume index must align with close"):
        evaluate_phase3_formula(
            "alpha_005",
            close=inputs["close"],
            volume=misaligned_volume,
        )
 # ── Alpha158 Phase 4: versioned alpha051-alpha100 formula contract ──────────
 def test_phase4_formula_catalog_is_versioned_exact_and_content_addressed():
    import hashlib
    import json
    from collections import Counter
    expected_ids = tuple(f"alpha_{number:03d}" for number in range(51, 101))
    expected_fields = {
        "name",
        "contract_version",
        "formula",
        "category",
        "complexity",
        "parameters",
        "description",
        "references",
        "call_inputs",
        "formula_inputs",
        "input_category",
    }
    assert ALPHA158_PHASE4_FORMULA_CONTRACT_VERSION == "1.0.0"
    assert list_phase4_formulas() == expected_ids
    assert tuple(ALPHA158_PHASE4_FORMULA_SPECS) == expected_ids
    assert Counter(
        spec["input_category"] for spec in ALPHA158_PHASE4_FORMULA_SPECS.values()
    ) == {"single": 1, "pair": 27, "triple": 11, "quadruple": 10, "quintuple": 1}
    for alpha_id, spec in ALPHA158_PHASE4_FORMULA_SPECS.items():
        assert set(spec) == expected_fields
        assert spec["name"] == alpha_id
        assert spec["contract_version"] == ALPHA158_PHASE4_FORMULA_CONTRACT_VERSION
        assert spec["formula"] == ALPHA158_REGISTRY[alpha_id]["formula"]
    serializable_specs = {
        alpha_id: {
            field: list(value) if isinstance(value, tuple) else value
            for field, value in spec.items()
        }
        for alpha_id, spec in ALPHA158_PHASE4_FORMULA_SPECS.items()
    }
    encoded = json.dumps(
        serializable_specs,
        sort_keys=True,
        separators=(",", ":"),
        ensure_ascii=False,
    ).encode()
    assert hashlib.sha256(encoded).hexdigest() == ALPHA158_PHASE4_FORMULA_CATALOG_SHA256
    assert ALPHA158_PHASE4_FORMULA_CATALOG_SHA256 == (
        "858daf5e2abab5063fc28fcf7c79936096e2c76dde582fc7bab78b3458f17054"
    )
 def test_phase4_formula_catalog_is_recursively_immutable():
    import operator
    with pytest.raises(TypeError):
        operator.setitem(ALPHA158_PHASE4_FORMULA_SPECS, "alpha_051", {})
    with pytest.raises(TypeError):
        operator.setitem(
            ALPHA158_PHASE4_FORMULA_SPECS["alpha_051"],
            "formula",
            "changed",
        )
    with pytest.raises(TypeError):
        operator.setitem(
            ALPHA158_PHASE4_FORMULA_SPECS["alpha_051"]["call_inputs"],
            0,
            "volume",
        )
 def test_phase4_catalog_freezes_callable_and_formula_inputs():
    import inspect
    for alpha_id, spec in ALPHA158_PHASE4_FORMULA_SPECS.items():
        function = getattr(alpha_factors_module, alpha_id)
        signature_inputs = tuple(
            "open" if name == "open_" else name
            for name in inspect.signature(function).parameters
        )
        assert spec["call_inputs"] == signature_inputs
        assert spec["formula_inputs"] == tuple(ALPHA158_REGISTRY[alpha_id]["inputs"])
    assert ALPHA158_PHASE4_FORMULA_SPECS["alpha_055"]["call_inputs"] == (
        "open",
        "high",
        "low",
        "volume",
        "close",
    )
 def test_phase4_dispatch_matches_all_existing_alpha051_alpha100_functions():
    inputs = _phase3_market_inputs()
    for alpha_id, spec in ALPHA158_PHASE4_FORMULA_SPECS.items():
        call_inputs = spec["call_inputs"]
        function = getattr(alpha_factors_module, alpha_id)
        expected = function(*(inputs[name] for name in call_inputs))
        actual = evaluate_phase4_formula(
            alpha_id,
            **{name: inputs[name] for name in reversed(call_inputs)},
        )
        pd.testing.assert_series_equal(actual, expected)
 def test_phase4_dispatch_rejects_unknown_missing_extra_and_non_series_inputs():
    inputs = _phase3_market_inputs()
    with pytest.raises(KeyError, match="not registered"):
        evaluate_phase4_formula("alpha_050", close=inputs["close"])
    with pytest.raises(ValueError, match=r"missing inputs.*low"):
        evaluate_phase4_formula("alpha_051", high=inputs["high"])
    with pytest.raises(ValueError, match=r"unexpected inputs.*vwap"):
        evaluate_phase4_formula(
            "alpha_051",
            high=inputs["high"],
            low=inputs["low"],
            vwap=inputs["vwap"],
        )
    with pytest.raises(TypeError, match="high must be a pandas Series"):
        evaluate_phase4_formula(  # type: ignore[arg-type]
            "alpha_051",
            high=[1.0, 2.0],
            low=inputs["low"],
        )
 def test_phase4_dispatch_rejects_implicit_series_alignment():
    inputs = _phase3_market_inputs()
    misaligned_low = inputs["low"].rename(index={79: 80})
    with pytest.raises(ValueError, match="low index must align with high"):
        evaluate_phase4_formula(
            "alpha_051",
            high=inputs["high"],
            low=misaligned_low,
        )
 # ── Alpha158 Phase 5: versioned alpha101-alpha150 formula contract ──────────
 def test_phase5_formula_catalog_is_versioned_exact_and_content_addressed():
    import hashlib
    import json
    from collections import Counter
    expected_ids = tuple(f"alpha_{number:03d}" for number in range(101, 151))
    expected_fields = {
        "name",
        "contract_version",
        "formula",
        "category",
        "complexity",
        "parameters",
        "description",
        "references",
        "call_inputs",
        "formula_inputs",
        "input_category",
    }
    assert ALPHA158_PHASE5_FORMULA_CONTRACT_VERSION == "1.0.0"
    assert list_phase5_formulas() == expected_ids
    assert tuple(ALPHA158_PHASE5_FORMULA_SPECS) == expected_ids
    assert Counter(
        spec["input_category"] for spec in ALPHA158_PHASE5_FORMULA_SPECS.values()
    ) == {"pair": 33, "triple": 14, "quadruple": 3}
    for alpha_id, spec in ALPHA158_PHASE5_FORMULA_SPECS.items():
        assert set(spec) == expected_fields
        assert spec["name"] == alpha_id
        assert spec["contract_version"] == ALPHA158_PHASE5_FORMULA_CONTRACT_VERSION
        assert spec["formula"] == ALPHA158_REGISTRY[alpha_id]["formula"]
    serializable_specs = {
        alpha_id: {
            field: list(value) if isinstance(value, tuple) else value
            for field, value in spec.items()
        }
        for alpha_id, spec in ALPHA158_PHASE5_FORMULA_SPECS.items()
    }
    encoded = json.dumps(
        serializable_specs,
        sort_keys=True,
        separators=(",", ":"),
        ensure_ascii=False,
    ).encode()
    assert hashlib.sha256(encoded).hexdigest() == ALPHA158_PHASE5_FORMULA_CATALOG_SHA256
    assert ALPHA158_PHASE5_FORMULA_CATALOG_SHA256 == (
        "3368796169c9fbd39c4a34ea137e569964b15882fbf4de25124790d548db6533"
    )
 def test_phase5_formula_catalog_is_recursively_immutable():
    import operator
    with pytest.raises(TypeError):
        operator.setitem(ALPHA158_PHASE5_FORMULA_SPECS, "alpha_101", {})
    with pytest.raises(TypeError):
        operator.setitem(
            ALPHA158_PHASE5_FORMULA_SPECS["alpha_101"],
            "formula",
            "changed",
        )
    with pytest.raises(TypeError):
        operator.setitem(
            ALPHA158_PHASE5_FORMULA_SPECS["alpha_101"]["call_inputs"],
            0,
            "volume",
        )
 def test_phase5_catalog_freezes_callable_and_formula_inputs():
    import inspect
    for alpha_id, spec in ALPHA158_PHASE5_FORMULA_SPECS.items():
        function = getattr(alpha_factors_module, alpha_id)
        signature_inputs = tuple(
            "open" if name == "open_" else name
            for name in inspect.signature(function).parameters
        )
        assert spec["call_inputs"] == signature_inputs
        assert spec["formula_inputs"] == tuple(ALPHA158_REGISTRY[alpha_id]["inputs"])
    assert ALPHA158_PHASE5_FORMULA_SPECS["alpha_101"]["call_inputs"] == (
        "close",
        "high",
        "low",
    )
 def test_phase5_dispatch_matches_all_existing_alpha101_alpha150_functions():
    inputs = _phase3_market_inputs()
    for alpha_id, spec in ALPHA158_PHASE5_FORMULA_SPECS.items():
        call_inputs = spec["call_inputs"]
        function = getattr(alpha_factors_module, alpha_id)
        expected = function(*(inputs[name] for name in call_inputs))
        actual = evaluate_phase5_formula(
            alpha_id,
            **{name: inputs[name] for name in reversed(call_inputs)},
        )
        pd.testing.assert_series_equal(actual, expected)
 def test_phase5_dispatch_rejects_unknown_missing_extra_and_non_series_inputs():
    inputs = _phase3_market_inputs()
    with pytest.raises(KeyError, match="not registered"):
        evaluate_phase5_formula("alpha_100", close=inputs["close"])
    with pytest.raises(KeyError, match="not registered"):
        evaluate_phase5_formula("alpha_151", close=inputs["close"])
    with pytest.raises(ValueError, match=r"missing inputs.*low"):
        evaluate_phase5_formula(
            "alpha_101",
            close=inputs["close"],
            high=inputs["high"],
        )
    with pytest.raises(ValueError, match=r"unexpected inputs.*vwap"):
        evaluate_phase5_formula(
            "alpha_101",
            close=inputs["close"],
            high=inputs["high"],
            low=inputs["low"],
            vwap=inputs["vwap"],
        )
    with pytest.raises(TypeError, match="high must be a pandas Series"):
        evaluate_phase5_formula(  # type: ignore[arg-type]
            "alpha_101",
            close=inputs["close"],
            high=[1.0, 2.0],
            low=inputs["low"],
        )
 def test_phase5_dispatch_rejects_length_and_index_alignment_errors():
    inputs = _phase3_market_inputs()
    shorter_low = inputs["low"].iloc[:-1]
    misaligned_high = inputs["high"].rename(index={79: 80})
    with pytest.raises(ValueError, match="low length must match close"):
        evaluate_phase5_formula(
            "alpha_101",
            close=inputs["close"],
            high=inputs["high"],
            low=shorter_low,
        )
    with pytest.raises(ValueError, match="high index must align with close"):
        evaluate_phase5_formula(
            "alpha_101",
            close=inputs["close"],
            high=misaligned_high,
            low=inputs["low"],
        )
 def test_phase5_contract_is_publicly_exported():
    assert {
        "ALPHA158_PHASE5_FORMULA_CONTRACT_VERSION",
        "ALPHA158_PHASE5_FORMULA_CATALOG_SHA256",
        "ALPHA158_PHASE5_FORMULA_SPECS",
        "list_phase5_formulas",
        "evaluate_phase5_formula",
    } <= set(alpha_factors_module.__all__)
@@ -164,7 +164,6 @@ def test_research_artifact_projects_annualized_risk_from_actual_positions() -> N
        covariance=covariance,
        return_frequency="1d",
        periods_per_year=252,
        data_snapshot_id="qtdb-pro-20260108-v1",
    )
    artifact = _build(result, risk_snapshots={trade_date: snapshot})
@@ -183,31 +182,6 @@ def test_research_artifact_projects_annualized_risk_from_actual_positions() -> N
    assert risk["risk_contribution"].sum() == pytest.approx(1.0)
 def test_research_artifact_rejects_risk_from_a_different_data_snapshot() -> None:
    result = _backtest_result()
    trade_date = result.position_weights.index[-1].date()
    covariance = pd.DataFrame(
        [[0.0001, 0.0], [0.0, 0.0004]],
        index=["A", "B"],
        columns=["A", "B"],
    )
    with pytest.raises(ValueError, match="data lineage differs"):
        _build(
            result,
            risk_snapshots={
                trade_date: CovarianceSnapshot(
                    snapshot_id="foreign-covariance",
                    as_of_date="2026-01-07",
                    covariance=covariance,
                    return_frequency="1d",
                    periods_per_year=252,
                    data_snapshot_id="different-market-snapshot",
                )
            },
        )
 def test_research_artifact_rejects_future_or_misaligned_risk_snapshots() -> None:
    result = _backtest_result()
    trade_date = result.position_weights.index[-1].date()
@@ -227,7 +201,6 @@ def test_research_artifact_rejects_future_or_misaligned_risk_snapshots() -> None
                    covariance=covariance,
                    return_frequency="1d",
                    periods_per_year=252,
                    data_snapshot_id="qtdb-pro-20260108-v1",
                )
            },
        )
@@ -242,7 +215,6 @@ def test_research_artifact_rejects_future_or_misaligned_risk_snapshots() -> None
                    covariance=covariance.loc[["B"], ["B"]],
                    return_frequency="1d",
                    periods_per_year=252,
                    data_snapshot_id="qtdb-pro-20260108-v1",
                )
            },
        )
@@ -1,408 +0,0 @@
 version = 1
 revision = 3
 requires-python = "==3.13.*"
 resolution-markers = [
    "sys_platform == 'win32'",
    "sys_platform == 'emscripten'",
    "sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
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Author	SHA1	Message	Date
ao gong	7c7f0c06a7	docs(handoff): record market lineage validation	2026-08-24 10:44:29 +08:00
ao gong	741d5f1ad3	feat(data): snapshot asset returns with stable lineage	2026-08-24 10:42:34 +08:00
ao gong	cbb56d2f52	docs: record covariance estimator decision	2026-08-21 23:49:06 +08:00
ao gong	524fc73852	feat: estimate deterministic covariance snapshots	2026-08-21 23:47:29 +08:00
ao gong	8ef8e3208e	test: define deterministic covariance estimator contract	2026-08-21 23:45:40 +08:00
ao gong	dc67f0e982	docs: record reproducible risk artifact contract	2026-08-21 23:41:21 +08:00
ao gong	1a60fef6e1	feat: publish reproducible portfolio risk facts	2026-08-21 23:28:34 +08:00
ao gong	a5dc04bf7e	test: define reproducible risk snapshot contract	2026-08-21 23:24:41 +08:00
ao gong	a3cefe364d	feat: add deterministic trade and signal identity	2026-08-21 22:32:37 +08:00
ao gong	d6c3614301	test: require deterministic trade identity	2026-08-21 22:32:14 +08:00
ao gong	8addce4a68	wip: hand off research artifact contract	2026-08-21 22:31:14 +08:00
ao gong	2071d508d0	test: cover sortino boundary contracts	2026-08-21 22:30:28 +08:00
ao gong	0568cabda1	feat: include signal facts in research artifact	2026-08-21 22:30:01 +08:00
ao gong	b2f3da9d66	test: require signal facts in research artifact	2026-08-21 22:29:32 +08:00
ao gong	cfa5bed188	feat: add deterministic research run artifact	2026-08-21 22:29:10 +08:00
ao gong	a9465e6479	test: define versioned research artifact contract	2026-08-21 22:26:51 +08:00
ao gong	55eeff3951	docs: add realized ledger weights to handoff	2026-08-21 22:22:00 +08:00
ao gong	3b1ad07c69	feat: expose realized ledger position weights	2026-08-21 22:21:32 +08:00
ao gong	1b5b353098	test: define realized ledger weight projection contract	2026-08-21 22:21:03 +08:00
ao gong	2bb9f52080	wip: hand off ledger-backed attribution	2026-08-21 22:19:20 +08:00
ao gong	76bb5494a2	docs: record lightweight attribution design references	2026-08-21 22:18:24 +08:00
ao gong	f7ad82534a	feat: add label-safe component risk decomposition	2026-08-21 22:17:05 +08:00
ao gong	35a52d781e	test: define labeled component risk contract	2026-08-21 22:16:24 +08:00
ao gong	f14ab464f7	feat: add strict benchmark-relative performance metrics	2026-08-21 22:15:47 +08:00
ao gong	de2f9494fc	test: define benchmark-relative performance contract	2026-08-21 22:15:05 +08:00
ao gong	19fe22b01a	feat: add ledger-backed daily return attribution	2026-08-21 22:14:18 +08:00
ao gong	212351e984	test: define post-execution return attribution contract	2026-08-21 22:13:04 +08:00
ao gong	5fb4b85cc3	wip: hand off stacked daily ledger	2026-08-21 22:06:51 +08:00
ao gong	a421278527	fix: align ledger performance with signal window	2026-08-21 22:05:28 +08:00
ao gong	c774a4546a	test: exclude factor warmup from performance window	2026-08-21 22:04:52 +08:00
ao gong	022b87fdac	feat: expose platform-neutral ledger projection	2026-08-21 22:03:21 +08:00
ao gong	1b39c53f18	test: define daily ledger projection contract	2026-08-21 22:02:58 +08:00
ao gong	b794ab2e8f	feat: add post-execution daily ledger	2026-08-21 22:01:35 +08:00
ao gong	a44e2d306a	test: define post-execution daily ledger contract	2026-08-21 21:58:01 +08:00
ao gong	15b283bdf9	docs: distinguish signal execution and holding times CI / lite (pull_request) Successful in 4s Details	2026-08-21 21:49:07 +08:00
ao gong	f9b7f2ab1a	feat: schedule factor weights for next-session execution	2026-08-21 21:47:53 +08:00
ao gong	213aa88deb	test: require execution-price input snapshot	2026-08-21 21:46:47 +08:00
ao gong	b7f77e2a6c	test: define lagged factor-to-execution contract	2026-08-21 21:46:00 +08:00
ao gong	c9fb5978f0	docs: clarify execution audit result contract CI / lite (pull_request) Successful in 3s Details	2026-08-21 21:39:12 +08:00
ao gong	b2af2a10a6	docs: document auditable execution workflow	2026-08-21 21:38:08 +08:00
ao gong	da2ca51ff7	fix: enforce cash-backed long-only rebalancing	2026-08-21 21:37:02 +08:00
ao gong	ee22d1eb9d	test: reproduce execution cash and long-only violations	2026-08-21 21:35:47 +08:00
ao gong	17b680604d	fix: derive multi-day trades from target-weight deltas	2026-08-21 21:35:15 +08:00
ao gong	c51d803daf	test: reproduce multi-day execution audit gaps	2026-08-21 21:31:52 +08:00
ao gong	5578851d85	fix: enforce chronological rebalance scores CI / lite (pull_request) Canceled after 0s Details	2026-08-21 21:25:27 +08:00
ao gong	b4f7b74c04	test: reject unsorted rebalance dates	2026-08-21 21:25:11 +08:00
ao gong	0a236622f3	docs: add factor-to-backtest portfolio workflow	2026-08-21 21:24:41 +08:00
ao gong	88af157ee7	fix: validate unique rebalance dates	2026-08-21 21:24:17 +08:00
ao gong	a1130ea43b	test: reject ambiguous duplicate rebalance dates	2026-08-21 21:24:03 +08:00
ao gong	96e8f1ad62	feat: build target weights from factor scores	2026-08-21 21:23:25 +08:00
ao gong	ecf6b4e4bf	test: add red factor-to-weights portfolio contract	2026-08-21 21:22:36 +08:00
ao gong	979166ad16	docs: document unified backtest result workflow CI / lite (pull_request) Successful in 3s Details	2026-08-21 21:11:52 +08:00
ao gong	cdf41edf1f	feat: add unified weight backtest result facade	2026-08-21 21:11:06 +08:00
ao gong	585a635797	test: add red contract for unified backtest result	2026-08-21 21:10:33 +08:00
ao gong	2ff0630973	refactor: clarify quant core validation internals CI / lite (pull_request) Successful in 3s Details	2026-08-21 21:00:19 +08:00
ao gong	bae4dedf70	test: satisfy metric contract lint	2026-08-21 20:59:27 +08:00
ao gong	e359792ef5	fix: harden quant core calculation boundaries	2026-08-21 20:58:30 +08:00
ao gong	0c3a375b1e	test: add red contracts for quant core boundaries	2026-08-21 20:57:49 +08:00