feat(governance): add risk-gated paper research slice

.gitea/workflows/ci.yml

@@ -16,6 +16,8 @@ permissions:
 jobs:
   lite:
     runs-on: ubuntu-latest
+    env:
+      UV_PYTHON_DOWNLOADS: never
     steps:
       - uses: actions/checkout@524e936cd9e579adf00e308bfdf971aebc7de09e
         with:
@@ -26,7 +28,18 @@ 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: python3 tests/governance/test_module_spec.py
+        run: |
+          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 python3 -m py_compile
+        run: git ls-files -z '*.py' | xargs -0 uv run --locked --no-sync python -m py_compile

.python-version

@@ -0,0 +1 @@
+3.13

README.md

@@ -25,6 +25,7 @@
 - `backtest` — weight-based 多日仿真（rebalance_table / compute_nav / compare_to_benchmark）
 - `portfolio_construction` — 多期因子分数 → Top-K → 等权目标权重表
 - `research_pipeline` — 因子日 → 下一真实交易日 → 显式执行价 → 日末估值 → 成本后绩效（防前视编排）
+- `governed_pipeline` — 数据快照 → 因子版本 → 策略版本 → 回测运行 → 目标组合 → 风险决策 → Paper 订单意图；全链路带确定性 ID，风险拒绝时禁止生成订单意图
 - `artifact` — 版本化、确定性、存储中立的完整 research run 事实表与 manifest
 - `attribution` — 基于实际成交后持仓的隔夜 / 日内 / 交易成本逐日收益归因与闭合审计
 - `metrics` — 绝对绩效 + 严格日期对齐的 TE / IR / alpha / beta 基准相对绩效
@@ -55,6 +56,9 @@ pytest                    # 单元测试
 pytest --cov=src          # 覆盖率
 mypy --strict src/        # 类型检查
 ruff check src/ tests/    # lint
+
+# 无网络、无数据库、无券商的架构烟测
+uv run python -m quant_engine.governed_pipeline
 ```
 
 ## 使用
@@ -139,15 +143,22 @@ print(factor_backtest.benchmark_stats(benchmark_returns))
 
 # 下游稳定交付：显式提供代码版本、数据快照和时区，不在核心层写数据库。
 from quant_engine.artifact import build_research_run_artifact
+from quant_engine.data_adapter import prepare_asset_return_snapshot
 from quant_engine.risk import estimate_covariance_snapshot
 
 risk_date = factor_backtest.position_weights.index[-1].date()
+market_snapshot = prepare_asset_return_snapshot(
+    qtdb_daily_long,
+    source="qtdb_pro.hq_daily",
+    source_snapshot_id="<upstream-ingestion-snapshot-id>",
+    adjustment="qfq",
+)
 risk_snapshot = estimate_covariance_snapshot(
-    daily_asset_returns,
+    market_snapshot.returns,
     as_of_date=risk_date,
     lookback_sessions=252,
     min_observations=120,
-    data_snapshot_id="<risk-return-data-snapshot-id>",
+    data_snapshot_id=market_snapshot.data_snapshot_id,
 )
 
 artifact = build_research_run_artifact(
@@ -158,7 +169,7 @@ artifact = build_research_run_artifact(
     strategy_version="1.0.0",
     engine_version="1.2.0",
     code_revision="<git-sha>",
-    data_snapshot_id="<data-snapshot-id>",
+    data_snapshot_id=market_snapshot.data_snapshot_id,
     calendar="CN-A",
     timezone="Asia/Shanghai",
     started_at="2026-08-21T10:00:00+08:00",
@@ -184,6 +195,23 @@ print(backtest.stats())
 print(backtest.benchmark_report())
 ```
 
+## 治理垂直切片
+
+`governed_pipeline` 不复制因子、回测、组合或执行算法，只编排现有能力并补充版本与风险契约。
+调用方必须显式提供 `DatasetSnapshot`、`FactorVersion`、`StrategyVersion`、代码提交和
+`RiskPolicy`。模块只会生成 `environment="paper"` 的订单意图，不连接数据库、数据供应商或
+券商；风险决策为拒绝时，订单意图固定为空，直接调用创建函数也会失败关闭。
+
+该切片对应 ResearchHub 架构的首个可执行验收链路：
+
+```text
+DatasetSnapshot → FactorVersion → StrategyVersion → BacktestRun
+→ PortfolioTarget → RiskDecision → PaperOrderIntent
+```
+
+平台总架构、五仓职责和十二层能力映射仍以 `research_platform/docs/architecture/` 为权威；
+本仓只拥有纯计算与离线模拟合同。
+
 ## 与 research_results 的关系
 
 `research_results` 依赖 `quant_engine`（通过 re-export 保持向后兼容）：

ci-profile.yml

@@ -0,0 +1,9 @@
+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

docs/OPEN_SOURCE_REFERENCES.md

@@ -46,6 +46,16 @@ component risk 闭合到年化组合波动，percentage contribution 闭合到 1
 pairwise 样本集合产生含义不一致的矩阵。snapshot ID 对窗口数据、缺失掩码、上游数据
 快照身份和估计参数做 SHA-256，追加未来数据不会改变历史快照。
 
+市场适配层现以 `AssetReturnSnapshot` 固化 simple-return 输入：上游 ingestion snapshot ID、
+数据源、价格字段、复权口径、规范化价格值和缺失掩码共同形成内容寻址 ID；不前向填充
+停牌/缺失价格。该 ID 同时传入协方差快照和研究运行工件，避免同一研究链出现两套数据
+身份。
+
+可选 shrinkage adapter 的评估结论是“保留边界，暂不实现”：当前运行依赖没有声明
+scikit-learn，本切片也不修改版本或锁文件。未来只有在依赖治理接受后，才以延迟导入
+直接调用 scikit-learn 的 `LedoitWolf` / `OAS`，并让估计器名称、库版本与参数进入
+snapshot identity；不复制成熟统计公式，也不让环境中偶然存在的包改变 baseline 行为。
+
 ## hikyuu 的定位
 
 [hikyuu](https://github.com/fasiondog/hikyuu) 的 SG / MM / CN / PG 部件化思想、

docs/handoff/2026-08-21-research-artifact-contract.md

@@ -24,12 +24,14 @@
 
 ## Verification
 
-- `pytest -q`: 529 passed，9 个既有 SciPy warning；
-- risk/artifact focused coverage 87.85%；
+- `pytest -q`: 540 passed，9 个既有 SciPy warning；
+- data-adapter focused coverage 77%（包含未连接真实 ClickHouse 的 I/O 便捷函数）；
 - `mypy --strict src/`: 16 source files passed；
 - changed-scope Ruff: passed；
 - no runtime dependency added；
 - no database, network, broker or filesystem write side effect in artifact builder。
+- 三仓隔离 ClickHouse 黄金链路通过：市场价格 → return snapshot → covariance → artifact →
+  publisher → reader；使用随机 localhost 端口、tmpfs 和自动容器清理。
 
 ## Current risk contract
 
@@ -40,9 +42,16 @@
 - `covariance_as_of_date` 不得晚于 `trade_date`；无正组合方差时拒绝产物。
 - `estimate_covariance_snapshot` 从显式数据快照的日收益生成无前视、complete-case、
   SHA-256 可复现的 per-period sample covariance；不包含 I/O 或未来行。
+- `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。
 
 ## Next action
 
 保持 Draft PR #5，不绕过堆叠顺序合并；下游 `research_results` / `research_platform`
-已在各自 Draft 分支兼容 1.0.0 / 1.1.0，下一阶段让市场数据适配器提供带稳定
-`data_snapshot_id` 的资产日收益，再评估可选 scikit-learn shrinkage adapter。
+继续在现有 Draft 分支消费同一数据 lineage。下一阶段优先把 ingestion snapshot ID 从
+真实 ELT 元数据接入调用方，再在依赖治理通过后单独交付可选 shrinkage adapter。

pyproject.toml

@@ -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 = ">=3.11"
+requires-python = ">=3.13,<3.14"
 license = { text = "MIT" }
 authors = [
     { name = "researchhub team" },
@@ -39,7 +39,7 @@ where = ["src"]
 
 [tool.ruff]
 line-length = 100
-target-version = "py311"
+target-version = "py313"
 
 [tool.ruff.lint]
 select = ["E", "F", "W", "I", "N", "UP", "B", "A", "C4", "PT", "RUF"]
@@ -54,10 +54,13 @@ ignore = [
 ]
 
 [tool.mypy]
-python_version = "3.11"
+python_version = "3.13"
 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"

src/quant_engine/alpha_factors.py

@@ -15,7 +15,9 @@ v1.2.0 Phase 0：5 个基础算子 + 5 个 alpha 公式（alpha001–alpha005）
 
 from __future__ import annotations
 
-from typing import Any
+from collections.abc import Callable, Mapping
+from types import MappingProxyType
+from typing import Any, cast
 
 import numpy as np
 import pandas as pd
@@ -209,6 +211,367 @@ 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） ─────────────────────────
 
 
@@ -2730,6 +3093,541 @@ 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))
+
+
+# ── Phase 6 formula contract: frozen alpha151-alpha158 surface ──────────────
+
+# Phase 6 completes the versioned formula contract without mutating any
+# earlier catalogue, digest, dispatch surface, or formula implementation.
+ALPHA158_PHASE6_FORMULA_CONTRACT_VERSION = "1.0.0"
+ALPHA158_PHASE6_FORMULA_CATALOG_SHA256 = (
+    "70ffae16ca6cbb59a1e8644f5cdeec1d291fa75ff8b5647fb534af0083408219"
+)
+
+_PHASE6_FORMULA_FUNCTIONS: dict[str, Callable[..., pd.Series]] = {
+    "alpha_151": alpha_151,
+    "alpha_152": alpha_152,
+    "alpha_153": alpha_153,
+    "alpha_154": alpha_154,
+    "alpha_155": alpha_155,
+    "alpha_156": alpha_156,
+    "alpha_157": alpha_157,
+    "alpha_158": alpha_158,
+}
+
+
+def _build_phase6_formula_specs() -> dict[str, dict[str, Any]]:
+    specs: dict[str, dict[str, Any]] = {}
+    for alpha_id, function in _PHASE6_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_PHASE6_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_PHASE6_FORMULA_SPECS: Mapping[str, Mapping[str, Any]] = (
+    _freeze_phase3_formula_specs(_build_phase6_formula_specs())
+)
+
+
+def list_phase6_formulas() -> tuple[str, ...]:
+    """Return the frozen alpha151-alpha158 formula IDs in stable order."""
+    return tuple(ALPHA158_PHASE6_FORMULA_SPECS)
+
+
+def evaluate_phase6_formula(name: str, **inputs: pd.Series) -> pd.Series:
+    """Evaluate a Phase 6 formula with an exact, alignment-safe input contract."""
+    if name not in ALPHA158_PHASE6_FORMULA_SPECS:
+        raise KeyError(f"formula {name!r} not registered")
+
+    spec = ALPHA158_PHASE6_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 = _PHASE6_FORMULA_FUNCTIONS[name]
+    return function(*(inputs[field] for field in required_inputs))
+
+
 __all__ = [
     "rank",
     "delta",
@@ -2755,6 +3653,34 @@ __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",
+    "ALPHA158_PHASE6_FORMULA_CONTRACT_VERSION",
+    "ALPHA158_PHASE6_FORMULA_CATALOG_SHA256",
+    "ALPHA158_PHASE6_FORMULA_SPECS",
+    "list_phase6_formulas",
+    "evaluate_phase6_formula",
     "alpha_001",
     "alpha_002",
     "alpha_003",

src/quant_engine/artifact.py

@@ -390,6 +390,7 @@ 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:
@@ -414,6 +415,8 @@ 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
@@ -572,6 +575,6 @@ 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, risk_snapshots),
+        _risk=_build_risk(result, normalized_run_id, normalized_snapshot, risk_snapshots),
         _performance=_build_performance(result, normalized_run_id, benchmark_returns),
     )

src/quant_engine/data_adapter.py

@@ -6,22 +6,27 @@
 - execution.py 需要**宽表**（date × stock_code）prices / volumes
 - Tushare 字段命名：`ts_code / vol(手) / amount(千元) / pct_chg`，且**无 vwap 字段**
 
-本模块提供 6 个纯函数，让新模块直接吃 qtdb_pro 真实数据：
+本模块提供可组合的数据适配函数，让新模块直接吃 qtdb_pro 真实数据：
 1. `long_to_wide()` — 长表 → 宽表（date × stock_code）
 2. `wide_to_long()` — 宽表 → 长表
 3. `rename_tushare_columns()` — 列名映射（ts_code→stock_code, vol→volume 等）
 4. `add_vwap_proxy()` — vwap 代理（Tushare 无 vwap 字段）
 5. `apply_adj_factor()` — 复权（hq_daily × hq_adj_factor 前复权）
 6. `prepare_stock_series()` — 单股提取（alpha_factors 输入）
-7. `prepare_execution_inputs()` — execution 输入（prices + volumes 宽表）
-8. `load_qtdb_daily()` — 便捷加载（qtdb_pro.hq_daily + 可选复权）
+7. `prepare_asset_return_snapshot()` — 带稳定 lineage 的资产日收益
+8. `prepare_execution_inputs()` — execution 输入（prices + volumes 宽表）
+9. `load_qtdb_daily()` — 便捷加载（qtdb_pro.hq_daily + 可选复权）
 
 全部纯 pandas/numpy，零新依赖，mypy strict 兼容。
 """
 
 from __future__ import annotations
 
+import hashlib
+import json
 from collections.abc import Mapping, Sequence
+from dataclasses import dataclass
+from datetime import date
 from typing import Any
 
 import numpy as np
@@ -32,12 +37,14 @@ from quant_engine.logging import get_logger
 logger = get_logger(__name__)
 
 __all__ = [
+    "AssetReturnSnapshot",
     "long_to_wide",
     "wide_to_long",
     "rename_tushare_columns",
     "add_vwap_proxy",
     "apply_adj_factor",
     "prepare_stock_series",
+    "prepare_asset_return_snapshot",
     "prepare_execution_inputs",
     "load_qtdb_daily",
 ]
@@ -57,6 +64,107 @@ TUSHARE_RENAME: dict[str, str] = {
 }
 
 
+@dataclass(frozen=True, slots=True, init=False, eq=False)
+class AssetReturnSnapshot:
+    """Immutable-by-interface daily return matrix with reproducible lineage."""
+
+    data_snapshot_id: str
+    source: str
+    source_snapshot_id: str
+    price_field: str
+    adjustment: str
+    return_method: str
+    start_date: date
+    end_date: date
+    sessions: int
+    assets: tuple[str, ...]
+    _returns: pd.DataFrame
+
+    def __init__(
+        self,
+        *,
+        data_snapshot_id: str,
+        source: str,
+        source_snapshot_id: str,
+        price_field: str,
+        adjustment: str,
+        return_method: str,
+        start_date: date,
+        end_date: date,
+        assets: tuple[str, ...],
+        returns: pd.DataFrame,
+    ) -> None:
+        for value, name in (
+            (data_snapshot_id, "data_snapshot_id"),
+            (source, "source"),
+            (source_snapshot_id, "source_snapshot_id"),
+            (price_field, "price_field"),
+            (adjustment, "adjustment"),
+            (return_method, "return_method"),
+        ):
+            if not isinstance(value, str) or not value.strip():
+                raise ValueError(f"{name} must be non-empty")
+        if returns.empty or not isinstance(returns.index, pd.DatetimeIndex):
+            raise ValueError("returns must contain a DatetimeIndex and at least one session")
+        if tuple(returns.columns) != assets:
+            raise ValueError("assets must match returns columns")
+        if start_date > end_date:
+            raise ValueError("start_date must not be after end_date")
+
+        object.__setattr__(self, "data_snapshot_id", data_snapshot_id.strip())
+        object.__setattr__(self, "source", source.strip())
+        object.__setattr__(self, "source_snapshot_id", source_snapshot_id.strip())
+        object.__setattr__(self, "price_field", price_field.strip())
+        object.__setattr__(self, "adjustment", adjustment.strip())
+        object.__setattr__(self, "return_method", return_method.strip())
+        object.__setattr__(self, "start_date", start_date)
+        object.__setattr__(self, "end_date", end_date)
+        object.__setattr__(self, "sessions", len(returns))
+        object.__setattr__(self, "assets", assets)
+        object.__setattr__(self, "_returns", returns.copy(deep=True))
+
+    @property
+    def returns(self) -> pd.DataFrame:
+        """Return an isolated copy so callers cannot mutate the snapshot."""
+        return self._returns.copy(deep=True)
+
+
+def _non_empty(value: str, name: str) -> str:
+    if not isinstance(value, str) or not value.strip():
+        raise ValueError(f"{name} must be non-empty")
+    return value.strip()
+
+
+def _asset_return_snapshot_id(
+    prices: pd.DataFrame,
+    *,
+    source: str,
+    source_snapshot_id: str,
+    price_field: str,
+    adjustment: str,
+) -> str:
+    values = prices.to_numpy(dtype=float, copy=True)
+    missing = np.isnan(values)
+    normalized = np.where(missing, 0.0, values).astype("<f8", copy=False)
+    metadata = {
+        "adjustment": adjustment,
+        "assets": [str(asset) for asset in prices.columns],
+        "price_field": price_field,
+        "return_method": "simple",
+        "schema": "asset-returns-v1",
+        "sessions": [timestamp.date().isoformat() for timestamp in prices.index],
+        "shape": list(values.shape),
+        "source": source,
+        "source_snapshot_id": source_snapshot_id,
+    }
+    digest = hashlib.sha256(
+        json.dumps(metadata, sort_keys=True, separators=(",", ":")).encode("utf-8")
+    )
+    digest.update(missing.astype(np.uint8, copy=False).tobytes(order="C"))
+    digest.update(normalized.tobytes(order="C"))
+    return f"asset-returns-v1:{digest.hexdigest()}"
+
+
 def long_to_wide(
     df: pd.DataFrame,
     value_col: str = "close",
@@ -283,6 +391,98 @@ def prepare_stock_series(
     return series_map
 
 
+def prepare_asset_return_snapshot(
+    df: pd.DataFrame,
+    *,
+    source: str,
+    source_snapshot_id: str,
+    price_col: str = "close",
+    adjustment: str = "none",
+    stock_col: str = "stock_code",
+    date_col: str = "trade_date",
+) -> AssetReturnSnapshot:
+    """Build deterministic simple daily returns from a long market-price table.
+
+    ``source_snapshot_id`` must identify the upstream ingestion snapshot. The
+    resulting ID additionally fingerprints canonical price values and their
+    missing mask, so changed contents cannot retain the same downstream identity.
+    Missing prices are never forward-filled.
+    """
+    normalized_source = _non_empty(source, "source")
+    normalized_source_snapshot_id = _non_empty(
+        source_snapshot_id,
+        "source_snapshot_id",
+    )
+    normalized_price_col = _non_empty(price_col, "price_col")
+    normalized_adjustment = _non_empty(adjustment, "adjustment")
+    if not isinstance(df, pd.DataFrame):
+        raise TypeError("df must be a pandas DataFrame")
+    if df.empty:
+        raise ValueError("df must contain market prices")
+    required = {date_col, stock_col, normalized_price_col}
+    missing_columns = sorted(required.difference(df.columns))
+    if missing_columns:
+        raise ValueError(f"prepare_asset_return_snapshot: missing columns={missing_columns}")
+
+    market = df[[date_col, stock_col, normalized_price_col]].copy()
+    if any(not isinstance(asset, str) or not asset.strip() for asset in market[stock_col]):
+        raise ValueError("asset labels must be non-empty strings")
+    market[stock_col] = market[stock_col].str.strip()
+    try:
+        normalized_dates = pd.to_datetime(market[date_col], errors="raise")
+    except (TypeError, ValueError) as error:
+        raise ValueError("trade dates must be valid dates") from error
+    if normalized_dates.isna().any():
+        raise ValueError("trade dates must be valid dates")
+    market[date_col] = normalized_dates.dt.normalize()
+    if market.duplicated(subset=[date_col, stock_col]).any():
+        raise ValueError("duplicate asset/session prices are not allowed")
+
+    try:
+        market[normalized_price_col] = pd.to_numeric(
+            market[normalized_price_col],
+            errors="raise",
+        )
+    except (TypeError, ValueError) as error:
+        raise ValueError("prices must be numeric") from error
+    observed_prices = market[normalized_price_col].dropna().to_numpy(dtype=float)
+    if observed_prices.size == 0 or not np.isfinite(observed_prices).all():
+        raise ValueError("prices must contain positive finite observations")
+    if (observed_prices <= 0.0).any():
+        raise ValueError("prices must contain positive finite observations")
+
+    prices = market.pivot(
+        index=date_col,
+        columns=stock_col,
+        values=normalized_price_col,
+    ).sort_index()
+    prices = prices.reindex(sorted(str(asset) for asset in prices.columns), axis="columns")
+    prices = prices.astype(float)
+    if len(prices) < 2:
+        raise ValueError("market prices must contain at least two sessions")
+    returns = prices.pct_change(fill_method=None)
+    assets = tuple(str(asset) for asset in prices.columns)
+    snapshot_id = _asset_return_snapshot_id(
+        prices,
+        source=normalized_source,
+        source_snapshot_id=normalized_source_snapshot_id,
+        price_field=normalized_price_col,
+        adjustment=normalized_adjustment,
+    )
+    return AssetReturnSnapshot(
+        data_snapshot_id=snapshot_id,
+        source=normalized_source,
+        source_snapshot_id=normalized_source_snapshot_id,
+        price_field=normalized_price_col,
+        adjustment=normalized_adjustment,
+        return_method="simple",
+        start_date=prices.index[0].date(),
+        end_date=prices.index[-1].date(),
+        assets=assets,
+        returns=returns,
+    )
+
+
 def prepare_execution_inputs(
     df: pd.DataFrame,
     stock_col: str = "stock_code",

src/quant_engine/governed_pipeline.py

@@ -0,0 +1,540 @@
+"""Versioned, risk-gated, paper-only quantitative research vertical slice.
+
+The module composes existing ``quant_engine`` calculations into a small set of
+storage-neutral governance contracts.  It never reads a database, calls a data
+vendor, loads credentials, or routes an order to a broker.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import math
+import re
+from collections.abc import Mapping
+from dataclasses import asdict, dataclass
+from datetime import UTC, datetime
+from enum import StrEnum
+from types import MappingProxyType
+
+import pandas as pd
+
+from quant_engine.execution import ExecutionConfig
+from quant_engine.research_pipeline import FactorBacktestResult, run_factor_backtest_research
+
+_SHA256 = re.compile(r"^[0-9a-f]{64}$")
+_GIT_SHA = re.compile(r"^[0-9a-f]{40}$")
+
+__all__ = [
+    "DatasetSnapshot",
+    "FactorVersion",
+    "StrategyStage",
+    "StrategyVersion",
+    "BacktestRun",
+    "PortfolioTarget",
+    "RiskPolicy",
+    "RiskDecisionStatus",
+    "RiskDecision",
+    "PaperOrderIntent",
+    "GovernedFactorSliceResult",
+    "evaluate_portfolio_risk",
+    "create_paper_order_intent",
+    "run_governed_factor_slice",
+]
+
+
+def _required_text(value: str, name: str) -> str:
+    normalized = value.strip()
+    if not normalized:
+        raise ValueError(f"{name} must be non-empty")
+    return normalized
+
+
+def _aware_utc(value: datetime, name: str) -> datetime:
+    if not isinstance(value, datetime) or value.tzinfo is None or value.utcoffset() is None:
+        raise ValueError(f"{name} must be timezone-aware")
+    return value.astimezone(UTC)
+
+
+def _canonical_value(value: object) -> object:
+    if value is None or isinstance(value, str | bool | int):
+        return value
+    if isinstance(value, float):
+        if math.isnan(value):
+            return "NaN"
+        if math.isinf(value):
+            return "Infinity" if value > 0 else "-Infinity"
+        return value
+    if isinstance(value, datetime):
+        return _aware_utc(value, "datetime").isoformat()
+    if isinstance(value, StrEnum):
+        return value.value
+    if isinstance(value, Mapping):
+        return {
+            str(key): _canonical_value(item)
+            for key, item in sorted(value.items(), key=lambda pair: str(pair[0]))
+        }
+    if isinstance(value, list | tuple):
+        return [_canonical_value(item) for item in value]
+    raise TypeError(f"unsupported canonical value: {type(value).__name__}")
+
+
+def _stable_id(prefix: str, payload: Mapping[str, object]) -> str:
+    encoded = json.dumps(
+        _canonical_value(payload),
+        ensure_ascii=False,
+        sort_keys=True,
+        separators=(",", ":"),
+        allow_nan=False,
+    ).encode("utf-8")
+    return f"{prefix}:{hashlib.sha256(encoded).hexdigest()}"
+
+
+def _immutable_weights(values: Mapping[str, float]) -> Mapping[str, float]:
+    normalized: dict[str, float] = {}
+    for raw_asset, raw_weight in values.items():
+        asset = _required_text(str(raw_asset), "asset_id")
+        weight = float(raw_weight)
+        if not math.isfinite(weight) or weight < 0:
+            raise ValueError("portfolio weights must be finite and non-negative")
+        if asset in normalized:
+            raise ValueError(f"duplicate asset_id: {asset}")
+        normalized[asset] = weight
+    return MappingProxyType(dict(sorted(normalized.items())))
+
+
+@dataclass(frozen=True, slots=True)
+class DatasetSnapshot:
+    """Point-in-time identity for caller-supplied research data."""
+
+    snapshot_id: str
+    schema_version: str
+    content_sha256: str
+    effective_at: datetime
+    available_at: datetime
+    ingested_at: datetime
+
+    def __post_init__(self) -> None:
+        object.__setattr__(self, "snapshot_id", _required_text(self.snapshot_id, "snapshot_id"))
+        object.__setattr__(
+            self, "schema_version", _required_text(self.schema_version, "schema_version")
+        )
+        digest = self.content_sha256.strip().lower()
+        if not _SHA256.fullmatch(digest):
+            raise ValueError("content_sha256 must be a lowercase SHA-256 digest")
+        object.__setattr__(self, "content_sha256", digest)
+        effective_at = _aware_utc(self.effective_at, "effective_at")
+        available_at = _aware_utc(self.available_at, "available_at")
+        ingested_at = _aware_utc(self.ingested_at, "ingested_at")
+        if not effective_at <= available_at <= ingested_at:
+            raise ValueError("timestamps must satisfy effective_at <= available_at <= ingested_at")
+        object.__setattr__(self, "effective_at", effective_at)
+        object.__setattr__(self, "available_at", available_at)
+        object.__setattr__(self, "ingested_at", ingested_at)
+
+
+@dataclass(frozen=True, slots=True)
+class FactorVersion:
+    """Versioned factor definition identity without factor implementation duplication."""
+
+    factor_id: str
+    version: str
+    definition_sha256: str
+    dataset_schema_version: str
+
+    def __post_init__(self) -> None:
+        object.__setattr__(self, "factor_id", _required_text(self.factor_id, "factor_id"))
+        object.__setattr__(self, "version", _required_text(self.version, "version"))
+        object.__setattr__(
+            self,
+            "dataset_schema_version",
+            _required_text(self.dataset_schema_version, "dataset_schema_version"),
+        )
+        digest = self.definition_sha256.strip().lower()
+        if not _SHA256.fullmatch(digest):
+            raise ValueError("definition_sha256 must be a lowercase SHA-256 digest")
+        object.__setattr__(self, "definition_sha256", digest)
+
+    @property
+    def version_id(self) -> str:
+        return f"{self.factor_id}@{self.version}"
+
+
+class StrategyStage(StrEnum):
+    DRAFT = "Draft"
+    RESEARCH = "Research"
+    VALIDATED = "Validated"
+    APPROVED = "Approved"
+    PAPER = "Paper"
+    LIVE = "Live"
+    PAUSED = "Paused"
+    RETIRED = "Retired"
+
+
+@dataclass(frozen=True, slots=True)
+class StrategyVersion:
+    """Strategy lineage and lifecycle state used by the governed slice."""
+
+    strategy_id: str
+    version: str
+    factor_version_id: str
+    stage: StrategyStage
+
+    def __post_init__(self) -> None:
+        object.__setattr__(self, "strategy_id", _required_text(self.strategy_id, "strategy_id"))
+        object.__setattr__(self, "version", _required_text(self.version, "version"))
+        object.__setattr__(
+            self,
+            "factor_version_id",
+            _required_text(self.factor_version_id, "factor_version_id"),
+        )
+        if not isinstance(self.stage, StrategyStage):
+            raise TypeError("stage must be a StrategyStage")
+
+    @property
+    def version_id(self) -> str:
+        return f"{self.strategy_id}@{self.version}"
+
+
+@dataclass(frozen=True, slots=True)
+class BacktestRun:
+    run_id: str
+    dataset_snapshot_id: str
+    factor_version_id: str
+    strategy_version_id: str
+    code_revision: str
+    config_hash: str
+    created_at: datetime
+
+
+@dataclass(frozen=True, slots=True)
+class PortfolioTarget:
+    target_id: str
+    backtest_run_id: str
+    dataset_snapshot_id: str
+    weights: Mapping[str, float]
+    created_at: datetime
+
+    def __post_init__(self) -> None:
+        object.__setattr__(self, "weights", _immutable_weights(self.weights))
+
+    @property
+    def gross_exposure(self) -> float:
+        return float(sum(abs(weight) for weight in self.weights.values()))
+
+
+@dataclass(frozen=True, slots=True)
+class RiskPolicy:
+    policy_id: str
+    max_gross_exposure: float
+    max_single_asset_weight: float
+    max_positions: int
+
+    def __post_init__(self) -> None:
+        object.__setattr__(self, "policy_id", _required_text(self.policy_id, "policy_id"))
+        if not math.isfinite(self.max_gross_exposure) or self.max_gross_exposure <= 0:
+            raise ValueError("max_gross_exposure must be positive and finite")
+        if not math.isfinite(self.max_single_asset_weight) or not (
+            0 < self.max_single_asset_weight <= 1
+        ):
+            raise ValueError("max_single_asset_weight must be in (0, 1]")
+        if (
+            isinstance(self.max_positions, bool)
+            or not isinstance(self.max_positions, int)
+            or self.max_positions <= 0
+        ):
+            raise ValueError("max_positions must be a positive integer")
+
+
+class RiskDecisionStatus(StrEnum):
+    APPROVED = "approved"
+    REJECTED = "rejected"
+
+
+@dataclass(frozen=True, slots=True)
+class RiskDecision:
+    decision_id: str
+    portfolio_target_id: str
+    policy_id: str
+    status: RiskDecisionStatus
+    reasons: tuple[str, ...]
+    created_at: datetime
+
+
+@dataclass(frozen=True, slots=True, init=False)
+class PaperOrderIntent:
+    intent_id: str
+    portfolio_target_id: str
+    risk_decision_id: str
+    environment: str
+    target_weights: Mapping[str, float]
+    created_at: datetime
+
+    def __init__(self, target: PortfolioTarget, decision: RiskDecision) -> None:
+        if decision.status is not RiskDecisionStatus.APPROVED:
+            raise ValueError("an approved risk decision is required")
+        if decision.portfolio_target_id != target.target_id:
+            raise ValueError("risk decision does not bind the supplied portfolio target")
+        target_weights = _immutable_weights(target.weights)
+        payload = {
+            "portfolio_target_id": target.target_id,
+            "risk_decision_id": decision.decision_id,
+            "environment": "paper",
+            "target_weights": target_weights,
+            "created_at": decision.created_at,
+        }
+        object.__setattr__(self, "intent_id", _stable_id("order-intent", payload))
+        object.__setattr__(self, "portfolio_target_id", target.target_id)
+        object.__setattr__(self, "risk_decision_id", decision.decision_id)
+        object.__setattr__(self, "environment", "paper")
+        object.__setattr__(self, "target_weights", target_weights)
+        object.__setattr__(self, "created_at", decision.created_at)
+
+
+@dataclass(frozen=True, slots=True)
+class GovernedFactorSliceResult:
+    dataset_snapshot: DatasetSnapshot
+    factor_version: FactorVersion
+    strategy_version: StrategyVersion
+    research_result: FactorBacktestResult
+    backtest_run: BacktestRun
+    portfolio_target: PortfolioTarget
+    risk_decision: RiskDecision
+    order_intent: PaperOrderIntent | None
+
+
+def evaluate_portfolio_risk(
+    target: PortfolioTarget,
+    policy: RiskPolicy,
+    *,
+    created_at: datetime | None = None,
+) -> RiskDecision:
+    """Apply fail-closed paper risk limits to a versioned target portfolio."""
+    decision_time = target.created_at if created_at is None else _aware_utc(created_at, "created_at")
+    reasons: list[str] = []
+    if target.gross_exposure > policy.max_gross_exposure + 1e-12:
+        reasons.append(
+            f"gross exposure {target.gross_exposure:.12g} exceeds "
+            f"limit {policy.max_gross_exposure:.12g}"
+        )
+    positive_weights = [weight for weight in target.weights.values() if weight > 0]
+    largest_weight = max(positive_weights, default=0.0)
+    if largest_weight > policy.max_single_asset_weight + 1e-12:
+        reasons.append(
+            f"single-asset weight {largest_weight:.12g} exceeds "
+            f"limit {policy.max_single_asset_weight:.12g}"
+        )
+    if len(positive_weights) > policy.max_positions:
+        reasons.append(
+            f"position count {len(positive_weights)} exceeds limit {policy.max_positions}"
+        )
+    status = RiskDecisionStatus.REJECTED if reasons else RiskDecisionStatus.APPROVED
+    payload = {
+        "portfolio_target_id": target.target_id,
+        "policy_id": policy.policy_id,
+        "status": status,
+        "reasons": reasons,
+        "created_at": decision_time,
+    }
+    return RiskDecision(
+        decision_id=_stable_id("risk-decision", payload),
+        portfolio_target_id=target.target_id,
+        policy_id=policy.policy_id,
+        status=status,
+        reasons=tuple(reasons),
+        created_at=decision_time,
+    )
+
+
+def create_paper_order_intent(
+    target: PortfolioTarget,
+    decision: RiskDecision,
+) -> PaperOrderIntent:
+    """Create a paper-only intent after an exact, approved risk decision."""
+    return PaperOrderIntent(target, decision)
+
+
+def run_governed_factor_slice(
+    *,
+    factor_scores: pd.DataFrame,
+    execution_prices: pd.DataFrame,
+    valuation_prices: pd.DataFrame,
+    dataset_snapshot: DatasetSnapshot,
+    factor_version: FactorVersion,
+    strategy_version: StrategyVersion,
+    risk_policy: RiskPolicy,
+    code_revision: str,
+    created_at: datetime,
+    top_k: int,
+    execution_price_field: str,
+    valuation_price_field: str,
+    lag_sessions: int = 1,
+    gross_exposure: float = 1.0,
+    largest: bool = True,
+    initial_cash: float = 1_000_000.0,
+    execution_config: ExecutionConfig | None = None,
+) -> GovernedFactorSliceResult:
+    """Run snapshot → factor → strategy → backtest → target → risk → paper intent."""
+    if strategy_version.factor_version_id != factor_version.version_id:
+        raise ValueError("strategy factor lineage does not match factor_version")
+    if dataset_snapshot.schema_version != factor_version.dataset_schema_version:
+        raise ValueError("factor dataset schema does not match dataset snapshot")
+    if strategy_version.stage not in {StrategyStage.APPROVED, StrategyStage.PAPER}:
+        raise ValueError("strategy must be in Approved or Paper stage")
+    normalized_revision = code_revision.strip().lower()
+    if not _GIT_SHA.fullmatch(normalized_revision):
+        raise ValueError("code_revision must be a lowercase 40-character Git SHA")
+    run_time = _aware_utc(created_at, "created_at")
+    if dataset_snapshot.available_at > run_time or dataset_snapshot.ingested_at > run_time:
+        raise ValueError("dataset snapshot must be available before the research run")
+    if not factor_scores.empty:
+        latest_decision = pd.Timestamp(factor_scores.index.max())
+        latest_decision_date = latest_decision.date()
+        if latest_decision_date > run_time.date():
+            raise ValueError("factor_scores contain future decision dates")
+    config = ExecutionConfig() if execution_config is None else execution_config
+    if not isinstance(config, ExecutionConfig):
+        raise TypeError("execution_config must be an ExecutionConfig")
+
+    parameters: dict[str, object] = {
+        "top_k": top_k,
+        "execution_price_field": execution_price_field,
+        "valuation_price_field": valuation_price_field,
+        "lag_sessions": lag_sessions,
+        "gross_exposure": gross_exposure,
+        "largest": largest,
+        "initial_cash": initial_cash,
+        "execution_config": asdict(config),
+    }
+    config_hash = _stable_id("config", parameters).split(":", maxsplit=1)[1]
+    run_payload = {
+        "dataset_snapshot_id": dataset_snapshot.snapshot_id,
+        "dataset_content_sha256": dataset_snapshot.content_sha256,
+        "factor_version_id": factor_version.version_id,
+        "factor_definition_sha256": factor_version.definition_sha256,
+        "strategy_version_id": strategy_version.version_id,
+        "code_revision": normalized_revision,
+        "config_hash": config_hash,
+        "created_at": run_time,
+    }
+    backtest_run = BacktestRun(
+        run_id=_stable_id("backtest-run", run_payload),
+        dataset_snapshot_id=dataset_snapshot.snapshot_id,
+        factor_version_id=factor_version.version_id,
+        strategy_version_id=strategy_version.version_id,
+        code_revision=normalized_revision,
+        config_hash=config_hash,
+        created_at=run_time,
+    )
+
+    research_result = run_factor_backtest_research(
+        factor_scores,
+        execution_prices,
+        valuation_prices,
+        top_k=top_k,
+        execution_price_field=execution_price_field,
+        valuation_price_field=valuation_price_field,
+        lag_sessions=lag_sessions,
+        gross_exposure=gross_exposure,
+        largest=largest,
+        initial_cash=initial_cash,
+        config=config,
+    )
+    if research_result.schedule.decision_weights.empty:
+        raise ValueError("governed slice requires at least one target portfolio")
+    final_weights = {
+        str(asset): float(weight)
+        for asset, weight in research_result.schedule.decision_weights.iloc[-1].items()
+    }
+    target_payload = {
+        "backtest_run_id": backtest_run.run_id,
+        "dataset_snapshot_id": dataset_snapshot.snapshot_id,
+        "weights": final_weights,
+        "created_at": run_time,
+    }
+    portfolio_target = PortfolioTarget(
+        target_id=_stable_id("portfolio-target", target_payload),
+        backtest_run_id=backtest_run.run_id,
+        dataset_snapshot_id=dataset_snapshot.snapshot_id,
+        weights=final_weights,
+        created_at=run_time,
+    )
+    risk_decision = evaluate_portfolio_risk(
+        portfolio_target,
+        risk_policy,
+        created_at=run_time,
+    )
+    order_intent = (
+        create_paper_order_intent(portfolio_target, risk_decision)
+        if risk_decision.status is RiskDecisionStatus.APPROVED
+        else None
+    )
+    return GovernedFactorSliceResult(
+        dataset_snapshot=dataset_snapshot,
+        factor_version=factor_version,
+        strategy_version=strategy_version,
+        research_result=research_result,
+        backtest_run=backtest_run,
+        portfolio_target=portfolio_target,
+        risk_decision=risk_decision,
+        order_intent=order_intent,
+    )
+
+
+def _demo() -> Mapping[str, object]:
+    dates = pd.date_range("2026-01-05", periods=4, freq="B")
+    scores = pd.DataFrame({"A": [2.0, 1.0], "B": [1.0, 2.0]}, index=dates[:2])
+    opens = pd.DataFrame({"A": [10.0, 10.0, 10.2, 10.3], "B": [20.0, 20.0, 20.5, 21.0]}, index=dates)
+    result = run_governed_factor_slice(
+        factor_scores=scores,
+        execution_prices=opens,
+        valuation_prices=opens * 1.01,
+        dataset_snapshot=DatasetSnapshot(
+            snapshot_id="dataset:architecture-smoke-v1",
+            schema_version="1.0.0",
+            content_sha256="a" * 64,
+            effective_at=datetime(2026, 1, 8, 7, tzinfo=UTC),
+            available_at=datetime(2026, 1, 8, 8, tzinfo=UTC),
+            ingested_at=datetime(2026, 1, 8, 8, 5, tzinfo=UTC),
+        ),
+        factor_version=FactorVersion(
+            factor_id="factor:architecture-smoke",
+            version="1.0.0",
+            definition_sha256="b" * 64,
+            dataset_schema_version="1.0.0",
+        ),
+        strategy_version=StrategyVersion(
+            strategy_id="strategy:architecture-smoke",
+            version="1.0.0",
+            factor_version_id="factor:architecture-smoke@1.0.0",
+            stage=StrategyStage.APPROVED,
+        ),
+        risk_policy=RiskPolicy(
+            policy_id="risk:architecture-smoke@1.0.0",
+            max_gross_exposure=1.0,
+            max_single_asset_weight=0.6,
+            max_positions=10,
+        ),
+        code_revision="c" * 40,
+        created_at=datetime(2026, 1, 9, 1, tzinfo=UTC),
+        top_k=2,
+        execution_price_field="open",
+        valuation_price_field="close",
+        execution_config=ExecutionConfig(
+            commission_bps=0,
+            stamp_tax_bps=0,
+            slippage_bps=0,
+            min_trade_amount=0,
+        ),
+    )
+    return {
+        "backtest_run_id": result.backtest_run.run_id,
+        "portfolio_target_id": result.portfolio_target.target_id,
+        "risk_decision": result.risk_decision.status.value,
+        "order_intent_id": result.order_intent.intent_id if result.order_intent else None,
+        "environment": result.order_intent.environment if result.order_intent else None,
+    }
+
+
+if __name__ == "__main__":
+    print(json.dumps(_demo(), ensure_ascii=False, sort_keys=True))

tests/governance/test_ci_contract.py

@@ -9,14 +9,22 @@ ROOT = Path(__file__).resolve().parents[2]
 
 
 class CiContractTests(unittest.TestCase):
-    def test_ci_is_one_dependency_free_lite_gate(self) -> None:
+    def test_ci_is_one_locked_shared_runtime_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*$", jobs), ["lite"])
         self.assertIn("actions/checkout@524e936cd9e579adf00e308bfdf971aebc7de09e", workflow)
         self.assertIn("persist-credentials: false", workflow)
-        self.assertIn("python3 tests/governance/test_module_spec.py", workflow)
-        for forbidden in ("setup-python", "pip ", "curl ", "wget ", "docker pull"):
+        self.assertIn("UV_PYTHON_DOWNLOADS: never", workflow)
+        self.assertIn('test "$(python3 --version)" = "Python 3.13.15"', workflow)
+        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)
 
 

tests/test_alpha_factors.py

@@ -6,8 +6,23 @@ 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,
+    ALPHA158_PHASE6_FORMULA_CATALOG_SHA256,
+    ALPHA158_PHASE6_FORMULA_CONTRACT_VERSION,
+    ALPHA158_PHASE6_FORMULA_SPECS,
     alpha_001,
     alpha_002,
     alpha_003,
@@ -166,6 +181,18 @@ 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,
+    evaluate_phase6_formula,
+    list_phase1_operators,
+    list_phase2_operators,
+    list_phase3_formulas,
+    list_phase4_formulas,
+    list_phase5_formulas,
+    list_phase6_formulas,
     correlation,
     covariance,
     decay_linear,
@@ -1232,3 +1259,901 @@ 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__)
+
+
+# ── Alpha158 Phase 6: versioned alpha151-alpha158 formula contract ──────────
+
+
+def test_phase6_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(151, 159))
+    expected_fields = {
+        "name",
+        "contract_version",
+        "formula",
+        "category",
+        "complexity",
+        "parameters",
+        "description",
+        "references",
+        "call_inputs",
+        "formula_inputs",
+        "input_category",
+    }
+
+    assert ALPHA158_PHASE6_FORMULA_CONTRACT_VERSION == "1.0.0"
+    assert list_phase6_formulas() == expected_ids
+    assert tuple(ALPHA158_PHASE6_FORMULA_SPECS) == expected_ids
+    assert Counter(
+        spec["input_category"] for spec in ALPHA158_PHASE6_FORMULA_SPECS.values()
+    ) == {"pair": 6, "triple": 2}
+
+    for alpha_id, spec in ALPHA158_PHASE6_FORMULA_SPECS.items():
+        assert set(spec) == expected_fields
+        assert spec["name"] == alpha_id
+        assert spec["contract_version"] == ALPHA158_PHASE6_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_PHASE6_FORMULA_SPECS.items()
+    }
+    encoded = json.dumps(
+        serializable_specs,
+        sort_keys=True,
+        separators=(",", ":"),
+        ensure_ascii=False,
+    ).encode()
+    assert hashlib.sha256(encoded).hexdigest() == ALPHA158_PHASE6_FORMULA_CATALOG_SHA256
+    assert ALPHA158_PHASE6_FORMULA_CATALOG_SHA256 == (
+        "70ffae16ca6cbb59a1e8644f5cdeec1d291fa75ff8b5647fb534af0083408219"
+    )
+
+
+def test_phase6_formula_catalog_is_recursively_immutable():
+    import operator
+
+    with pytest.raises(TypeError):
+        operator.setitem(ALPHA158_PHASE6_FORMULA_SPECS, "alpha_151", {})
+    with pytest.raises(TypeError):
+        operator.setitem(
+            ALPHA158_PHASE6_FORMULA_SPECS["alpha_151"],
+            "formula",
+            "changed",
+        )
+    with pytest.raises(TypeError):
+        operator.setitem(
+            ALPHA158_PHASE6_FORMULA_SPECS["alpha_151"]["call_inputs"],
+            0,
+            "volume",
+        )
+
+
+def test_phase6_catalog_freezes_callable_and_formula_inputs():
+    import inspect
+
+    for alpha_id, spec in ALPHA158_PHASE6_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_PHASE6_FORMULA_SPECS["alpha_158"]["call_inputs"] == (
+        "high",
+        "low",
+        "volume",
+    )
+
+
+def test_phase6_dispatch_matches_all_existing_alpha151_alpha158_functions():
+    inputs = _phase3_market_inputs()
+
+    for alpha_id, spec in ALPHA158_PHASE6_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_phase6_formula(
+            alpha_id,
+            **{name: inputs[name] for name in reversed(call_inputs)},
+        )
+        pd.testing.assert_series_equal(actual, expected)
+
+
+def test_phase6_dispatch_rejects_unknown_missing_extra_and_non_series_inputs():
+    inputs = _phase3_market_inputs()
+
+    with pytest.raises(KeyError, match="not registered"):
+        evaluate_phase6_formula("alpha_150", close=inputs["close"])
+    with pytest.raises(KeyError, match="not registered"):
+        evaluate_phase6_formula("alpha_159", close=inputs["close"])
+    with pytest.raises(ValueError, match=r"missing inputs.*volume"):
+        evaluate_phase6_formula("alpha_151", close=inputs["close"])
+    with pytest.raises(ValueError, match=r"unexpected inputs.*vwap"):
+        evaluate_phase6_formula(
+            "alpha_151",
+            close=inputs["close"],
+            volume=inputs["volume"],
+            vwap=inputs["vwap"],
+        )
+    with pytest.raises(TypeError, match="volume must be a pandas Series"):
+        evaluate_phase6_formula(  # type: ignore[arg-type]
+            "alpha_151",
+            close=inputs["close"],
+            volume=[1.0, 2.0],
+        )
+
+
+def test_phase6_dispatch_rejects_length_and_index_alignment_errors():
+    inputs = _phase3_market_inputs()
+    shorter_volume = inputs["volume"].iloc[:-1]
+    misaligned_low = inputs["low"].rename(index={79: 80})
+
+    with pytest.raises(ValueError, match="volume length must match close"):
+        evaluate_phase6_formula(
+            "alpha_151",
+            close=inputs["close"],
+            volume=shorter_volume,
+        )
+    with pytest.raises(ValueError, match="low index must align with high"):
+        evaluate_phase6_formula(
+            "alpha_158",
+            high=inputs["high"],
+            low=misaligned_low,
+            volume=inputs["volume"],
+        )
+
+
+def test_phase6_preserves_complete_alpha001_alpha158_formula_body_fingerprint():
+    import ast
+    import hashlib
+    import inspect
+    import json
+    import textwrap
+
+    fingerprints = {}
+    for number in range(1, 159):
+        alpha_id = f"alpha_{number:03d}"
+        source = textwrap.dedent(inspect.getsource(getattr(alpha_factors_module, alpha_id)))
+        node = ast.parse(source).body[0]
+        assert isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef))
+        body = ast.dump(
+            ast.Module(body=node.body, type_ignores=[]),
+            include_attributes=False,
+        )
+        fingerprints[alpha_id] = hashlib.sha256(body.encode()).hexdigest()
+
+    encoded = json.dumps(
+        fingerprints,
+        sort_keys=True,
+        separators=(",", ":"),
+    ).encode()
+    assert hashlib.sha256(encoded).hexdigest() == (
+        "f3ae807983cf8ca083d0b924c3807ffd84a62a5bd354f9efb1a1a16ca40c7da6"
+    )
+
+
+def test_phase6_contract_is_publicly_exported():
+    assert {
+        "ALPHA158_PHASE6_FORMULA_CONTRACT_VERSION",
+        "ALPHA158_PHASE6_FORMULA_CATALOG_SHA256",
+        "ALPHA158_PHASE6_FORMULA_SPECS",
+        "list_phase6_formulas",
+        "evaluate_phase6_formula",
+    } <= set(alpha_factors_module.__all__)

tests/test_artifact.py

@@ -164,6 +164,7 @@ 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})
@@ -182,6 +183,31 @@ 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()
@@ -201,6 +227,7 @@ 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",
                 )
             },
         )
@@ -215,6 +242,7 @@ 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",
                 )
             },
         )

tests/test_data_adapter.py

@@ -7,10 +7,12 @@ import pandas as pd
 import pytest
 
 from quant_engine.data_adapter import (
+    AssetReturnSnapshot,
     add_vwap_proxy,
     apply_adj_factor,
     load_qtdb_daily,
     long_to_wide,
+    prepare_asset_return_snapshot,
     prepare_execution_inputs,
     prepare_stock_series,
     rename_tushare_columns,
@@ -298,13 +300,176 @@ def test_prepare_execution_inputs_missing_close_raises() -> None:
 
 
 def test_prepare_execution_inputs_missing_selected_price_raises() -> None:
-    df = pd.DataFrame(
-        {"stock_code": ["A"], "trade_date": ["2024-01-01"], "close": [10.0]}
-    )
+    df = pd.DataFrame({"stock_code": ["A"], "trade_date": ["2024-01-01"], "close": [10.0]})
     with pytest.raises(ValueError, match="缺 open"):
         prepare_execution_inputs(df, price_col="open")
 
 
+# ── prepare_asset_return_snapshot ────────────────────────────
+
+
+def _daily_prices() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "stock_code": ["B", "A", "B", "A", "B", "A"],
+            "trade_date": [
+                "2024-01-02",
+                "2024-01-01",
+                "2024-01-01",
+                "2024-01-03",
+                "2024-01-03",
+                "2024-01-02",
+            ],
+            "close": [18.0, 10.0, 20.0, 12.1, 19.8, 11.0],
+        }
+    )
+
+
+def test_prepare_asset_return_snapshot_is_stable_and_immutable_by_interface() -> None:
+    snapshot = prepare_asset_return_snapshot(
+        _daily_prices(),
+        source="qtdb_pro.hq_daily",
+        source_snapshot_id="hq-daily:2024-01-03:v1",
+        adjustment="qfq",
+    )
+
+    assert isinstance(snapshot, AssetReturnSnapshot)
+    assert snapshot.data_snapshot_id.startswith("asset-returns-v1:")
+    assert snapshot.source == "qtdb_pro.hq_daily"
+    assert snapshot.source_snapshot_id == "hq-daily:2024-01-03:v1"
+    assert snapshot.price_field == "close"
+    assert snapshot.adjustment == "qfq"
+    assert snapshot.return_method == "simple"
+    assert snapshot.start_date.isoformat() == "2024-01-01"
+    assert snapshot.end_date.isoformat() == "2024-01-03"
+    assert snapshot.sessions == 3
+    assert snapshot.assets == ("A", "B")
+
+    expected = pd.DataFrame(
+        {
+            "A": [np.nan, 0.1, 0.1],
+            "B": [np.nan, -0.1, 0.1],
+        },
+        index=pd.to_datetime(["2024-01-01", "2024-01-02", "2024-01-03"]),
+    )
+    expected.index.name = "trade_date"
+    expected.columns.name = "stock_code"
+    pd.testing.assert_frame_equal(snapshot.returns, expected)
+
+    exposed = snapshot.returns
+    exposed.iloc[1, 0] = 999.0
+    assert snapshot.returns.iloc[1, 0] == pytest.approx(0.1)
+
+
+def test_asset_return_snapshot_identity_is_order_independent_and_content_addressed() -> None:
+    kwargs = {
+        "source": "qtdb_pro.hq_daily",
+        "source_snapshot_id": "hq-daily:2024-01-03:v1",
+        "adjustment": "none",
+    }
+    baseline = prepare_asset_return_snapshot(_daily_prices(), **kwargs)
+    shuffled = prepare_asset_return_snapshot(
+        _daily_prices().sample(frac=1.0, random_state=7),
+        **kwargs,
+    )
+    changed_prices = _daily_prices().copy()
+    changed_prices.loc[changed_prices["close"] == 12.1, "close"] = 12.2
+    changed_content = prepare_asset_return_snapshot(changed_prices, **kwargs)
+    changed_source = prepare_asset_return_snapshot(
+        _daily_prices(),
+        source="qtdb_pro.hq_daily",
+        source_snapshot_id="hq-daily:2024-01-03:v2",
+        adjustment="none",
+    )
+
+    assert shuffled.data_snapshot_id == baseline.data_snapshot_id
+    assert changed_content.data_snapshot_id != baseline.data_snapshot_id
+    assert changed_source.data_snapshot_id != baseline.data_snapshot_id
+
+
+def test_prepare_asset_return_snapshot_does_not_fill_missing_prices() -> None:
+    prices = _daily_prices()
+    prices.loc[
+        (prices["stock_code"] == "A") & (prices["trade_date"] == "2024-01-02"),
+        "close",
+    ] = np.nan
+
+    snapshot = prepare_asset_return_snapshot(
+        prices,
+        source="qtdb_pro.hq_daily",
+        source_snapshot_id="hq-daily:missing-middle",
+    )
+
+    assert pd.isna(snapshot.returns.loc[pd.Timestamp("2024-01-02"), "A"])
+    assert pd.isna(snapshot.returns.loc[pd.Timestamp("2024-01-03"), "A"])
+
+
+def test_prepare_asset_return_snapshot_rejects_duplicate_sessions() -> None:
+    duplicate = pd.concat([_daily_prices(), _daily_prices().iloc[[0]]], ignore_index=True)
+
+    with pytest.raises(ValueError, match="duplicate"):
+        prepare_asset_return_snapshot(
+            duplicate,
+            source="qtdb_pro.hq_daily",
+            source_snapshot_id="hq-daily:duplicate",
+        )
+
+
+@pytest.mark.parametrize("invalid_price", [0.0, -1.0, np.inf])
+def test_prepare_asset_return_snapshot_rejects_invalid_prices(invalid_price: float) -> None:
+    prices = _daily_prices()
+    prices.loc[0, "close"] = invalid_price
+
+    with pytest.raises(ValueError, match="positive finite"):
+        prepare_asset_return_snapshot(
+            prices,
+            source="qtdb_pro.hq_daily",
+            source_snapshot_id="hq-daily:invalid-price",
+        )
+
+
+@pytest.mark.parametrize(
+    ("source", "source_snapshot_id", "adjustment"),
+    [
+        ("", "source-1", "none"),
+        ("qtdb_pro.hq_daily", "", "none"),
+        ("qtdb_pro.hq_daily", "source-1", ""),
+    ],
+)
+def test_prepare_asset_return_snapshot_requires_explicit_identity_semantics(
+    source: str,
+    source_snapshot_id: str,
+    adjustment: str,
+) -> None:
+    with pytest.raises(ValueError, match="must be non-empty"):
+        prepare_asset_return_snapshot(
+            _daily_prices(),
+            source=source,
+            source_snapshot_id=source_snapshot_id,
+            adjustment=adjustment,
+        )
+
+
+def test_asset_return_snapshot_feeds_reproducible_covariance_lineage() -> None:
+    from quant_engine.risk import estimate_covariance_snapshot
+
+    market_snapshot = prepare_asset_return_snapshot(
+        _daily_prices(),
+        source="qtdb_pro.hq_daily",
+        source_snapshot_id="hq-daily:2024-01-03:v1",
+    )
+    covariance_snapshot = estimate_covariance_snapshot(
+        market_snapshot.returns,
+        as_of_date=market_snapshot.end_date,
+        lookback_sessions=3,
+        min_observations=2,
+        data_snapshot_id=market_snapshot.data_snapshot_id,
+    )
+
+    assert covariance_snapshot.data_snapshot_id == market_snapshot.data_snapshot_id
+    assert covariance_snapshot.snapshot_id.startswith("sample-cov-v1:")
+
+
 # ── 端到端：长表 → 适配 → alpha158 + execution ──────────────
 
 

tests/test_governed_pipeline.py

@@ -0,0 +1,320 @@
+"""Governed Personal Quant OS vertical-slice contracts."""
+
+from __future__ import annotations
+
+from datetime import UTC, datetime
+
+import pandas as pd
+import pytest
+
+from quant_engine.execution import ExecutionConfig
+from quant_engine.governed_pipeline import (
+    DatasetSnapshot,
+    FactorVersion,
+    PaperOrderIntent,
+    RiskDecisionStatus,
+    RiskPolicy,
+    StrategyStage,
+    StrategyVersion,
+    create_paper_order_intent,
+    run_governed_factor_slice,
+)
+
+
+def _calendar() -> pd.DatetimeIndex:
+    return pd.date_range("2026-01-05", periods=4, freq="B")
+
+
+def _scores() -> pd.DataFrame:
+    dates = _calendar()
+    return pd.DataFrame(
+        {"A": [3.0, 1.0], "B": [2.0, 3.0], "C": [1.0, 2.0]},
+        index=dates[:2],
+    )
+
+
+def _prices() -> tuple[pd.DataFrame, pd.DataFrame]:
+    dates = _calendar()
+    opens = pd.DataFrame(
+        {"A": [10.0, 10.0, 10.2, 10.4], "B": [20.0, 20.0, 20.5, 21.0], "C": [30.0, 30.0, 30.0, 30.0]},
+        index=dates,
+    )
+    closes = opens * 1.01
+    return opens, closes
+
+
+def _snapshot() -> DatasetSnapshot:
+    return DatasetSnapshot(
+        snapshot_id="dataset:cn-a-daily-20260108-v1",
+        schema_version="1.0.0",
+        content_sha256="a" * 64,
+        effective_at=datetime(2026, 1, 8, 7, tzinfo=UTC),
+        available_at=datetime(2026, 1, 8, 8, tzinfo=UTC),
+        ingested_at=datetime(2026, 1, 8, 8, 5, tzinfo=UTC),
+    )
+
+
+def _factor() -> FactorVersion:
+    return FactorVersion(
+        factor_id="factor:demo-momentum",
+        version="1.0.0",
+        definition_sha256="b" * 64,
+        dataset_schema_version="1.0.0",
+    )
+
+
+def _strategy() -> StrategyVersion:
+    return StrategyVersion(
+        strategy_id="strategy:demo-top2",
+        version="1.0.0",
+        factor_version_id="factor:demo-momentum@1.0.0",
+        stage=StrategyStage.APPROVED,
+    )
+
+
+def _execution_config() -> ExecutionConfig:
+    return ExecutionConfig(
+        commission_bps=0,
+        stamp_tax_bps=0,
+        slippage_bps=0,
+        min_trade_amount=0,
+    )
+
+
+def test_governed_slice_is_reproducible_and_creates_only_paper_intent() -> None:
+    opens, closes = _prices()
+    created_at = datetime(2026, 1, 9, 1, tzinfo=UTC)
+    policy = RiskPolicy(
+        policy_id="risk:paper-default@1.0.0",
+        max_gross_exposure=1.0,
+        max_single_asset_weight=0.6,
+        max_positions=10,
+    )
+
+    result = run_governed_factor_slice(
+        factor_scores=_scores(),
+        execution_prices=opens,
+        valuation_prices=closes,
+        dataset_snapshot=_snapshot(),
+        factor_version=_factor(),
+        strategy_version=_strategy(),
+        risk_policy=policy,
+        code_revision="c" * 40,
+        created_at=created_at,
+        top_k=2,
+        execution_price_field="open",
+        valuation_price_field="close",
+        execution_config=_execution_config(),
+    )
+
+    assert result.backtest_run.dataset_snapshot_id == _snapshot().snapshot_id
+    assert result.backtest_run.factor_version_id == _factor().version_id
+    assert result.backtest_run.strategy_version_id == _strategy().version_id
+    assert result.backtest_run.code_revision == "c" * 40
+    assert len(result.backtest_run.config_hash) == 64
+    assert result.portfolio_target.backtest_run_id == result.backtest_run.run_id
+    assert result.risk_decision.status is RiskDecisionStatus.APPROVED
+    assert result.risk_decision.portfolio_target_id == result.portfolio_target.target_id
+    assert result.order_intent is not None
+    assert result.order_intent.environment == "paper"
+    assert result.order_intent.risk_decision_id == result.risk_decision.decision_id
+    assert result.order_intent.portfolio_target_id == result.portfolio_target.target_id
+
+    repeated = run_governed_factor_slice(
+        factor_scores=_scores(),
+        execution_prices=opens,
+        valuation_prices=closes,
+        dataset_snapshot=_snapshot(),
+        factor_version=_factor(),
+        strategy_version=_strategy(),
+        risk_policy=policy,
+        code_revision="c" * 40,
+        created_at=created_at,
+        top_k=2,
+        execution_price_field="open",
+        valuation_price_field="close",
+        execution_config=_execution_config(),
+    )
+    assert repeated.backtest_run.run_id == result.backtest_run.run_id
+    assert repeated.portfolio_target.target_id == result.portfolio_target.target_id
+    assert repeated.risk_decision.decision_id == result.risk_decision.decision_id
+    assert repeated.order_intent == result.order_intent
+
+
+def test_risk_rejection_blocks_order_intent() -> None:
+    opens, closes = _prices()
+    result = run_governed_factor_slice(
+        factor_scores=_scores(),
+        execution_prices=opens,
+        valuation_prices=closes,
+        dataset_snapshot=_snapshot(),
+        factor_version=_factor(),
+        strategy_version=_strategy(),
+        risk_policy=RiskPolicy(
+            policy_id="risk:no-concentration@1.0.0",
+            max_gross_exposure=1.0,
+            max_single_asset_weight=0.4,
+            max_positions=10,
+        ),
+        code_revision="c" * 40,
+        created_at=datetime(2026, 1, 9, 1, tzinfo=UTC),
+        top_k=2,
+        execution_price_field="open",
+        valuation_price_field="close",
+        execution_config=_execution_config(),
+    )
+
+    assert result.risk_decision.status is RiskDecisionStatus.REJECTED
+    assert any("single-asset weight" in reason for reason in result.risk_decision.reasons)
+    assert result.order_intent is None
+    with pytest.raises(ValueError, match="approved risk decision"):
+        create_paper_order_intent(result.portfolio_target, result.risk_decision)
+    with pytest.raises(ValueError, match="approved risk decision"):
+        PaperOrderIntent(result.portfolio_target, result.risk_decision)
+
+
+def test_dataset_snapshot_requires_point_in_time_ordering_and_aware_times() -> None:
+    with pytest.raises(ValueError, match="timezone-aware"):
+        DatasetSnapshot(
+            snapshot_id="dataset:invalid",
+            schema_version="1.0.0",
+            content_sha256="a" * 64,
+            effective_at=datetime(2026, 1, 8, 7),
+            available_at=datetime(2026, 1, 8, 8, tzinfo=UTC),
+            ingested_at=datetime(2026, 1, 8, 9, tzinfo=UTC),
+        )
+
+    with pytest.raises(ValueError, match="effective_at <= available_at <= ingested_at"):
+        DatasetSnapshot(
+            snapshot_id="dataset:invalid",
+            schema_version="1.0.0",
+            content_sha256="a" * 64,
+            effective_at=datetime(2026, 1, 8, 9, tzinfo=UTC),
+            available_at=datetime(2026, 1, 8, 8, tzinfo=UTC),
+            ingested_at=datetime(2026, 1, 8, 10, tzinfo=UTC),
+        )
+
+
+def test_strategy_factor_lineage_must_match() -> None:
+    opens, closes = _prices()
+    mismatched = StrategyVersion(
+        strategy_id="strategy:demo-top2",
+        version="1.0.0",
+        factor_version_id="factor:other@1.0.0",
+        stage=StrategyStage.APPROVED,
+    )
+
+    with pytest.raises(ValueError, match="factor lineage"):
+        run_governed_factor_slice(
+            factor_scores=_scores(),
+            execution_prices=opens,
+            valuation_prices=closes,
+            dataset_snapshot=_snapshot(),
+            factor_version=_factor(),
+            strategy_version=mismatched,
+            risk_policy=RiskPolicy(
+                policy_id="risk:paper-default@1.0.0",
+                max_gross_exposure=1.0,
+                max_single_asset_weight=0.6,
+                max_positions=10,
+            ),
+            code_revision="c" * 40,
+            created_at=datetime(2026, 1, 9, 1, tzinfo=UTC),
+            top_k=2,
+            execution_price_field="open",
+            valuation_price_field="close",
+            execution_config=_execution_config(),
+        )
+
+
+def test_governed_slice_requires_matching_schema_and_snapshot_available_by_run_time() -> None:
+    opens, closes = _prices()
+    common = {
+        "factor_scores": _scores(),
+        "execution_prices": opens,
+        "valuation_prices": closes,
+        "strategy_version": _strategy(),
+        "risk_policy": RiskPolicy(
+            policy_id="risk:paper-default@1.0.0",
+            max_gross_exposure=1.0,
+            max_single_asset_weight=0.6,
+            max_positions=10,
+        ),
+        "code_revision": "c" * 40,
+        "top_k": 2,
+        "execution_price_field": "open",
+        "valuation_price_field": "close",
+        "execution_config": _execution_config(),
+    }
+
+    with pytest.raises(ValueError, match="dataset schema"):
+        run_governed_factor_slice(
+            dataset_snapshot=_snapshot(),
+            factor_version=FactorVersion(
+                factor_id="factor:demo-momentum",
+                version="1.0.0",
+                definition_sha256="b" * 64,
+                dataset_schema_version="2.0.0",
+            ),
+            created_at=datetime(2026, 1, 9, 1, tzinfo=UTC),
+            **common,
+        )
+
+    with pytest.raises(ValueError, match="available before the research run"):
+        run_governed_factor_slice(
+            dataset_snapshot=_snapshot(),
+            factor_version=_factor(),
+            created_at=datetime(2026, 1, 8, 7, 30, tzinfo=UTC),
+            **common,
+        )
+
+    future_scores = _scores()
+    future_scores.index = pd.date_range("2026-01-12", periods=2, freq="B")
+    with pytest.raises(ValueError, match="future decision dates"):
+        run_governed_factor_slice(
+            dataset_snapshot=_snapshot(),
+            factor_version=_factor(),
+            factor_scores=future_scores,
+            execution_prices=opens,
+            valuation_prices=closes,
+            strategy_version=common["strategy_version"],
+            risk_policy=common["risk_policy"],
+            code_revision="c" * 40,
+            created_at=datetime(2026, 1, 9, 1, tzinfo=UTC),
+            top_k=2,
+            execution_price_field="open",
+            valuation_price_field="close",
+            execution_config=_execution_config(),
+        )
+
+
+def test_paper_intent_requires_approved_strategy_stage() -> None:
+    opens, closes = _prices()
+    validated = StrategyVersion(
+        strategy_id="strategy:demo-top2",
+        version="1.0.0",
+        factor_version_id=_factor().version_id,
+        stage=StrategyStage.VALIDATED,
+    )
+
+    with pytest.raises(ValueError, match="Approved or Paper"):
+        run_governed_factor_slice(
+            factor_scores=_scores(),
+            execution_prices=opens,
+            valuation_prices=closes,
+            dataset_snapshot=_snapshot(),
+            factor_version=_factor(),
+            strategy_version=validated,
+            risk_policy=RiskPolicy(
+                policy_id="risk:paper-default@1.0.0",
+                max_gross_exposure=1.0,
+                max_single_asset_weight=0.6,
+                max_positions=10,
+            ),
+            code_revision="c" * 40,
+            created_at=datetime(2026, 1, 9, 1, tzinfo=UTC),
+            top_k=2,
+            execution_price_field="open",
+            valuation_price_field="close",
+            execution_config=_execution_config(),
+        )

uv.lock

@@ -0,0 +1,408 @@
+version = 1
+revision = 3
+requires-python = "==3.13.*"
+resolution-markers = [
+    "sys_platform == 'win32'",
+    "sys_platform == 'emscripten'",
+    "sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "ast-serialize"
+version = "0.8.0"
+source = { registry = "https://mirrors.cloud.tencent.com/pypi/simple" }
+sdist = { url = "https://mirrors.cloud.tencent.com/pypi/packages/e1/a9/11851c3e02a3fea2ddc9932d1fdc7d2edaeecc0d2e11bc5f2a7fde2b0934/ast_serialize-0.8.0.tar.gz", hash = "sha256:6c37c43e4004dfb42d321ddedc569dc17ff4259296f3af577c9ea46a809bc010" }
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