test(performance): enforce semantic artifact authority boundary

MODULE_SPEC.yaml

@@ -1,7 +1,7 @@
 {
   "schema_version": 1,
   "module_id": "quant_engine",
-  "authority": {"scope": "module_metadata", "subject": "quant_engine", "owner": "quant-engine-owner", "source": "MODULE_SPEC.yaml", "revision": 1, "effective_from": "2026-08-20T00:00:00+08:00"},
+  "authority": {"scope": "module_metadata", "subject": "quant_engine", "owner": "quant-engine-owner", "source": "MODULE_SPEC.yaml", "revision": 5, "effective_from": "2026-09-01T00:00:00+08:00"},
   "repository": {"name": "quant_engine", "workspace_id": "researchhub", "type": "research_engine", "maturity": "operational"},
   "bounded_context": {
     "domain": "quantitative-research-engine",
@@ -11,6 +11,7 @@
       "Submitting live orders, routing trades, managing brokerage accounts, or claiming transaction execution",
       "Owning market-data source facts, research-result publication, or platform presentation state",
       "Loading provider credentials, brokerage credentials, or production secrets",
+      "Granting portfolio approval, maker-checker decisions, publication eligibility, paper execution, or live execution authority",
       "Changing financial model semantics through module metadata"
     ]
   },
@@ -18,13 +19,29 @@
     {"id": "factor-and-indicator-calculation", "summary": "Calculate reusable alpha factors and technical indicators from caller-supplied data.", "status": "operational"},
     {"id": "execution-simulation", "summary": "Simulate costs, slippage, market constraints, fills, NAV, and PnL without live order routing.", "status": "operational"},
     {"id": "portfolio-backtesting", "summary": "Run weight-based backtests and benchmark comparisons.", "status": "operational"},
+    {"id": "backtest-evidence-contracts", "summary": "Identify governed offline backtest inputs and close existing research artifact and performance-methodology evidence without recomputation, persistence, or decision authority.", "status": "operational"},
+    {"id": "portfolio-risk-computation-contracts", "summary": "Verify deterministic portfolio-computation receipts and expose S3-bound portfolio decisions and risk assessments without adding algorithms or execution authority.", "status": "operational"},
     {"id": "risk-and-performance-analysis", "summary": "Calculate portfolio decomposition, risk contribution, and performance statistics.", "status": "operational"}
   ],
   "data": {"owns": [
     {"asset_id": "quantitative-model-implementations", "kind": "model", "classification": "internal"},
     {"asset_id": "simulation-and-metric-results", "kind": "artifact", "classification": "confidential"}
   ]},
-  "contracts": {"provides": [], "consumes": []},
+  "contracts": {
+    "provides": [
+      {"contract_id": "researchhub.factor-definition", "version": "1.0.0", "authority": "quant_engine", "path": "src/quant_engine/factor_contracts.py"},
+      {"contract_id": "researchhub.factor-set-ref", "version": "1.0.0", "authority": "quant_engine", "path": "src/quant_engine/factor_contracts.py"},
+      {"contract_id": "researchhub.backtest-run-ref", "version": "1.0.0", "authority": "quant_engine", "path": "src/quant_engine/governed_pipeline.py"},
+      {"contract_id": "researchhub.backtest-evidence-manifest", "version": "1.0.0", "authority": "quant_engine", "path": "src/quant_engine/artifact.py"},
+      {"contract_id": "researchhub.performance-evidence", "version": "1.0.0", "authority": "quant_engine", "path": "src/quant_engine/artifact.py"},
+      {"contract_id": "researchhub.portfolio-decision", "version": "1.0.0", "authority": "quant_engine", "path": "src/quant_engine/portfolio_risk_contracts.py"},
+      {"contract_id": "researchhub.risk-assessment", "version": "1.0.0", "authority": "quant_engine", "path": "src/quant_engine/portfolio_risk_contracts.py"}
+    ],
+    "consumes": [
+      {"contract_id": "researchhub.dataset-snapshot", "version": "1.0.0", "authority": "researchhub.data", "admission": "qualified_immutable_envelope"},
+      {"contract_id": "researchhub.data-foundation", "version": "1.0.0", "authority": "researchhub.data", "admission": "content_addressed_selected_views"}
+    ]
+  },
   "dependencies": [],
   "agent_context": {
     "default_entrypoints": [

README.md

@@ -19,13 +19,16 @@
 ## 模块
 
 - `alpha_factors` — 158 alpha 公式 + 24 基础算子（移植自 qlib alpha158）
+- `factor_contracts` — `FactorDefinition` / `FactorSetRef` v1 纯计算合同、严格 PIT/availability 输入准入与显式 legacy 投影
 - `execution` — A 股长仓执行仿真（成本/滑点/现金约束）+ 稀疏调仓/完整交易日 Ledger + 可投影成交与 NAV 审计；T+1、涨跌停、成交量与价差提供独立约束函数
 - `indicators` — 50+ 技术指标（MACD / KDJ / 布林 / ATR / ADX / 等）
 - `data_adapter` — 桥接 qtdb_pro 长表与新模块（rename / long-wide / 复权 / vwap 代理）
 - `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）
-- `artifact` — 版本化、确定性、存储中立的完整 research run 事实表与 manifest
+- `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；同时拥有输入/配置/重放血缘决定的 `BacktestRunRef`
+- `artifact` — 版本化、确定性、存储中立的完整 research run 事实表，以及只映射现有表的 `BacktestEvidenceManifest`
+- `portfolio_risk_contracts` — S3 证据闭合的 `PortfolioDecision` / `RiskAssessment` v1；独立复核 freshness、约束与 computation receipt，并复用既有标签安全风险分解
 - `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
 - `factor_library` — 通用方法（turnover / winsorize / IC / OLS / jb_test）
@@ -55,6 +58,9 @@ 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
@@ -191,6 +197,220 @@ print(backtest.stats())
 print(backtest.benchmark_report())
 ```
 
+## 因子／特征合同 v1
+
+`quant_engine.factor_contracts` 提供 `researchhub.factor-definition` 与
+`researchhub.factor-set-ref` `1.0.0`。合同使用受限 canonical JSON：只接受 ASCII
+lower-snake-case object key、UTF-8 string、bool/null 和 safe integer；小数参数必须用显式
+canonical decimal string。定义、输入映射、上游证据、输出 schema/content 和 lineage 的任一
+语义变化都会产生新 identity。
+
+创建 `FactorSetRef` 必须提供完整且可重算 identity 的 `DatasetSnapshotEnvelope` 与
+`DataFoundationEnvelope`，不能用 ID 字符串或布尔值代替资格证明。每个因子输入都要映射到一个
+实际选中的 `StandardizedViewRef`，schema 必须同时匹配定义和 view；未消费、缺失、重复或跨
+snapshot/Foundation/PIT 的 view 都会失败关闭。snapshot PIT 可以早于 Foundation/view PIT，
+但始终满足 knowledge ≤ snapshot PIT ≤ Foundation/view/FactorSet PIT ≤ evaluation。
+
+```python
+from quant_engine.factor_contracts import (
+    DataFoundationEnvelope,
+    DatasetSnapshotEnvelope,
+    FactorDefinition,
+    FactorSetRef,
+)
+
+snapshot = DatasetSnapshotEnvelope.from_dict(dataset_snapshot_v1)
+foundation = DataFoundationEnvelope.from_dict(data_foundation_v1)
+
+# definition 必须是完整的 FactorDefinition；FactorSetRef.create 还要求显式 input bindings、
+# view availability、output quality/coverage、canonical output bytes 和 immutable artifact ref。
+factor_set = FactorSetRef.create(
+    definitions=(definition,),
+    dataset_snapshot=snapshot,
+    foundation=foundation,
+    **explicit_factor_set_evidence,
+)
+```
+
+`availability_mode="as_available"` 声明 source/view 和计算产物在历史 evaluation 前实际可用；
+`"retrospective_replay"` 保留历史 evaluation，但要求真实 publication/view creation、compute 和
+artifact 时间位于之后，并固定 `historical_availability="not_established"`。两种模式都不会授予
+decision、real-data、production、paper 或 live readiness。
+
+旧 `governed_pipeline.FactorVersion` 的四字段构造器、`factor_id@version`、run/target/risk/order
+identity 均保持不变。迁移只能通过 content-addressed `LegacyFactorBinding`，再显式调用
+`bind_legacy_factor()` 或 `project_legacy_factor()`；后者是有损投影，不表示旧 digest 与新定义
+digest 等价，也不会把旧 run 静默升级为新合同。
+
+## 回测引用与证据合同 v1
+
+`quant_engine.governed_pipeline.BacktestRunRef` 是合格回测运行身份的唯一权威。`run_id` 只由
+已验收的 Dataset Snapshot / Data Foundation / `FactorSetRef` 身份、universe、日历与公司行动
+祖先、策略、执行/成本模型、严格整数 seed、完整代码提交、环境锁、配置、时间和重放血缘决定；
+它不包含任何输出摘要。重放必须绑定直接父运行、连续 attempt 和不变的
+`replay_spec_digest`，输入漂移或血缘环会失败关闭。
+
+`quant_engine.artifact.BacktestEvidenceManifest` 只摘要 `ResearchRunArtifact` 已有的九张事实表。
+固定 `offline_research_v1` 映射为 `run`、`signal`、`fill`、`position_nav`、`performance`、
+`attribution`、`risk_snapshot` 与 `replay`；每张表都保留列模式摘要、行数和内容摘要，空 risk
+表也必须有稳定 schema。`manifest_id` 由完整 RunRef 与输出证据决定，因此结果变化不会反向改变
+`run_id`。当前 artifact 不拥有订单或拒绝事实，所以此画像明确不声明 `order` / `rejection`。
+
+旧 `BacktestRun` 只能通过 `build_legacy_backtest_evidence_manifest()` 显式映射为
+`LEGACY_EXPLORATORY`；不能隐式提升为 `CONTRACT_QUALIFIED`。所有资格均只描述离线证据闭合，
+不表示投资有效、组合获批、Paper、生产或实盘就绪。
+
+## 绩效证据与方法论合同 v1
+
+`quant_engine.artifact.PerformanceEvidenceV1` 在现有计算和事实表之外增加一层只读、内容寻址的
+owner 证据。`build_performance_evidence()` 只接受同一运行的完整 `ResearchRunArtifact`、
+`BacktestRunRef` 与 `CONTRACT_QUALIFIED BacktestEvidenceManifest`；它核对全部 artifact 表、
+performance 表和唯一行摘要，并绑定 artifact、row 与严格对齐 benchmark series 的独立摘要。
+生产 builder 不重算、填补、重命名或覆盖任何绩效值。
+
+方法论固定为日简单收益、252 期年化、绝对指标年化无风险利率 `0.0`、benchmark 日无风险利率
+`0.0`，以及 benchmark 存在时的 `exact_session_index`。相对指标使用封闭 availability：无基准为
+`benchmark_absent`；active variance、benchmark variance 或 alpha 几何年化域不足时分别使用
+对应 `not_estimable_*` 原因。benchmark 存在时 tracking error 始终必须是有限非负值；null 不会
+被转成零。
+
+```python
+from quant_engine.artifact import build_performance_evidence
+
+performance_evidence = build_performance_evidence(
+    artifact,
+    backtest_run_ref,
+    backtest_evidence_manifest,
+)
+canonical_bytes = performance_evidence.canonical_bytes()
+```
+
+该合同范围固定为 `offline_research_only`。它不授予排名、推荐、决策、发布、论文、Paper、生产、
+实盘、交易或投资建议权限，也不包含原始参数、returns、NAV、benchmark series、表字节、存储
+locator、URI 或凭证。
+
+## 组合决策与风险评估合同 v1
+
+`quant_engine.portfolio_risk_contracts` 是现有计算 owner 外围的薄合同层。创建
+`PortfolioDecision` 必须同时提供完整 `BacktestRunRef`、嵌入同一 RunRef 的
+`CONTRACT_QUALIFIED` 非 legacy `BacktestEvidenceManifest`、现有 `PortfolioTarget`、
+`FreshnessPolicy`、`ConstraintSetV1` 与 `ComputationReceipt`。适配器会从权威输入独立重算
+receipt 的 input/constraint/output digest、敞口、持仓数和 L1 turnover 残差；receipt 自报
+成功、fallback 或放宽 tolerance 均不能替代复核。
+
+```python
+from quant_engine.portfolio_risk_contracts import (
+    ComputationReceipt,
+    ConstraintSetV1,
+    FreshnessPolicy,
+    assess_portfolio_risk,
+    build_portfolio_decision,
+    compute_portfolio_receipt_digests,
+)
+
+freshness = FreshnessPolicy(
+    max_manifest_age_seconds=3600,
+    max_covariance_age_days=5,
+)
+constraints = ConstraintSetV1(
+    gross_exposure_max=1.0,
+    single_asset_max=0.10,
+    position_count_max=20,
+    turnover_max=0.30,
+)
+
+# 生产者先形成公开 canonical digest；decision 构建时仍会独立重算。
+expected = compute_portfolio_receipt_digests(
+    backtest_run_ref=run_ref,
+    manifest=evidence_manifest,
+    target=portfolio_target,
+    objective_name="long_only_allocation",
+    objective_version="1.0.0",
+    objective_digest=objective_digest,
+    model_name="factor_weighting",
+    model_version="1.0.0",
+    model_digest=model_digest,
+    expected_return_digest=expected_return_digest,
+    covariance_digest=covariance_digest,
+    scenario_digest=scenario_digest,
+    constraints=constraints,
+    freshness_policy=freshness,
+    prior_weights=prior_weights,
+)
+
+receipt = ComputationReceipt(
+    algorithm="factor_weighting",
+    algorithm_version="1.0.0",
+    implementation_digest=implementation_digest,
+    parameter_digest=parameter_digest,
+    input_digest=expected["input_digest"],
+    constraint_digest=expected["constraint_digest"],
+    output_digest=expected["output_digest"],
+    status="completed",
+    solver_required=False,
+    solver_name=None,
+    solver_version=None,
+    solver_config_digest=None,
+    iterations=None,
+    objective_value=None,
+    max_constraint_residual=expected["max_constraint_residual"],
+    tolerance=1e-12,
+    computed_at=computed_at,
+)
+
+decision = build_portfolio_decision(
+    backtest_run_ref=run_ref,
+    manifest=evidence_manifest,
+    target=portfolio_target,
+    objective_name="long_only_allocation",
+    objective_version="1.0.0",
+    objective_digest=objective_digest,
+    model_name="factor_weighting",
+    model_version="1.0.0",
+    model_digest=model_digest,
+    expected_return_digest=expected_return_digest,
+    covariance_digest=covariance_digest,
+    scenario_digest=scenario_digest,
+    constraints=constraints,
+    freshness_policy=freshness,
+    receipt=receipt,
+    computed_at=computed_at,
+    prior_weights=prior_weights,
+)
+
+assessment = assess_portfolio_risk(
+    portfolio_decision=decision,
+    backtest_run_ref=run_ref,
+    manifest=evidence_manifest,
+    covariance=covariance_snapshot,
+    risk_model_name="euler_volatility",
+    risk_model_version="1.0.0",
+    risk_model_digest=risk_model_digest,
+)
+```
+
+`source_universe_digest` 保留 S3 研究 universe 身份，`portfolio_asset_set_digest` 只描述实际
+目标资产标签；二者不会互相冒充成员证明。风险评估在任何数值计算前要求 covariance、target、
+RunRef 的 dataset identity 三方一致，并且只调用一次现有 `labeled_component_risk()`。合同中的
+`qualified` 仅表示 S4.1 计算证据闭合，不授予 maker-checker、发布、订单、Paper、生产或实盘权限。
+
+## 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）：

src/quant_engine/alpha_factors.py

@@ -3539,6 +3539,95 @@ def evaluate_phase5_formula(name: str, **inputs: pd.Series) -> pd.Series:
     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",
@@ -3587,6 +3676,11 @@ __all__ = [
     "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",

tests/fixtures/backtest-evidence-v1.golden.json

@@ -0,0 +1,17 @@
+{
+  "run_id": "rhbacktestrunv1:sha256:5036c771c44a2adade9ea590eee0d8cf824ff8f519fadd8ba424e5d914856386",
+  "replay_spec_digest": "sha256:20f07fcb526bc38b4ba3d71d6c3b00a8c63ad96cab0fecd869326503ab42d98b",
+  "manifest_id": "rhbacktestevidencev1:sha256:f94733e849433f62f3da1e1ec8999891b93d49c7d657832d64e64bef9e211117",
+  "evidence_digest": "sha256:f3913894d032c389c64eef59058b3cbc694cb9cc14ce9cee2699f0068200b650",
+  "table_content_digests": {
+    "run": "sha256:7d947ec93f714641669cbb14bd69dd8cf30387aabb7f3a086918fc2e878ab05c",
+    "signals": "sha256:72dc15064cc45d7c51d2dd4b8c3a6d8c7d4155232d5d70d3c9e7697fab70ce50",
+    "trades": "sha256:2b8b9321e7993941ac486cf50cab5b4c6425b2571a4701f0eb02273ec9a96c53",
+    "positions": "sha256:b456a48fab51742b05084ca6dcaf01c03dfe5b39ad71215ada070e9b1f59f7ea",
+    "nav": "sha256:25649efce860b76410f87dbd36c81085887620086dc0b48b07099918f65d9c78",
+    "performance": "sha256:0856439ea7ec84e38887ccfa0067324f2e9cd293543e4ef683b9c2beb9eb34cf",
+    "attribution": "sha256:ad0f12f668d0a2d9ae5b3636d29989ab4bafcfb95f5de77abeb52a6d9e95d366",
+    "attribution_daily": "sha256:d4459ad650f88871d7b1e40392033037b5818ef92fdf1ee021868929fc1455a5",
+    "risk": "sha256:4816dd4812b5ff2e97e74bf34ca2221bfde675b387a96e277683557f0ad7d975"
+  }
+}

tests/fixtures/factor-contracts-v1.golden.json

@@ -0,0 +1,206 @@
+{
+  "dataset_snapshot": {
+    "contract_name": "researchhub.dataset-snapshot",
+    "schema_version": "1.0.0",
+    "snapshot_id": "rhdsv1:sha256:f63a29b4795c63fb7d6b2d3b5544cee9274b633db77c75c63d50a340c0827d57",
+    "descriptor": {
+      "dataset": {
+        "dataset_id": "rhdataset:market:0123456789abcdef0123456789abcdef",
+        "dataset_kind": "market",
+        "record_schema_version": "1.0.0",
+        "dimensions": ["instrument_id", "effective_time"]
+      },
+      "published_at": "2026-01-02T07:05:00Z",
+      "time_semantics": {
+        "effective_time": {
+          "start_inclusive": "2026-01-02T07:00:00Z",
+          "end_inclusive": "2026-01-02T07:00:00Z"
+        },
+        "knowledge_time": {
+          "start_inclusive": "2026-01-02T07:01:00Z",
+          "end_inclusive": "2026-01-02T07:01:00Z"
+        },
+        "pit_cutoff": "2026-01-02T07:01:00Z"
+      },
+      "content": {
+        "digest_algorithm": "sha256",
+        "canonicalization": "RFC8785",
+        "record_order": "canonical-record-byte-order",
+        "content_digest": "sha256:44ea11ba64dc2e6fd55c6d8e038c5edc84ee38d6fd46e38b15a1d5409662a020",
+        "logical_manifest": {
+          "record_count": 2,
+          "chunks": [
+            {
+              "chunk_index": 0,
+              "content_digest": "sha256:44ea11ba64dc2e6fd55c6d8e038c5edc84ee38d6fd46e38b15a1d5409662a020",
+              "record_count": 2
+            }
+          ]
+        },
+        "manifest_digest": "sha256:d991bb2f8f6b80525f93c51e0b371213a3ed4649dffb073ed4605bfbd32349bd",
+        "record_count": 2
+      },
+      "lineage": {
+        "publisher": {"id": "researchhub.data", "version": "1.0.0"},
+        "transformation": {
+          "id": "rhtransform:00112233445566778899aabbccddeeff",
+          "version": "1.0.0"
+        },
+        "upstream_snapshot_ids": [],
+        "upstream_content_digests": []
+      },
+      "quality": {
+        "status": "passed",
+        "checks": [
+          {
+            "check_id": "completeness",
+            "status": "passed",
+            "severity": "blocking",
+            "evidence_digest": "sha256:876fc2fcc6414ddc3f824a47f475d34c82a53d2bda5dc72a234a3f3164e8e2ec"
+          },
+          {
+            "check_id": "pit_time_integrity",
+            "status": "passed",
+            "severity": "blocking",
+            "evidence_digest": "sha256:90a6cc46b9f2ab317a1c6d14dc173784e19b5621cd7fe956e8f41338cbdc5944"
+          }
+        ]
+      },
+      "qualification": {
+        "status": "qualified",
+        "policy_id": "researchhub.dataset-snapshot.pit",
+        "policy_version": "1.0.0",
+        "evaluated_at": "2026-01-02T07:04:00Z",
+        "evidence_digest": "sha256:e192462f9022f2b477f73cdbe9e6c9f891ebcfdc2b4ed4f8ddd7b1ff107ee6a6"
+      }
+    }
+  },
+  "data_foundation": {
+    "contract_name": "researchhub.data-foundation",
+    "schema_version": "1.0.0",
+    "foundation_id": "rhdfv1:sha256:d848237ab753ee9432ae78ec1f93b6ac45c8072d6694023b7f288203daf9d838",
+    "dataset_snapshot_id": "rhdsv1:sha256:f63a29b4795c63fb7d6b2d3b5544cee9274b633db77c75c63d50a340c0827d57",
+    "pit_cutoff": "2026-01-03T00:00:00Z",
+    "instrument_routes": [
+      {
+        "route_revision_id": "rhroutev1:sha256:ca67013250e28ab4cce16570607379a8792400e62415ee6cb71c75508e2f3d86",
+        "instrument_id": "rhinstrument:0123456789abcdef0123456789abcdef",
+        "revision_number": 1,
+        "symbol": "600000",
+        "mic": "XSHG",
+        "currency": "CNY",
+        "asset_class": "equity",
+        "instrument_type": "stock",
+        "calendar_id": "rhcalendar:11112222333344445555666677778888",
+        "effective_from": "2020-01-01T00:00:00Z",
+        "knowledge_time": "2026-01-01T07:00:00Z",
+        "evidence_digest": "sha256:aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
+      }
+    ],
+    "trading_calendar_revisions": [
+      {
+        "calendar_revision_id": "rhcalv1:sha256:1f4ca22557063389badd669cf774bb35234066e847646682dccfe411e252a078",
+        "calendar_id": "rhcalendar:11112222333344445555666677778888",
+        "session_date": "2026-01-02",
+        "revision_number": 1,
+        "status": "open",
+        "sessions": [
+          {"opens_at": "2026-01-02T01:30:00Z", "closes_at": "2026-01-02T07:00:00Z"}
+        ],
+        "knowledge_time": "2026-01-01T08:00:00Z",
+        "evidence_digest": "sha256:bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb"
+      }
+    ],
+    "corporate_action_revisions": [
+      {
+        "action_revision_id": "rhcav1:sha256:0f947df29f152bfa2c6ab0da7a0d464670c7ad2526cd10f2a7939b42fee5c275",
+        "action_id": "rhaction:99998888777766665555444433332222",
+        "instrument_id": "rhinstrument:0123456789abcdef0123456789abcdef",
+        "revision_number": 1,
+        "action_type": "cash_dividend",
+        "status": "confirmed",
+        "effective_time": "2026-01-02T00:00:00Z",
+        "knowledge_time": "2026-01-01T09:00:00Z",
+        "terms_digest": "sha256:cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc",
+        "evidence_digest": "sha256:dddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddd"
+      }
+    ],
+    "standardized_views": [
+      {
+        "view_ref_id": "rhviewrefv1:sha256:bf776bcd26d940fafde1d650776a5505fb3fe8b5b068c351622bf2c42385629c",
+        "view_id": "rhview:abcdef0123456789abcdef0123456789",
+        "view_version": "1.0.0",
+        "dataset_snapshot_id": "rhdsv1:sha256:f63a29b4795c63fb7d6b2d3b5544cee9274b633db77c75c63d50a340c0827d57",
+        "pit_cutoff": "2026-01-03T00:00:00Z",
+        "schema_digest": "sha256:0123456789abcdef0123456789abcdef0123456789abcdef0123456789abcdef",
+        "content_digest": "sha256:123456789abcdef0123456789abcdef0123456789abcdef0123456789abcdef0",
+        "transformation_digest": "sha256:23456789abcdef0123456789abcdef0123456789abcdef0123456789abcdef01",
+        "instrument_route_revision_ids": [
+          "rhroutev1:sha256:ca67013250e28ab4cce16570607379a8792400e62415ee6cb71c75508e2f3d86"
+        ],
+        "trading_calendar_revision_ids": [
+          "rhcalv1:sha256:1f4ca22557063389badd669cf774bb35234066e847646682dccfe411e252a078"
+        ],
+        "corporate_action_revision_ids": [
+          "rhcav1:sha256:0f947df29f152bfa2c6ab0da7a0d464670c7ad2526cd10f2a7939b42fee5c275"
+        ]
+      }
+    ],
+    "revision_lineage": [
+      {
+        "revision_kind": "instrument_route",
+        "revision_id": "rhroutev1:sha256:ca67013250e28ab4cce16570607379a8792400e62415ee6cb71c75508e2f3d86",
+        "revision_number": 1,
+        "knowledge_time": "2026-01-01T07:00:00Z",
+        "evidence_digest": "sha256:aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
+      },
+      {
+        "revision_kind": "trading_calendar",
+        "revision_id": "rhcalv1:sha256:1f4ca22557063389badd669cf774bb35234066e847646682dccfe411e252a078",
+        "revision_number": 1,
+        "knowledge_time": "2026-01-01T08:00:00Z",
+        "evidence_digest": "sha256:bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb"
+      },
+      {
+        "revision_kind": "corporate_action",
+        "revision_id": "rhcav1:sha256:0f947df29f152bfa2c6ab0da7a0d464670c7ad2526cd10f2a7939b42fee5c275",
+        "revision_number": 1,
+        "knowledge_time": "2026-01-01T09:00:00Z",
+        "evidence_digest": "sha256:dddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddd"
+      }
+    ],
+    "readiness": {
+      "evidence_scope": "synthetic_fixture",
+      "contract_validation": {
+        "status": "validated",
+        "evidence_digests": [
+          "sha256:eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee"
+        ]
+      },
+      "real_data_validation": {"status": "not_validated", "evidence_digests": []},
+      "production_validation": {"status": "not_validated", "evidence_digests": []},
+      "live_validation": {"status": "not_validated", "evidence_digests": []}
+    }
+  },
+  "output_schema": {
+    "columns": ["evaluation_at", "factor_id", "instrument_id", "value"],
+    "schema_version": "1.0.0"
+  },
+  "output_content": {
+    "rows": [
+      {
+        "evaluation_at": "2026-01-03T11:00:00Z",
+        "factor_id": "alpha_005",
+        "instrument_id": "rhinstrument:0123456789abcdef0123456789abcdef",
+        "value": "0.125"
+      }
+    ]
+  },
+  "expected": {
+    "definition_id": "rhfactorv1:sha256:978fb8000d318373844a5e044ca14bf377e01ebe8d85964b826ecd2af9085ce9",
+    "input_schema_digest": "sha256:4501aeab99b4bcc25a1b8813ebe197fb498053fd710d73746bf20fc8eeb4bfa7",
+    "factor_set_id": "rhfactorsetv1:sha256:e9339581cf569e92459f672e8081337712e7bf98e58ad42d60d7ed13f9b5a021",
+    "output_artifact_id": "rhfactoroutputv1:sha256:a4803b5ff66d12d3a0e7e8e5b8cca953cbc137e2bf41514b7c4e5f05da5ee68b",
+    "legacy_binding_id": "rhlegacyfactorv1:sha256:541bc5a9469f9c8e4c2d696a9972fc5f2e6e2bef218b86f728823994b915dede"
+  }
+}

tests/fixtures/performance-evidence-v1.golden.json

@@ -0,0 +1,871 @@
+{
+  "cases": {
+    "absent": {
+      "artifact_available_at": "2026-01-08T02:05:00Z",
+      "authority": "quant_engine",
+      "backtest_evidence_manifest_document_sha256": "sha256:b7e9139e021de3122bee9376a8c8387fca3db75fe2a698adccf33471caf971d2",
+      "backtest_evidence_manifest_evidence_digest": "sha256:fae26a93d754e98f437bf9e4b635fc1cdc4f85d004a4bde396823b52e2115de5",
+      "backtest_evidence_manifest_id": "rhbacktestevidencev1:sha256:3f67fcad23c75684ffeb325d8405b2f81139a732e237d30fbb30d23f91e32726",
+      "backtest_evidence_qualification": "contract_qualified",
+      "backtest_run_ref_document_sha256": "sha256:6a798adb3e0568aca84ed3e3a285b92d181ec569462fc003952a8c0d8382ae3a",
+      "backtest_run_ref_id": "rhbacktestrunv1:sha256:5036c771c44a2adade9ea590eee0d8cf824ff8f519fadd8ba424e5d914856386",
+      "benchmark_alignment_policy": "none",
+      "benchmark_id": "",
+      "benchmark_series_digest": null,
+      "calendar": "CN-A",
+      "code_revision": "dddddddddddddddddddddddddddddddddddddddd",
+      "configuration_digest": "sha256:d28b49ea1bebc78d0023667f4fb9990b1fb45807176c2193b458525def056f4d",
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+      "cost_model_version": "1.0.0",
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+      "dataset_manifest_digest": "sha256:d991bb2f8f6b80525f93c51e0b371213a3ed4649dffb073ed4605bfbd32349bd",
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+      "document_sha256": "sha256:729852af307fd4a94ac566ae45df2e57b3d45c4fbdf45820351b5ef19cdf0ad3",
+      "end_date": "2026-01-08",
+      "environment_lock_digest": "sha256:9999999999999999999999999999999999999999999999999999999999999999",
+      "execution_model_digest": "sha256:7777777777777777777777777777777777777777777777777777777777777777",
+      "execution_model_version": "1.0.0",
+      "factor_output_content_digest": "sha256:d78751460dc27fc796163c462d946924ce2bcb45dcfceb5e4b77f76093befc9e",
+      "factor_set_digest": "sha256:e9339581cf569e92459f672e8081337712e7bf98e58ad42d60d7ed13f9b5a021",
+      "factor_set_id": "rhfactorsetv1:sha256:e9339581cf569e92459f672e8081337712e7bf98e58ad42d60d7ed13f9b5a021",
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+      "frequency": "1d",
+      "methodology": {
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+        "annual_risk_free": 0.0,
+        "annualized_return": "geometric_compound",
+        "annualized_volatility": "sample_std_sqrt_periods",
+        "benchmark_alignment": "none",
+        "benchmark_risk_free_daily": 0.0,
+        "beta": "sample_covariance_over_sample_variance",
+        "calmar_ratio": "unadjusted_annualized_return_over_absolute_maximum_drawdown",
+        "code_revision": "dddddddddddddddddddddddddddddddddddddddd",
+        "implementation_module": "quant_engine.metrics",
+        "implementation_version": "researchhub.quant-performance-methodology.v1",
+        "information_ratio": "mean_active_over_sample_std_active_sqrt_periods",
+        "maximum_drawdown": "non_positive_peak_to_trough_ratio_with_initial_nav_one",
+        "methodology_id": "researchhub.quant-performance-methodology.v1",
+        "periods_per_year": 252,
+        "return_type": "simple",
+        "sharpe_ratio": "annualized_return_minus_annual_risk_free_over_annualized_volatility",
+        "sortino_ratio": "annualized_return_minus_annual_risk_free_over_root_mean_square_negative_returns_sqrt_periods",
+        "source_frequency": "1d",
+        "total_return": "final_nav_minus_one",
+        "tracking_error": "sample_std_active_return_sqrt_periods",
+        "win_rate": "positive_daily_return_count_over_observation_count"
+      },
+      "methodology_id": "researchhub.quant-performance-methodology.v1",
+      "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+      "metrics": [
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+          "availability": "available",
+          "key": "total_return",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "total_ret",
+          "unit": "ratio",
+          "value": 0.575
+        },
+        {
+          "availability": "available",
+          "key": "annualized_return",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "ann_ret",
+          "unit": "ratio_per_year",
+          "value": 2683336646708.1
+        },
+        {
+          "availability": "available",
+          "key": "annualized_volatility",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "ann_volatility",
+          "unit": "ratio_per_year",
+          "value": 1.38901943830891
+        },
+        {
+          "availability": "available",
+          "key": "sharpe_ratio",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "sharpe",
+          "unit": "ratio",
+          "value": 1931820803008.3313
+        },
+        {
+          "availability": "available",
+          "key": "sortino_ratio",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "sortino",
+          "unit": "ratio",
+          "value": 0.0
+        },
+        {
+          "availability": "available",
+          "key": "maximum_drawdown",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "max_dd",
+          "unit": "ratio",
+          "value": 0.0
+        },
+        {
+          "availability": "available",
+          "key": "calmar_ratio",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "calmar",
+          "unit": "ratio",
+          "value": 0.0
+        },
+        {
+          "availability": "available",
+          "key": "win_rate",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "win_rate",
+          "unit": "ratio",
+          "value": 0.75
+        },
+        {
+          "availability": "benchmark_absent",
+          "key": "tracking_error",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": true,
+          "source_column": "tracking_error",
+          "unit": "ratio_per_year",
+          "value": null
+        },
+        {
+          "availability": "benchmark_absent",
+          "key": "information_ratio",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": true,
+          "source_column": "ir",
+          "unit": "ratio",
+          "value": null
+        },
+        {
+          "availability": "benchmark_absent",
+          "key": "alpha",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": true,
+          "source_column": "alpha",
+          "unit": "ratio_per_year",
+          "value": null
+        },
+        {
+          "availability": "benchmark_absent",
+          "key": "beta",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": true,
+          "source_column": "beta",
+          "unit": "ratio",
+          "value": null
+        },
+        {
+          "availability": "available",
+          "key": "trade_count",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "n_trades",
+          "unit": "count",
+          "value": 3
+        },
+        {
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+          "nullable": false,
+          "source_column": "n_trades",
+          "unit": "count",
+          "value": 3
+        },
+        {
+          "availability": "available",
+          "key": "day_count",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "n_days",
+          "unit": "count",
+          "value": 4
+        }
+      ],
+      "performance_evidence_id": "rhperformanceevidencev1:sha256:e6730d25d90c85a6370adb72bc45c1bf505235f77b72521dd8197898ca369fe5",
+      "performance_row_digest": "sha256:6528c47fa9416e350aa5010477dbd8c83a35cecf87aa5bc413cec95a9765114d",
+      "performance_table_content_digest": "sha256:7d57a966431c68093a9f6193ae62f79a63d33832f11dd68524ed9bcd2cb6257c",
+      "performance_table_logical_name": "performance",
+      "performance_table_row_count": 1,
+      "performance_table_schema_digest": "sha256:16cef93a679761103ae405e622b7929abbfe07115bf276be4f164da5a16128d0",
+      "research_artifact_content_digest": "sha256:b17ab9e158a7ceeb5107f6fe8a61f332009c314186f3436d4750aa44d6ca3856",
+      "research_artifact_schema_version": "1.1.0",
+      "run_id": "rhbacktestrunv1:sha256:5036c771c44a2adade9ea590eee0d8cf824ff8f519fadd8ba424e5d914856386",
+      "schema_version": "researchhub.performance-evidence.v1",
+      "scope": "offline_research_only",
+      "start_date": "2026-01-05",
+      "strategy_digest": "sha256:6666666666666666666666666666666666666666666666666666666666666666",
+      "strategy_id": "alpha-top1",
+      "strategy_version": "1.0.0",
+      "timezone": "Asia/Shanghai"
+    },
+    "zero_benchmark_variance": {
+      "artifact_available_at": "2026-01-08T02:05:00Z",
+      "authority": "quant_engine",
+      "backtest_evidence_manifest_document_sha256": "sha256:2389b804f5d8d20a37b31f596b52892484777c5da799dd68865a082ee90e6e5b",
+      "backtest_evidence_manifest_evidence_digest": "sha256:54bb315bc1940ef6df79999cf03ebb8d10defaf526bf7802d63da33f612520ec",
+      "backtest_evidence_manifest_id": "rhbacktestevidencev1:sha256:ad9559e1f8feca28bb310765216a975935f05f51057139c2d02fd2fe7dfe501e",
+      "backtest_evidence_qualification": "contract_qualified",
+      "backtest_run_ref_document_sha256": "sha256:6a798adb3e0568aca84ed3e3a285b92d181ec569462fc003952a8c0d8382ae3a",
+      "backtest_run_ref_id": "rhbacktestrunv1:sha256:5036c771c44a2adade9ea590eee0d8cf824ff8f519fadd8ba424e5d914856386",
+      "benchmark_alignment_policy": "exact_session_index",
+      "benchmark_id": "000300.SH",
+      "benchmark_series_digest": "sha256:d0f1e954d796e95254bcccc893d6e3a8f9d541c3028b88b2c0b24c1658bb2408",
+      "calendar": "CN-A",
+      "code_revision": "dddddddddddddddddddddddddddddddddddddddd",
+      "configuration_digest": "sha256:d28b49ea1bebc78d0023667f4fb9990b1fb45807176c2193b458525def056f4d",
+      "cost_model_digest": "sha256:8888888888888888888888888888888888888888888888888888888888888888",
+      "cost_model_version": "1.0.0",
+      "dataset_content_digest": "sha256:44ea11ba64dc2e6fd55c6d8e038c5edc84ee38d6fd46e38b15a1d5409662a020",
+      "dataset_manifest_digest": "sha256:d991bb2f8f6b80525f93c51e0b371213a3ed4649dffb073ed4605bfbd32349bd",
+      "dataset_snapshot_id": "rhdsv1:sha256:f63a29b4795c63fb7d6b2d3b5544cee9274b633db77c75c63d50a340c0827d57",
+      "document_sha256": "sha256:077f3ec84802aff2f046365a3a5f61f475b1ad63ec05e96742844bc531bcb551",
+      "end_date": "2026-01-08",
+      "environment_lock_digest": "sha256:9999999999999999999999999999999999999999999999999999999999999999",
+      "execution_model_digest": "sha256:7777777777777777777777777777777777777777777777777777777777777777",
+      "execution_model_version": "1.0.0",
+      "factor_output_content_digest": "sha256:d78751460dc27fc796163c462d946924ce2bcb45dcfceb5e4b77f76093befc9e",
+      "factor_set_digest": "sha256:e9339581cf569e92459f672e8081337712e7bf98e58ad42d60d7ed13f9b5a021",
+      "factor_set_id": "rhfactorsetv1:sha256:e9339581cf569e92459f672e8081337712e7bf98e58ad42d60d7ed13f9b5a021",
+      "foundation_digest": "sha256:d848237ab753ee9432ae78ec1f93b6ac45c8072d6694023b7f288203daf9d838",
+      "foundation_id": "rhdfv1:sha256:d848237ab753ee9432ae78ec1f93b6ac45c8072d6694023b7f288203daf9d838",
+      "frequency": "1d",
+      "methodology": {
+        "alpha": "daily_ols_intercept_geometric_annualization",
+        "annual_risk_free": 0.0,
+        "annualized_return": "geometric_compound",
+        "annualized_volatility": "sample_std_sqrt_periods",
+        "benchmark_alignment": "exact_session_index",
+        "benchmark_risk_free_daily": 0.0,
+        "beta": "sample_covariance_over_sample_variance",
+        "calmar_ratio": "unadjusted_annualized_return_over_absolute_maximum_drawdown",
+        "code_revision": "dddddddddddddddddddddddddddddddddddddddd",
+        "implementation_module": "quant_engine.metrics",
+        "implementation_version": "researchhub.quant-performance-methodology.v1",
+        "information_ratio": "mean_active_over_sample_std_active_sqrt_periods",
+        "maximum_drawdown": "non_positive_peak_to_trough_ratio_with_initial_nav_one",
+        "methodology_id": "researchhub.quant-performance-methodology.v1",
+        "periods_per_year": 252,
+        "return_type": "simple",
+        "sharpe_ratio": "annualized_return_minus_annual_risk_free_over_annualized_volatility",
+        "sortino_ratio": "annualized_return_minus_annual_risk_free_over_root_mean_square_negative_returns_sqrt_periods",
+        "source_frequency": "1d",
+        "total_return": "final_nav_minus_one",
+        "tracking_error": "sample_std_active_return_sqrt_periods",
+        "win_rate": "positive_daily_return_count_over_observation_count"
+      },
+      "methodology_id": "researchhub.quant-performance-methodology.v1",
+      "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+      "metrics": [
+        {
+          "availability": "available",
+          "key": "total_return",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "total_ret",
+          "unit": "ratio",
+          "value": 0.575
+        },
+        {
+          "availability": "available",
+          "key": "annualized_return",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "ann_ret",
+          "unit": "ratio_per_year",
+          "value": 2683336646708.1
+        },
+        {
+          "availability": "available",
+          "key": "annualized_volatility",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "ann_volatility",
+          "unit": "ratio_per_year",
+          "value": 1.38901943830891
+        },
+        {
+          "availability": "available",
+          "key": "sharpe_ratio",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "sharpe",
+          "unit": "ratio",
+          "value": 1931820803008.3313
+        },
+        {
+          "availability": "available",
+          "key": "sortino_ratio",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "sortino",
+          "unit": "ratio",
+          "value": 0.0
+        },
+        {
+          "availability": "available",
+          "key": "maximum_drawdown",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "max_dd",
+          "unit": "ratio",
+          "value": 0.0
+        },
+        {
+          "availability": "available",
+          "key": "calmar_ratio",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "calmar",
+          "unit": "ratio",
+          "value": 0.0
+        },
+        {
+          "availability": "available",
+          "key": "win_rate",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "win_rate",
+          "unit": "ratio",
+          "value": 0.75
+        },
+        {
+          "availability": "available",
+          "key": "tracking_error",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": true,
+          "source_column": "tracking_error",
+          "unit": "ratio_per_year",
+          "value": 1.38901943830891
+        },
+        {
+          "availability": "available",
+          "key": "information_ratio",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": true,
+          "source_column": "ir",
+          "unit": "ratio",
+          "value": 22.299903907544408
+        },
+        {
+          "availability": "not_estimable_benchmark_variance",
+          "key": "alpha",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": true,
+          "source_column": "alpha",
+          "unit": "ratio_per_year",
+          "value": null
+        },
+        {
+          "availability": "not_estimable_benchmark_variance",
+          "key": "beta",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": true,
+          "source_column": "beta",
+          "unit": "ratio",
+          "value": null
+        },
+        {
+          "availability": "available",
+          "key": "trade_count",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "n_trades",
+          "unit": "count",
+          "value": 3
+        },
+        {
+          "availability": "available",
+          "key": "day_count",
+          "methodology_id": "researchhub.quant-performance-methodology.v1",
+          "metric_schema_id": "researchhub.quant-performance-metrics.v1",
+          "nullable": false,
+          "source_column": "n_days",
+          "unit": "count",
+          "value": 4
+        }
+      ],
+      "performance_evidence_id": "rhperformanceevidencev1:sha256:86133a6b3c3091477cdc4618ebc294e671c9ba0a3ef5015a068f92bb575729b9",
+      "performance_row_digest": "sha256:90c9aa2f4168c15ff9ac5d4da2a35c3e915bb4e04736e5a440ea80c787a99553",
+      "performance_table_content_digest": "sha256:1570b64d83ad2a849607e6f5203f5ec2f0291a8d6cbb00b8edfe0ae030b7967d",
+      "performance_table_logical_name": "performance",
+      "performance_table_row_count": 1,
+      "performance_table_schema_digest": "sha256:16cef93a679761103ae405e622b7929abbfe07115bf276be4f164da5a16128d0",
+      "research_artifact_content_digest": "sha256:fed7a28888a6e98ccce78e7d84f22d4e3636e928485861af7a97f90eddc91f94",
+      "research_artifact_schema_version": "1.1.0",
+      "run_id": "rhbacktestrunv1:sha256:5036c771c44a2adade9ea590eee0d8cf824ff8f519fadd8ba424e5d914856386",
+      "schema_version": "researchhub.performance-evidence.v1",
+      "scope": "offline_research_only",
+      "start_date": "2026-01-05",
+      "strategy_digest": "sha256:6666666666666666666666666666666666666666666666666666666666666666",
+      "strategy_id": "alpha-top1",
+      "strategy_version": "1.0.0",
+      "timezone": "Asia/Shanghai"
+    }
+  },
+  "schema_version": 1,
+  "source_commit": "a724e1e57a99d1304a932d01ee836bac56c5c15c",
+  "source_tree": "4774e88442d25bf79a54eab3d7106ff4d0ba9603"
+}

tests/fixtures/portfolio-risk-computation-v1.golden.json

@@ -0,0 +1,129 @@
+{
+  "portfolio_decision": {
+    "computed_at": "2026-01-08T03:01:00Z",
+    "constraint_residuals": {
+      "gross_exposure_max": 0.0,
+      "net_exposure_max": 0.0,
+      "net_exposure_min": 0.0,
+      "position_count_max": 0.0,
+      "single_asset_max": 0.0,
+      "single_asset_min": 0.0,
+      "turnover_max": 0.0
+    },
+    "constraints": {
+      "gross_exposure_max": 1.0,
+      "net_exposure_max": 1.0,
+      "net_exposure_min": 1.0,
+      "position_count_max": 2,
+      "schema_version": "1.0.0",
+      "single_asset_max": 0.7,
+      "single_asset_min": 0.2,
+      "turnover_max": 0.2
+    },
+    "contract_name": "researchhub.portfolio-decision",
+    "covariance_digest": "sha256:aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
+    "dataset_snapshot_id": "rhdsv1:sha256:f63a29b4795c63fb7d6b2d3b5544cee9274b633db77c75c63d50a340c0827d57",
+    "decision_id": "rhportfoliodecisionv1:sha256:0e6e5ce2fa08de8006cc392610695a013327fc80645bfa4d7a908ad3f615bebd",
+    "effective_at": "2026-01-08T03:00:00Z",
+    "evidence_digest": "sha256:f3913894d032c389c64eef59058b3cbc694cb9cc14ce9cee2699f0068200b650",
+    "expected_return_digest": "sha256:dddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddd",
+    "freshness_policy": {
+      "max_covariance_age_days": 0,
+      "max_manifest_age_seconds": 3600,
+      "schema_version": "1.0.0"
+    },
+    "gross_exposure": 1.0,
+    "manifest_document_sha256": "fbf54218f770528978f9ccd35577e1ab00877143ea397a576e071e75f0afbab0",
+    "manifest_id": "rhbacktestevidencev1:sha256:681c49cbdfb3e221b273e7bc616602ad80a7ab01206970807ad4294b176ebc75",
+    "model_digest": "sha256:cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc",
+    "model_name": "deterministic_weights",
+    "model_version": "1.0.0",
+    "net_exposure": 1.0,
+    "objective_digest": "sha256:bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb",
+    "objective_name": "long_only_allocation",
+    "objective_version": "1.0.0",
+    "output_digest": "sha256:bd3b964c628c8648322d036e57dd6f444ca287017d1578bab3689d07d32b28ce",
+    "portfolio_asset_set_digest": "sha256:b64e3448a83a5b86466465080361c1a7e1157a27ddccd4b68069cb18caffb74a",
+    "position_count": 2,
+    "prior_weights": {
+      "A": 0.5,
+      "B": 0.5
+    },
+    "receipt": {
+      "algorithm": "bounded_allocation",
+      "algorithm_version": "1.0.0",
+      "computed_at": "2026-01-08T03:01:00Z",
+      "constraint_digest": "sha256:34df0e5c00f503748ff936f9cd415a8947169181f8ca0917bce97dd606e08e94",
+      "implementation_digest": "sha256:ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff",
+      "input_digest": "sha256:ebd8ff957115e1adfd84eafa5cea49470356194d747b64ee5897858f9dd067b7",
+      "iterations": null,
+      "max_constraint_residual": 0.0,
+      "objective_value": null,
+      "output_digest": "sha256:bd3b964c628c8648322d036e57dd6f444ca287017d1578bab3689d07d32b28ce",
+      "parameter_digest": "sha256:0000000000000000000000000000000000000000000000000000000000000000",
+      "schema_version": "1.0.0",
+      "solver_config_digest": null,
+      "solver_name": null,
+      "solver_required": false,
+      "solver_version": null,
+      "status": "completed",
+      "tolerance": 1e-12
+    },
+    "run_id": "rhbacktestrunv1:sha256:5036c771c44a2adade9ea590eee0d8cf824ff8f519fadd8ba424e5d914856386",
+    "run_ref_document_sha256": "6a798adb3e0568aca84ed3e3a285b92d181ec569462fc003952a8c0d8382ae3a",
+    "scenario_digest": "sha256:eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee",
+    "schema_version": "1.0.0",
+    "source_universe_digest": "sha256:5555555555555555555555555555555555555555555555555555555555555555",
+    "target_id": "portfolio-target:synthetic-v1",
+    "target_weights": {
+      "A": 0.6,
+      "B": 0.4
+    },
+    "turnover_l1": 0.19999999999999996
+  },
+  "risk_assessment": {
+    "assessment_id": "rhriskassessmentv1:sha256:dbc38825cffcf6d95bd0216d22dbba4e0d4a1359ca5924a3ee529b99a7d78b6d",
+    "component_risk": {
+      "A": 1.4549226783578566,
+      "B": 1.4549226783578568
+    },
+    "contract_name": "researchhub.risk-assessment",
+    "covariance_as_of_date": "2026-01-08",
+    "covariance_data_snapshot_id": "rhdsv1:sha256:f63a29b4795c63fb7d6b2d3b5544cee9274b633db77c75c63d50a340c0827d57",
+    "covariance_input_digest": "sha256:aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
+    "covariance_snapshot_id": "covariance:synthetic-v1",
+    "dataset_snapshot_id": "rhdsv1:sha256:f63a29b4795c63fb7d6b2d3b5544cee9274b633db77c75c63d50a340c0827d57",
+    "decision_id": "rhportfoliodecisionv1:sha256:0e6e5ce2fa08de8006cc392610695a013327fc80645bfa4d7a908ad3f615bebd",
+    "findings": [],
+    "freshness_policy_digest": "sha256:833f58f4d1056f0450f7369fd4edd70534cc74e9e55cd60f05b2b3d7a2979763",
+    "group_exposure": {
+      "equity": 1.4549226783578566,
+      "fixed_income": 1.4549226783578568
+    },
+    "manifest_id": "rhbacktestevidencev1:sha256:681c49cbdfb3e221b273e7bc616602ad80a7ab01206970807ad4294b176ebc75",
+    "marginal_risk": {
+      "A": 2.424871130596428,
+      "B": 3.637306695894642
+    },
+    "percentage_risk": {
+      "A": 0.49999999999999983,
+      "B": 0.49999999999999994
+    },
+    "periods_per_year": 252,
+    "portfolio_volatility": 2.909845356715714,
+    "portfolio_volatility_limit": 10.0,
+    "qualified": true,
+    "return_frequency": "1d",
+    "risk_budget": {
+      "A": 0.8,
+      "B": 0.8
+    },
+    "risk_model_digest": "sha256:2222222222222222222222222222222222222222222222222222222222222222",
+    "risk_model_name": "euler_volatility",
+    "risk_model_version": "1.0.0",
+    "run_id": "rhbacktestrunv1:sha256:5036c771c44a2adade9ea590eee0d8cf824ff8f519fadd8ba424e5d914856386",
+    "scenario_digest": "sha256:eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee",
+    "schema_version": "1.0.0",
+    "status": "ready"
+  }
+}

tests/governance/test_module_spec.py

@@ -1,32 +1,77 @@
 from __future__ import annotations
 
 import json
-import unittest
 from pathlib import Path
 
 
 ROOT = Path(__file__).resolve().parents[2]
 
 
-class ModuleSpecTests(unittest.TestCase):
+def test_module_spec_declares_pure_research_engine_boundary() -> None:
-    def test_module_spec_declares_pure_research_engine_boundary(self) -> None:
     spec = json.loads((ROOT / "MODULE_SPEC.yaml").read_text(encoding="utf-8"))
-        self.assertEqual(spec["module_id"], "quant_engine")
+    assert spec["module_id"] == "quant_engine"
-        self.assertEqual(spec["authority"]["subject"], spec["module_id"])
+    assert spec["authority"]["subject"] == spec["module_id"]
-        self.assertEqual(spec["repository"]["type"], "research_engine")
+    assert spec["repository"]["type"] == "research_engine"
-        self.assertEqual(spec["bounded_context"]["domain"], "quantitative-research-engine")
+    assert spec["bounded_context"]["domain"] == "quantitative-research-engine"
     prohibited = " ".join(spec["bounded_context"]["prohibited_responsibilities"]).lower()
     for term in ("investment advice", "live order", "credentials", "source facts"):
-            self.assertIn(term, prohibited)
+        assert term in prohibited
-        self.assertEqual(spec["contracts"], {"provides": [], "consumes": []})
+    assert spec["authority"]["revision"] == 5
-        self.assertEqual(spec["dependencies"], [])
+    assert {
-        self.assertTrue(
+        (item["contract_id"], item["version"])
-            all(
+        for item in spec["contracts"]["provides"]
+    } == {
+        ("researchhub.factor-definition", "1.0.0"),
+        ("researchhub.factor-set-ref", "1.0.0"),
+        ("researchhub.backtest-run-ref", "1.0.0"),
+        ("researchhub.backtest-evidence-manifest", "1.0.0"),
+        ("researchhub.performance-evidence", "1.0.0"),
+        ("researchhub.portfolio-decision", "1.0.0"),
+        ("researchhub.risk-assessment", "1.0.0"),
+    }
+    expected_paths = {
+        "researchhub.factor-definition": "src/quant_engine/factor_contracts.py",
+        "researchhub.factor-set-ref": "src/quant_engine/factor_contracts.py",
+        "researchhub.backtest-run-ref": "src/quant_engine/governed_pipeline.py",
+        "researchhub.backtest-evidence-manifest": "src/quant_engine/artifact.py",
+        "researchhub.performance-evidence": "src/quant_engine/artifact.py",
+        "researchhub.portfolio-decision": "src/quant_engine/portfolio_risk_contracts.py",
+        "researchhub.risk-assessment": "src/quant_engine/portfolio_risk_contracts.py",
+    }
+    assert all(item["authority"] == "quant_engine" for item in spec["contracts"]["provides"])
+    assert {
+        item["contract_id"]: item["path"] for item in spec["contracts"]["provides"]
+    } == expected_paths
+    assert {
+        (item["contract_id"], item["version"])
+        for item in spec["contracts"]["consumes"]
+    } == {
+        ("researchhub.dataset-snapshot", "1.0.0"),
+        ("researchhub.data-foundation", "1.0.0"),
+    }
+    assert all(
+        item["authority"] == "researchhub.data"
+        for item in spec["contracts"]["consumes"]
+    )
+    assert spec["dependencies"] == []
+    capabilities = {item["id"]: item for item in spec["capabilities"]}
+    evidence_contract = capabilities["backtest-evidence-contracts"]
+    assert evidence_contract["status"] == "operational"
+    evidence_summary = evidence_contract["summary"].lower()
+    for term in ("performance-methodology", "without recomputation", "decision authority"):
+        assert term in evidence_summary
+    portfolio_contract = capabilities["portfolio-risk-computation-contracts"]
+    assert portfolio_contract["status"] == "operational"
+    summary = portfolio_contract["summary"].lower()
+    for term in ("receipt", "portfolio decisions", "risk assessments", "without"):
+        assert term in summary
+    for term in ("approval", "maker-checker", "publication", "paper", "live"):
+        assert term in prohibited
+    assert all(
         command["required"] and not command["network"]
         for command in spec["verification"]["commands"]
     )
-        )
 
 
 if __name__ == "__main__":
-    unittest.main()
+    test_module_spec_declares_pure_research_engine_boundary()

tests/test_alpha_factors.py

@@ -7,6 +7,13 @@ import pandas as pd
 import pytest
 
 import quant_engine.alpha_factors as alpha_factors_module
+from quant_engine.factor_contracts import (
+    FactorContractError,
+    FactorInput,
+    ProducerIdentity,
+    factor_definition_from_alpha158,
+    factor_input_schema_digest,
+)
 from quant_engine.alpha_factors import (
     ALPHA158_REGISTRY,
     ALPHA158_PHASE1_OPERATOR_SPECS,
@@ -20,6 +27,9 @@ from quant_engine.alpha_factors import (
     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,
@@ -183,11 +193,13 @@ from quant_engine.alpha_factors import (
     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,
@@ -429,6 +441,50 @@ def test_alpha_registry_required_fields():
         assert required <= set(meta.keys()), f"{alpha_id} missing fields"
 
 
+def test_alpha_registry_adapts_to_definition_without_copying_formula_or_inputs():
+    factor_input = FactorInput(
+        "market",
+        "sha256:" + "1" * 64,
+        tuple(ALPHA158_REGISTRY["alpha_005"]["inputs"]),
+    )
+    definition = factor_definition_from_alpha158(
+        "alpha_005",
+        version="1.0.0",
+        parameters={},
+        inputs=(factor_input,),
+        implementation_digest="sha256:" + "2" * 64,
+        input_schema_digest=factor_input_schema_digest((factor_input,)),
+        valid_from="2026-01-01T00:00:00Z",
+        valid_until="2027-01-01T00:00:00Z",
+        warmup_sessions=10,
+        lag_sessions=1,
+        producer=ProducerIdentity("quant_engine", "1.0.0"),
+        code_revision="c" * 40,
+    )
+
+    assert definition.formula == ALPHA158_REGISTRY["alpha_005"]["formula"]
+    assert definition.inputs[0].required_columns == tuple(
+        ALPHA158_REGISTRY["alpha_005"]["inputs"]
+    )
+
+    incomplete = FactorInput("market", "sha256:" + "1" * 64, ("close",))
+    with pytest.raises(FactorContractError, match="exactly correspond"):
+        factor_definition_from_alpha158(
+            "alpha_005",
+            version="1.0.0",
+            parameters={},
+            inputs=(incomplete,),
+            implementation_digest="sha256:" + "2" * 64,
+            input_schema_digest=factor_input_schema_digest((incomplete,)),
+            valid_from="2026-01-01T00:00:00Z",
+            valid_until="2027-01-01T00:00:00Z",
+            warmup_sessions=10,
+            lag_sessions=1,
+            producer=ProducerIdentity("quant_engine", "1.0.0"),
+            code_revision="c" * 40,
+        )
+
+
 def test_get_alpha_meta_success():
     """已知 alpha_id 返回完整 meta。"""
     meta = get_alpha_meta("alpha_001")
@@ -1962,3 +2018,193 @@ def test_phase5_contract_is_publicly_exported():
         "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_backtest_contracts.py

@@ -0,0 +1,773 @@
+"""Backtest run-reference and closed-evidence contract conformance."""
+
+from __future__ import annotations
+
+import copy
+import hashlib
+import json
+from dataclasses import replace
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+import pytest
+
+from quant_engine.artifact import (
+    BacktestEvidenceManifest,
+    EvidenceQualification,
+    RESEARCH_ARTIFACT_SCHEMA_VERSION,
+    ResearchRunArtifact,
+    build_backtest_evidence_manifest,
+    build_legacy_backtest_evidence_manifest,
+    build_research_run_artifact,
+)
+from quant_engine.execution import ExecutionConfig
+from quant_engine.factor_contracts import (
+    ActorIdentity,
+    AvailabilityMode,
+    Causation,
+    DataFoundationEnvelope,
+    DatasetSnapshotEnvelope,
+    FactorInput,
+    FactorSetRef,
+    InputBinding,
+    OutputArtifactRef,
+    OutputCoverage,
+    OutputQuality,
+    OutputQualityCheck,
+    ProducerIdentity,
+    ViewAvailability,
+    canonical_json_bytes,
+    factor_definition_from_alpha158,
+    factor_input_schema_digest,
+)
+from quant_engine.governed_pipeline import (
+    BacktestContractError,
+    BacktestContractErrorCode,
+    BacktestRun,
+    BacktestRunRef,
+)
+from quant_engine.research_pipeline import FactorBacktestResult, run_factor_backtest_research
+
+
+ROOT = Path(__file__).resolve().parents[1]
+FACTOR_FIXTURE = ROOT / "tests" / "fixtures" / "factor-contracts-v1.golden.json"
+BACKTEST_FIXTURE = ROOT / "tests" / "fixtures" / "backtest-evidence-v1.golden.json"
+VIEW_REF_ID = "rhviewrefv1:sha256:bf776bcd26d940fafde1d650776a5505fb3fe8b5b068c351622bf2c42385629c"
+VIEW_SCHEMA_DIGEST = "sha256:0123456789abcdef0123456789abcdef0123456789abcdef0123456789abcdef"
+CALENDAR_REVISION_ID = "rhcalv1:sha256:1f4ca22557063389badd669cf774bb35234066e847646682dccfe411e252a078"
+ACTION_REVISION_ID = "rhcav1:sha256:0f947df29f152bfa2c6ab0da7a0d464670c7ad2526cd10f2a7939b42fee5c275"
+PARAMETERS = {"lag_sessions": 1, "top_k": 1}
+
+
+def _sha256(value: bytes) -> str:
+    return f"sha256:{hashlib.sha256(value).hexdigest()}"
+
+
+def _accepted_authorities(
+    *,
+    factor_evaluation_at: str = "2026-01-03T11:00:00Z",
+    factor_computed_at: str = "2026-01-03T10:15:00Z",
+    factor_artifact_available_at: str = "2026-01-03T10:20:00Z",
+    factor_availability_mode: AvailabilityMode = AvailabilityMode.AS_AVAILABLE,
+) -> tuple[
+    DatasetSnapshotEnvelope,
+    DataFoundationEnvelope,
+    FactorSetRef,
+]:
+    fixture = json.loads(FACTOR_FIXTURE.read_text(encoding="utf-8"))
+    snapshot = DatasetSnapshotEnvelope.from_dict(fixture["dataset_snapshot"])
+    foundation = DataFoundationEnvelope.from_dict(fixture["data_foundation"])
+    factor_input = FactorInput("market", VIEW_SCHEMA_DIGEST, ("close", "volume"))
+    definition = factor_definition_from_alpha158(
+        "alpha_005",
+        version="1.0.0",
+        parameters={},
+        inputs=(factor_input,),
+        implementation_digest="sha256:" + "1" * 64,
+        input_schema_digest=factor_input_schema_digest((factor_input,)),
+        valid_from="2026-01-01T00:00:00.000000Z",
+        valid_until="2027-01-01T00:00:00Z",
+        warmup_sessions=10,
+        lag_sessions=1,
+        producer=ProducerIdentity("quant_engine", "1.0.0"),
+        code_revision="c" * 40,
+    )
+    output_schema_bytes = canonical_json_bytes(fixture["output_schema"])
+    output_content_bytes = canonical_json_bytes(fixture["output_content"])
+    artifact_ref = OutputArtifactRef.create(
+        schema_digest=_sha256(output_schema_bytes),
+        content_digest=_sha256(output_content_bytes),
+    )
+    factor_set = FactorSetRef.create(
+        definitions=(definition,),
+        dataset_snapshot=snapshot,
+        foundation=foundation,
+        selected_view_ref_ids=(VIEW_REF_ID,),
+        input_bindings=(
+            InputBinding(definition.definition_id, "market", VIEW_REF_ID, VIEW_SCHEMA_DIGEST),
+        ),
+        view_availability=(
+            ViewAvailability(VIEW_REF_ID, "2026-01-02T23:50:00Z", "sha256:" + "2" * 64),
+        ),
+        output_quality=OutputQuality(
+            "passed",
+            (OutputQualityCheck("finite_values", "passed", "sha256:" + "3" * 64),),
+        ),
+        output_coverage=OutputCoverage(
+            "complete", 1, 1, "row", "alpha_005.cn_a", "sha256:" + "4" * 64
+        ),
+        output_schema_bytes=output_schema_bytes,
+        output_content_bytes=output_content_bytes,
+        output_artifact_ref=artifact_ref,
+        availability_mode=factor_availability_mode,
+        evaluation_at=factor_evaluation_at,
+        computed_at=factor_computed_at,
+        artifact_available_at=factor_artifact_available_at,
+        producer=ProducerIdentity("quant_engine", "1.0.0"),
+        code_revision="c" * 40,
+        actor=ActorIdentity("service", "factor_worker_v1"),
+        correlation_id="research_run_001",
+        causation=Causation("foundation", foundation.foundation_id),
+        evidence_scope="synthetic_fixture",
+        decision_eligible=False,
+    )
+    return snapshot, foundation, factor_set
+
+
+def _config_digest(parameters: dict[str, object] | None = None) -> str:
+    encoded = json.dumps(
+        PARAMETERS if parameters is None else parameters,
+        ensure_ascii=False,
+        sort_keys=True,
+        separators=(",", ":"),
+        allow_nan=False,
+    ).encode("utf-8")
+    return _sha256(encoded)
+
+
+def _run_ref(**overrides: Any) -> BacktestRunRef:
+    snapshot, foundation, factor_set = _accepted_authorities()
+    arguments: dict[str, Any] = {
+        "dataset_snapshot": snapshot,
+        "foundation": foundation,
+        "factor_set": factor_set,
+        "universe_digest": "sha256:" + "5" * 64,
+        "trading_calendar_revision_ids": (CALENDAR_REVISION_ID,),
+        "corporate_action_revision_ids": (ACTION_REVISION_ID,),
+        "strategy_id": "alpha-top1",
+        "strategy_version": "1.0.0",
+        "strategy_digest": "sha256:" + "6" * 64,
+        "execution_model_version": "1.0.0",
+        "execution_model_digest": "sha256:" + "7" * 64,
+        "cost_model_version": "1.0.0",
+        "cost_model_digest": "sha256:" + "8" * 64,
+        "random_seed": 7,
+        "code_revision": "d" * 40,
+        "environment_lock_digest": "sha256:" + "9" * 64,
+        "configuration_digest": _config_digest(),
+        "evaluation_at": "2026-01-08T01:00:00Z",
+        "computed_at": "2026-01-08T02:00:00Z",
+    }
+    arguments.update(overrides)
+    return BacktestRunRef.create(**arguments)
+
+
+def _backtest_result() -> FactorBacktestResult:
+    dates = pd.date_range("2026-01-05", periods=4, freq="B")
+    scores = pd.DataFrame({"A": [2.0, 0.0], "B": [1.0, 3.0]}, index=dates[:2])
+    opens = pd.DataFrame(
+        {"A": [10.0, 10.0, 15.0, 15.0], "B": [20.0, 20.0, 20.0, 21.0]},
+        index=dates,
+    )
+    closes = pd.DataFrame(
+        {"A": [10.0, 12.0, 15.0, 15.0], "B": [20.0, 20.0, 18.0, 21.0]},
+        index=dates,
+    )
+    return run_factor_backtest_research(
+        scores,
+        opens,
+        closes,
+        top_k=1,
+        execution_price_field="open",
+        valuation_price_field="close",
+        initial_cash=1_000.0,
+        config=ExecutionConfig(
+            commission_bps=0,
+            stamp_tax_bps=0,
+            slippage_bps=0,
+            min_trade_amount=0,
+        ),
+    )
+
+
+def _artifact(run_ref: BacktestRunRef, *, run_id: str | None = None) -> ResearchRunArtifact:
+    result = _backtest_result()
+    benchmark = pd.Series(
+        [0.0, 0.01, -0.01, 0.02],
+        index=result.returns.index,
+        name="benchmark_return",
+    )
+    return build_research_run_artifact(
+        result,
+        run_id=run_ref.run_id if run_id is None else run_id,
+        strategy_id=run_ref.strategy_id,
+        strategy_name="Alpha Top 1",
+        strategy_version=run_ref.strategy_version,
+        engine_version="1.2.0",
+        code_revision=run_ref.code_revision,
+        data_snapshot_id=run_ref.dataset_snapshot_id,
+        calendar="CN-A",
+        timezone="Asia/Shanghai",
+        started_at="2026-01-08T10:00:00+08:00",
+        finished_at="2026-01-08T10:01:00+08:00",
+        parameters=PARAMETERS,
+        benchmark_id="000300.SH",
+        benchmark_returns=benchmark,
+    )
+
+
+def _assert_error(
+    error: pytest.ExceptionInfo[BacktestContractError],
+    code: BacktestContractErrorCode,
+    path: str,
+) -> None:
+    assert error.value.code is code
+    assert error.value.path == path
+
+
+def test_backtest_run_ref_is_deterministic_and_binds_only_opaque_authorities() -> None:
+    first = _run_ref()
+    second = _run_ref()
+
+    assert first == second
+    assert first.run_id.startswith("rhbacktestrunv1:sha256:")
+    assert first.replay_spec_digest.startswith("sha256:")
+    assert first.dataset_snapshot_id.startswith("rhdsv1:sha256:")
+    assert first.foundation_id.startswith("rhdfv1:sha256:")
+    assert first.factor_set_id.startswith("rhfactorsetv1:sha256:")
+    assert first.trading_calendar_revision_ids == (CALENDAR_REVISION_ID,)
+    assert first.corporate_action_revision_ids == (ACTION_REVISION_ID,)
+    assert first.replay_parent_run_id is None
+    assert first.replay_attempt == 0
+    assert first.replay_ancestor_run_ids == ()
+    snapshot, foundation, factor_set = _accepted_authorities()
+    assert BacktestRunRef.from_dict(
+        first.to_dict(),
+        dataset_snapshot=snapshot,
+        foundation=foundation,
+        factor_set=factor_set,
+    ) == first
+    forbidden = ("latest", "locator", "uri", "credential", "provider", "broker")
+    assert not any(token in first.to_json().lower() for token in forbidden)
+
+
+@pytest.mark.parametrize(
+    ("field", "value"),
+    [
+        ("universe_digest", "sha256:" + "a" * 64),
+        ("strategy_digest", "sha256:" + "b" * 64),
+        ("execution_model_digest", "sha256:" + "c" * 64),
+        ("cost_model_digest", "sha256:" + "e" * 64),
+        ("random_seed", 8),
+        ("code_revision", "e" * 40),
+        ("environment_lock_digest", "sha256:" + "f" * 64),
+        ("configuration_digest", "sha256:" + "0" * 64),
+        ("evaluation_at", "2026-01-08T01:00:01Z"),
+        ("computed_at", "2026-01-08T02:00:01Z"),
+    ],
+)
+def test_every_governed_run_input_mutation_changes_run_identity(
+    field: str,
+    value: object,
+) -> None:
+    assert _run_ref(**{field: value}).run_id != _run_ref().run_id
+
+
+def test_backtest_run_ref_rejects_unclosed_upstream_and_unsafe_scalars() -> None:
+    with pytest.raises(BacktestContractError) as wrong_calendar:
+        _run_ref(trading_calendar_revision_ids=())
+    _assert_error(
+        wrong_calendar,
+        BacktestContractErrorCode.INPUT_CLOSURE_VIOLATION,
+        "$.trading_calendar_revision_ids",
+    )
+    with pytest.raises(BacktestContractError) as wrong_action:
+        _run_ref(corporate_action_revision_ids=())
+    _assert_error(
+        wrong_action,
+        BacktestContractErrorCode.INPUT_CLOSURE_VIOLATION,
+        "$.corporate_action_revision_ids",
+    )
+    with pytest.raises(BacktestContractError) as bool_seed:
+        _run_ref(random_seed=True)
+    _assert_error(bool_seed, BacktestContractErrorCode.TYPE_ERROR, "$.random_seed")
+    with pytest.raises(BacktestContractError) as bad_revision:
+        _run_ref(code_revision="abc")
+    _assert_error(bad_revision, BacktestContractErrorCode.INVALID_FORMAT, "$.code_revision")
+    with pytest.raises(BacktestContractError) as bad_digest:
+        _run_ref(universe_digest="5" * 64)
+    _assert_error(bad_digest, BacktestContractErrorCode.INVALID_FORMAT, "$.universe_digest")
+    with pytest.raises(BacktestContractError) as lookahead:
+        _run_ref(computed_at="2026-01-08T00:59:59Z")
+    _assert_error(lookahead, BacktestContractErrorCode.TIME_ORDER_VIOLATION, "$.computed_at")
+    with pytest.raises(BacktestContractError) as factor_type:
+        _run_ref(factor_set="rhfactorsetv1:sha256:" + "0" * 64)
+    _assert_error(factor_type, BacktestContractErrorCode.TYPE_ERROR, "$.factor_set")
+
+
+@pytest.mark.parametrize(
+    ("factor_times", "expected_path"),
+    [
+        (
+            {
+                "factor_evaluation_at": "2026-01-08T01:00:01Z",
+                "factor_computed_at": "2026-01-08T00:59:59Z",
+                "factor_artifact_available_at": "2026-01-08T01:00:00Z",
+            },
+            "$.evaluation_at",
+        ),
+        (
+            {
+                "factor_evaluation_at": "2026-01-03T11:00:00Z",
+                "factor_computed_at": "2026-01-08T01:00:00Z",
+                "factor_artifact_available_at": "2026-01-08T01:00:01Z",
+                "factor_availability_mode": AvailabilityMode.RETROSPECTIVE_REPLAY,
+            },
+            "$.evaluation_at",
+        ),
+    ],
+)
+def test_run_ref_evaluation_closes_factor_pit(
+    factor_times: dict[str, Any],
+    expected_path: str,
+) -> None:
+    snapshot, foundation, factor_set = _accepted_authorities(**factor_times)
+
+    with pytest.raises(BacktestContractError) as lookahead:
+        _run_ref(
+            dataset_snapshot=snapshot,
+            foundation=foundation,
+            factor_set=factor_set,
+        )
+
+    _assert_error(
+        lookahead,
+        BacktestContractErrorCode.TIME_ORDER_VIOLATION,
+        expected_path,
+    )
+
+
+def test_run_ref_rejects_aliases_locators_unsafe_integers_and_invalid_text() -> None:
+    with pytest.raises(BacktestContractError) as mutable_alias:
+        _run_ref(strategy_id="latest")
+    _assert_error(
+        mutable_alias,
+        BacktestContractErrorCode.INVALID_FORMAT,
+        "$.strategy_id",
+    )
+    with pytest.raises(BacktestContractError) as physical_uri:
+        _run_ref(execution_model_version="s3://model-bucket/current")
+    _assert_error(
+        physical_uri,
+        BacktestContractErrorCode.INVALID_FORMAT,
+        "$.execution_model_version",
+    )
+    with pytest.raises(BacktestContractError) as unsafe_seed:
+        _run_ref(random_seed=2**53)
+    _assert_error(
+        unsafe_seed,
+        BacktestContractErrorCode.INVALID_VALUE,
+        "$.random_seed",
+    )
+    with pytest.raises(BacktestContractError) as invalid_unicode:
+        _run_ref(strategy_id="\ud800")
+    _assert_error(
+        invalid_unicode,
+        BacktestContractErrorCode.INVALID_FORMAT,
+        "$.strategy_id",
+    )
+
+    run_ref = _run_ref()
+    mixed_keys = run_ref.to_dict()
+    mixed_keys[1] = "not-a-contract-key"  # type: ignore[index]
+    snapshot, foundation, factor_set = _accepted_authorities()
+    with pytest.raises(BacktestContractError) as invalid_key:
+        BacktestRunRef.from_dict(
+            mixed_keys,
+            dataset_snapshot=snapshot,
+            foundation=foundation,
+            factor_set=factor_set,
+        )
+    _assert_error(invalid_key, BacktestContractErrorCode.TYPE_ERROR, "$")
+
+
+@pytest.mark.parametrize(
+    "physical_id",
+    ["db.table", "source_alpha", "wind.model", "qtdb_view", "bloomberg-signal"],
+)
+def test_run_ref_rejects_physical_terms_in_logical_ids(physical_id: str) -> None:
+    with pytest.raises(BacktestContractError) as physical:
+        _run_ref(strategy_id=physical_id)
+    _assert_error(
+        physical,
+        BacktestContractErrorCode.INVALID_FORMAT,
+        "$.strategy_id",
+    )
+
+
+@pytest.mark.parametrize("version", ["1.0.0-.", "1.0.0-foo..bar", "1.0.0-01"])
+def test_run_ref_requires_strict_semver_prerelease_identifiers(version: str) -> None:
+    with pytest.raises(BacktestContractError) as invalid:
+        _run_ref(strategy_version=version)
+    _assert_error(
+        invalid,
+        BacktestContractErrorCode.INVALID_FORMAT,
+        "$.strategy_version",
+    )
+
+    assert _run_ref(strategy_version="1.0.0-alpha.1").strategy_version == "1.0.0-alpha.1"
+
+
+def test_replay_lineage_is_acyclic_and_cannot_claim_changed_inputs() -> None:
+    parent = _run_ref()
+    replay = _run_ref(
+        computed_at="2026-01-08T03:00:00Z",
+        parent=parent,
+        replay_reason="deterministic_reproduction",
+        replay_attempt=1,
+    )
+
+    assert replay.run_id != parent.run_id
+    assert replay.replay_spec_digest == parent.replay_spec_digest
+    assert replay.replay_parent_run_id == parent.run_id
+    assert replay.replay_ancestor_run_ids == (parent.run_id,)
+
+    with pytest.raises(BacktestContractError) as changed_input:
+        _run_ref(
+            universe_digest="sha256:" + "a" * 64,
+            computed_at="2026-01-08T03:00:00Z",
+            parent=parent,
+            replay_reason="changed_universe",
+            replay_attempt=1,
+        )
+    _assert_error(
+        changed_input,
+        BacktestContractErrorCode.LINEAGE_VIOLATION,
+        "$.replay_spec_digest",
+    )
+    with pytest.raises(BacktestContractError) as skipped_attempt:
+        _run_ref(
+            computed_at="2026-01-08T03:00:00Z",
+            parent=parent,
+            replay_reason="skipped_attempt",
+            replay_attempt=2,
+        )
+    _assert_error(
+        skipped_attempt,
+        BacktestContractErrorCode.LINEAGE_VIOLATION,
+        "$.replay_attempt",
+    )
+
+
+def test_offline_research_manifest_closes_exact_existing_evidence_mapping() -> None:
+    run_ref = _run_ref()
+    artifact = _artifact(run_ref)
+    first = build_backtest_evidence_manifest(
+        run_ref,
+        artifact,
+        artifact_available_at="2026-01-08T02:05:00Z",
+        qualification=EvidenceQualification.CONTRACT_QUALIFIED,
+    )
+    second = build_backtest_evidence_manifest(
+        run_ref,
+        artifact,
+        artifact_available_at="2026-01-08T02:05:00Z",
+        qualification=EvidenceQualification.CONTRACT_QUALIFIED,
+    )
+
+    assert first == second
+    assert first.manifest_id.startswith("rhbacktestevidencev1:sha256:")
+    assert first.run_id == run_ref.run_id
+    assert first.profile == "offline_research_v1"
+    assert first.qualification is EvidenceQualification.CONTRACT_QUALIFIED
+    mapping = {
+        item.category: tuple(table.logical_name for table in item.tables)
+        for item in first.evidence
+    }
+    assert mapping == {
+        "run": ("run",),
+        "signal": ("signals",),
+        "fill": ("trades",),
+        "position_nav": ("positions", "nav"),
+        "performance": ("performance",),
+        "attribution": ("attribution", "attribution_daily"),
+        "risk_snapshot": ("risk",),
+        "replay": (),
+    }
+    assert "order" not in mapping
+    assert "rejection" not in mapping
+    risk = next(item for item in first.evidence if item.category == "risk_snapshot")
+    assert risk.tables[0].row_count == 0
+    assert risk.tables[0].schema_digest.startswith("sha256:")
+
+    changed_performance = artifact.performance
+    changed_performance.loc[0, "n_days"] += 1
+    changed_artifact = replace(artifact, _performance=changed_performance)
+    changed = build_backtest_evidence_manifest(
+        run_ref,
+        changed_artifact,
+        artifact_available_at="2026-01-08T02:05:00Z",
+    )
+    assert changed.manifest_id != first.manifest_id
+    assert run_ref.run_id == first.run_id == changed.run_id
+
+
+def test_manifest_rejects_missing_mismatched_or_duplicate_evidence() -> None:
+    run_ref = _run_ref()
+    artifact = _artifact(run_ref)
+    with pytest.raises(BacktestContractError) as wrong_run:
+        build_backtest_evidence_manifest(
+            run_ref,
+            _artifact(run_ref, run_id="different-run"),
+            artifact_available_at="2026-01-08T02:05:00Z",
+        )
+    _assert_error(
+        wrong_run,
+        BacktestContractErrorCode.IDENTITY_MISMATCH,
+        "$.artifact.tables.run.run_id",
+    )
+    missing_signals = replace(artifact, _signals=None)  # type: ignore[arg-type]
+    with pytest.raises(BacktestContractError) as missing_table:
+        build_backtest_evidence_manifest(
+            run_ref,
+            missing_signals,
+            artifact_available_at="2026-01-08T02:05:00Z",
+        )
+    _assert_error(
+        missing_table,
+        BacktestContractErrorCode.TYPE_ERROR,
+        "$.artifact.tables.signals",
+    )
+    with pytest.raises(BacktestContractError) as digest_mismatch:
+        build_backtest_evidence_manifest(
+            run_ref,
+            artifact,
+            artifact_available_at="2026-01-08T02:05:00Z",
+            expected_table_digests={"performance": "sha256:" + "0" * 64},
+        )
+    _assert_error(
+        digest_mismatch,
+        BacktestContractErrorCode.EVIDENCE_MISMATCH,
+        "$.artifact.tables.performance.content_digest",
+    )
+    manifest = build_backtest_evidence_manifest(
+        run_ref,
+        artifact,
+        artifact_available_at="2026-01-08T02:05:00Z",
+    )
+    duplicate = manifest.to_dict()
+    duplicate["evidence"].append(copy.deepcopy(duplicate["evidence"][0]))
+    with pytest.raises(BacktestContractError) as duplicate_category:
+        BacktestEvidenceManifest.from_dict(
+            duplicate,
+            backtest_run_ref=run_ref,
+            artifact=artifact,
+        )
+    _assert_error(
+        duplicate_category,
+        BacktestContractErrorCode.INVALID_VALUE,
+        "$.evidence[8].category",
+    )
+
+
+def test_manifest_binds_supported_schema_and_has_collision_free_cell_encoding() -> None:
+    run_ref = _run_ref()
+    artifact = _artifact(run_ref)
+
+    with pytest.raises(BacktestContractError) as unsupported_schema:
+        build_backtest_evidence_manifest(
+            run_ref,
+            replace(artifact, schema_version="999.0.0"),
+            artifact_available_at="2026-01-08T02:05:00Z",
+        )
+    _assert_error(
+        unsupported_schema,
+        BacktestContractErrorCode.INVALID_VALUE,
+        "$.artifact.schema_version",
+    )
+
+    identities: set[str] = set()
+    for value in (float("nan"), float("inf"), float("-inf")):
+        performance = artifact.performance
+        performance.loc[0, "alpha"] = value
+        manifest = build_backtest_evidence_manifest(
+            run_ref,
+            replace(artifact, _performance=performance),
+            artifact_available_at="2026-01-08T02:05:00Z",
+        )
+        assert manifest.artifact_schema_version == RESEARCH_ARTIFACT_SCHEMA_VERSION
+        identities.add(manifest.manifest_id)
+    assert len(identities) == 3
+
+    content_digests: set[str] = set()
+    for value in (float("nan"), {"non_finite_float": "nan"}):
+        performance = artifact.performance.astype(object)
+        performance.at[0, "alpha"] = value
+        manifest = build_backtest_evidence_manifest(
+            run_ref,
+            replace(artifact, _performance=performance),
+            artifact_available_at="2026-01-08T02:05:00Z",
+        )
+        performance_entry = next(
+            entry for entry in manifest.evidence if entry.category == "performance"
+        )
+        content_digests.add(performance_entry.tables[0].content_digest)
+    assert len(content_digests) == 2
+
+    unsupported = artifact.performance.astype(object)
+    unsupported.loc[0, "alpha"] = object()
+    with pytest.raises(BacktestContractError) as unsupported_cell:
+        build_backtest_evidence_manifest(
+            run_ref,
+            replace(artifact, _performance=unsupported),
+            artifact_available_at="2026-01-08T02:05:00Z",
+        )
+    _assert_error(
+        unsupported_cell,
+        BacktestContractErrorCode.TYPE_ERROR,
+        "$.artifact.tables.performance.rows[0].alpha",
+    )
+
+    invalid_nested_key = artifact.performance.astype(object)
+    invalid_nested_key.at[0, "alpha"] = {"\ud800": "value"}
+    with pytest.raises(BacktestContractError) as invalid_utf8:
+        build_backtest_evidence_manifest(
+            run_ref,
+            replace(artifact, _performance=invalid_nested_key),
+            artifact_available_at="2026-01-08T02:05:00Z",
+        )
+    _assert_error(
+        invalid_utf8,
+        BacktestContractErrorCode.INVALID_FORMAT,
+        "$.artifact.tables.performance.rows[0].alpha.keys",
+    )
+
+    unsafe_integer = artifact.performance.astype(object)
+    unsafe_integer.loc[0, "alpha"] = 10**5000
+    with pytest.raises(BacktestContractError) as unsafe_cell:
+        build_backtest_evidence_manifest(
+            run_ref,
+            replace(artifact, _performance=unsafe_integer),
+            artifact_available_at="2026-01-08T02:05:00Z",
+        )
+    _assert_error(
+        unsafe_cell,
+        BacktestContractErrorCode.INVALID_VALUE,
+        "$.artifact.tables.performance.rows[0].alpha",
+    )
+
+
+def test_artifact_canonical_content_has_typed_collision_free_cell_encoding() -> None:
+    run_ref = _run_ref()
+    artifact = _artifact(run_ref)
+
+    content_hashes: set[str] = set()
+    for value in (
+        float("nan"),
+        float("inf"),
+        float("-inf"),
+        {"non_finite_float": "nan"},
+    ):
+        performance = artifact.performance.astype(object)
+        performance.at[0, "alpha"] = value
+        mutated = replace(artifact, _performance=performance)
+        content_hashes.add(mutated.content_sha256)
+        assert "non_finite_float" in mutated.canonical_json()
+    assert len(content_hashes) == 4
+
+    unsupported = artifact.performance.astype(object)
+    unsupported.loc[0, "alpha"] = object()
+    with pytest.raises(BacktestContractError) as unsupported_cell:
+        replace(artifact, _performance=unsupported).canonical_json()
+    _assert_error(
+        unsupported_cell,
+        BacktestContractErrorCode.TYPE_ERROR,
+        "$.tables.performance.rows[0].alpha",
+    )
+
+
+def test_legacy_bridge_is_explicit_and_cannot_be_contract_qualified() -> None:
+    run_ref = _run_ref()
+    legacy_run = BacktestRun(
+        run_id="legacy-run-001",
+        dataset_snapshot_id=run_ref.dataset_snapshot_id,
+        factor_version_id="alpha_005@1.0.0",
+        strategy_version_id="alpha-top1@1.0.0",
+        code_revision=run_ref.code_revision,
+        config_hash=_config_digest().removeprefix("sha256:"),
+        created_at=datetime(2026, 1, 8, 2, 0, tzinfo=UTC),
+    )
+    artifact = _artifact(run_ref, run_id=legacy_run.run_id)
+    manifest = build_legacy_backtest_evidence_manifest(
+        legacy_run,
+        artifact,
+        artifact_available_at="2026-01-08T02:05:00Z",
+    )
+
+    assert manifest.qualification is EvidenceQualification.LEGACY_EXPLORATORY
+    assert manifest.run_id == legacy_run.run_id
+    assert manifest.backtest_run_ref is None
+    assert manifest.to_dict()["run_reference"]["kind"] == "legacy_backtest_run"
+    assert BacktestEvidenceManifest.from_dict(
+        manifest.to_dict(),
+        artifact=artifact,
+    ) == manifest
+    with pytest.raises(BacktestContractError) as implicit_promotion:
+        build_backtest_evidence_manifest(  # type: ignore[arg-type]
+            legacy_run,
+            artifact,
+            artifact_available_at="2026-01-08T02:05:00Z",
+            qualification=EvidenceQualification.CONTRACT_QUALIFIED,
+        )
+    _assert_error(
+        implicit_promotion,
+        BacktestContractErrorCode.TYPE_ERROR,
+        "$.backtest_run_ref",
+    )
+
+
+def test_golden_contract_and_architecture_boundary() -> None:
+    run_ref = _run_ref()
+    artifact = _artifact(run_ref)
+    manifest = build_backtest_evidence_manifest(
+        run_ref,
+        artifact,
+        artifact_available_at="2026-01-08T02:05:00Z",
+    )
+    golden = json.loads(BACKTEST_FIXTURE.read_text(encoding="utf-8"))
+
+    table_digests = {
+        table.logical_name: table.content_digest
+        for item in manifest.evidence
+        for table in item.tables
+    }
+    assert golden == {
+        "run_id": run_ref.run_id,
+        "replay_spec_digest": run_ref.replay_spec_digest,
+        "manifest_id": manifest.manifest_id,
+        "evidence_digest": manifest.evidence_digest,
+        "table_content_digests": table_digests,
+    }
+    governed_source = (ROOT / "src" / "quant_engine" / "governed_pipeline.py").read_text(
+        encoding="utf-8"
+    )
+    artifact_source = (ROOT / "src" / "quant_engine" / "artifact.py").read_text(
+        encoding="utf-8"
+    )
+    assert "from quant_engine.artifact" not in governed_source
+    assert "BacktestRunRef" in governed_source
+    assert "BacktestEvidenceManifest" not in governed_source
+    assert "BacktestEvidenceManifest" in artifact_source
+    assert not (ROOT / "src" / "quant_engine" / "backtest_contracts.py").exists()

tests/test_factor_contracts.py

@@ -0,0 +1,938 @@
+"""Versioned factor-definition and factor-set contract conformance."""
+
+from __future__ import annotations
+
+import copy
+import hashlib
+import json
+from dataclasses import FrozenInstanceError
+from pathlib import Path
+from typing import Any, Callable
+
+import pytest
+
+from quant_engine.factor_contracts import (
+    ActorIdentity,
+    AvailabilityMode,
+    ContractErrorCode,
+    Causation,
+    DataFoundationEnvelope,
+    DatasetSnapshotEnvelope,
+    FactorContractError,
+    FactorDefinition,
+    FactorInput,
+    FactorSetRef,
+    HistoricalAvailability,
+    InputBinding,
+    LegacyFactorBinding,
+    OutputArtifactRef,
+    OutputCoverage,
+    OutputQuality,
+    OutputQualityCheck,
+    PayloadValidation,
+    ProducerIdentity,
+    TypedParameter,
+    ViewAvailability,
+    canonical_json_bytes,
+    factor_definition_from_alpha158,
+    factor_input_schema_digest,
+    validate_factor_catalog,
+)
+from quant_engine.governed_pipeline import (
+    FactorVersion,
+    bind_legacy_factor,
+    project_legacy_factor,
+)
+
+
+FIXTURE_PATH = Path(__file__).parent / "fixtures" / "factor-contracts-v1.golden.json"
+VIEW_REF_ID = "rhviewrefv1:sha256:bf776bcd26d940fafde1d650776a5505fb3fe8b5b068c351622bf2c42385629c"
+VIEW_SCHEMA_DIGEST = "sha256:0123456789abcdef0123456789abcdef0123456789abcdef0123456789abcdef"
+
+
+def _golden() -> dict[str, Any]:
+    loaded = json.loads(FIXTURE_PATH.read_text(encoding="utf-8"))
+    assert isinstance(loaded, dict)
+    return loaded
+
+
+def _sha256(value: bytes) -> str:
+    return f"sha256:{hashlib.sha256(value).hexdigest()}"
+
+
+def _reidentify(item: dict[str, Any], field: str, prefix: str) -> None:
+    payload = {key: value for key, value in item.items() if key != field}
+    item[field] = f"{prefix}{hashlib.sha256(canonical_json_bytes(payload)).hexdigest()}"
+
+
+def _snapshot_and_foundation(
+    fixture: dict[str, Any] | None = None,
+) -> tuple[DatasetSnapshotEnvelope, DataFoundationEnvelope]:
+    source = _golden() if fixture is None else fixture
+    return (
+        DatasetSnapshotEnvelope.from_dict(source["dataset_snapshot"]),
+        DataFoundationEnvelope.from_dict(source["data_foundation"]),
+    )
+
+
+def _definition(
+    *,
+    inputs: tuple[FactorInput, ...] | None = None,
+    **overrides: Any,
+) -> FactorDefinition:
+    factor_inputs = inputs or (
+        FactorInput("market", VIEW_SCHEMA_DIGEST, ("close", "volume")),
+    )
+    arguments: dict[str, Any] = {
+        "factor_id": "alpha_005",
+        "version": "1.0.0",
+        "formula": "correlation(close, volume, 10)",
+        "parameters": {},
+        "implementation_digest": "sha256:" + "1" * 64,
+        "input_schema_digest": factor_input_schema_digest(factor_inputs),
+        "inputs": factor_inputs,
+        "valid_from": "2026-01-01T00:00:00.000000Z",
+        "valid_until": "2027-01-01T00:00:00Z",
+        "warmup_sessions": 10,
+        "lag_sessions": 1,
+        "producer": ProducerIdentity("quant_engine", "1.0.0"),
+        "code_revision": "c" * 40,
+    }
+    arguments.update(overrides)
+    return FactorDefinition.create(**arguments)
+
+
+def _golden_definition() -> FactorDefinition:
+    factor_input = FactorInput("market", VIEW_SCHEMA_DIGEST, ("close", "volume"))
+    return factor_definition_from_alpha158(
+        "alpha_005",
+        version="1.0.0",
+        parameters={},
+        inputs=(factor_input,),
+        implementation_digest="sha256:" + "1" * 64,
+        input_schema_digest=factor_input_schema_digest((factor_input,)),
+        valid_from="2026-01-01T00:00:00.000000Z",
+        valid_until="2027-01-01T00:00:00Z",
+        warmup_sessions=10,
+        lag_sessions=1,
+        producer=ProducerIdentity("quant_engine", "1.0.0"),
+        code_revision="c" * 40,
+    )
+
+
+def _factor_set_arguments(
+    *,
+    fixture: dict[str, Any] | None = None,
+    snapshot: DatasetSnapshotEnvelope | None = None,
+    foundation: DataFoundationEnvelope | None = None,
+    definition: FactorDefinition | None = None,
+) -> dict[str, Any]:
+    source = _golden() if fixture is None else fixture
+    if snapshot is None or foundation is None:
+        parsed_snapshot, parsed_foundation = _snapshot_and_foundation(source)
+        snapshot = snapshot or parsed_snapshot
+        foundation = foundation or parsed_foundation
+    selected_definition = definition or _golden_definition()
+    output_schema_bytes = canonical_json_bytes(source["output_schema"])
+    output_content_bytes = canonical_json_bytes(source["output_content"])
+    artifact = OutputArtifactRef.create(
+        schema_digest=_sha256(output_schema_bytes),
+        content_digest=_sha256(output_content_bytes),
+    )
+    return {
+        "definitions": (selected_definition,),
+        "dataset_snapshot": snapshot,
+        "foundation": foundation,
+        "selected_view_ref_ids": (VIEW_REF_ID,),
+        "input_bindings": (
+            InputBinding(
+                selected_definition.definition_id,
+                "market",
+                VIEW_REF_ID,
+                VIEW_SCHEMA_DIGEST,
+            ),
+        ),
+        "view_availability": (
+            ViewAvailability(VIEW_REF_ID, "2026-01-02T23:50:00Z", "sha256:" + "2" * 64),
+        ),
+        "output_quality": OutputQuality(
+            "passed",
+            (OutputQualityCheck("finite_values", "passed", "sha256:" + "3" * 64),),
+        ),
+        "output_coverage": OutputCoverage(
+            "complete",
+            1,
+            1,
+            "row",
+            "alpha_005.cn_a",
+            "sha256:" + "4" * 64,
+        ),
+        "output_schema_bytes": output_schema_bytes,
+        "output_content_bytes": output_content_bytes,
+        "output_artifact_ref": artifact,
+        "availability_mode": AvailabilityMode.AS_AVAILABLE,
+        "evaluation_at": "2026-01-03T11:00:00Z",
+        "computed_at": "2026-01-03T10:15:00Z",
+        "artifact_available_at": "2026-01-03T10:20:00Z",
+        "producer": ProducerIdentity("quant_engine", "1.0.0"),
+        "code_revision": "c" * 40,
+        "actor": ActorIdentity("service", "factor_worker_v1"),
+        "correlation_id": "research_run_001",
+        "causation": Causation("foundation", foundation.foundation_id),
+        "evidence_scope": "synthetic_fixture",
+        "decision_eligible": False,
+    }
+
+
+def _factor_set(**overrides: Any) -> FactorSetRef:
+    arguments = _factor_set_arguments()
+    arguments.update(overrides)
+    return FactorSetRef.create(**arguments)
+
+
+def _assert_error(
+    error: pytest.ExceptionInfo[FactorContractError],
+    code: ContractErrorCode,
+    path: str,
+) -> None:
+    assert error.value.code is code
+    assert error.value.path == path
+
+
+def _mutate_artifact_schema_binding(value: dict[str, Any]) -> None:
+    artifact = value["output_artifact_ref"]
+    artifact["schema_digest"] = "sha256:" + "0" * 64
+    _reidentify(artifact, "artifact_id", "rhfactoroutputv1:sha256:")
+
+
+def test_golden_contracts_are_content_addressed_round_trippable_and_deeply_immutable() -> None:
+    fixture = _golden()
+    original_snapshot = copy.deepcopy(fixture["dataset_snapshot"])
+    original_foundation = copy.deepcopy(fixture["data_foundation"])
+    snapshot, foundation = _snapshot_and_foundation(fixture)
+    definition = _golden_definition()
+    factor_set = FactorSetRef.create(**_factor_set_arguments(fixture=fixture, snapshot=snapshot, foundation=foundation, definition=definition))
+    binding = LegacyFactorBinding.create(
+        definition=definition,
+        legacy_factor_id="factor:demo-momentum",
+        legacy_version="1.0.0",
+        legacy_definition_sha256="b" * 64,
+        legacy_dataset_schema_version="1.0.0",
+        canonical_input_schema_digest=definition.input_schema_digest,
+        correspondence_evidence_digest="sha256:" + "5" * 64,
+    )
+
+    assert snapshot.pit_cutoff == "2026-01-02T07:01:00Z"
+    assert foundation.pit_cutoff == factor_set.pit_cutoff == "2026-01-03T00:00:00Z"
+    assert snapshot.pit_cutoff != foundation.pit_cutoff
+    assert definition.definition_id == fixture["expected"]["definition_id"]
+    assert definition.input_schema_digest == fixture["expected"]["input_schema_digest"]
+    assert factor_set.factor_set_id == fixture["expected"]["factor_set_id"]
+    assert factor_set.output_artifact_ref.artifact_id == fixture["expected"]["output_artifact_id"]
+    assert binding.binding_id == fixture["expected"]["legacy_binding_id"]
+    assert not definition.to_json().endswith("\n")
+    assert not factor_set.to_json().endswith("\n")
+    assert FactorDefinition.from_json(definition.to_json()) == definition
+
+    reparsed = FactorSetRef.from_json(
+        factor_set.to_json(),
+        definitions=(definition,),
+        dataset_snapshot=snapshot,
+        foundation=foundation,
+        output_schema_bytes=canonical_json_bytes(fixture["output_schema"]),
+        output_content_bytes=canonical_json_bytes(fixture["output_content"]),
+    )
+    reference_only = FactorSetRef.from_json(
+        factor_set.to_json(),
+        definitions=(definition,),
+        dataset_snapshot=snapshot,
+        foundation=foundation,
+    )
+    assert reparsed.factor_set_id == factor_set.factor_set_id
+    assert reparsed.payload_validation is PayloadValidation.PAYLOAD_REVALIDATED
+    assert reference_only.payload_validation is PayloadValidation.REFERENCE_ONLY
+
+    fixture["dataset_snapshot"]["descriptor"]["dataset"]["dimensions"].append("forbidden")
+    fixture["data_foundation"]["standardized_views"][0]["schema_digest"] = "sha256:" + "0" * 64
+    assert snapshot.to_dict() == original_snapshot
+    assert foundation.to_dict() == original_foundation
+    returned = snapshot.to_dict()
+    returned["descriptor"]["dataset"]["dimensions"].append("also_forbidden")
+    assert snapshot.to_dict() == original_snapshot
+    with pytest.raises(FrozenInstanceError):
+        snapshot.snapshot_id = "rhdsv1:sha256:" + "0" * 64  # type: ignore[misc]
+
+
+@pytest.mark.parametrize("variant", ["whitespace", "key_order"])
+def test_contract_decoders_reject_non_canonical_json(variant: str) -> None:
+    fixture = _golden()
+    snapshot, foundation = _snapshot_and_foundation(fixture)
+    definition = _golden_definition()
+    factor_set = FactorSetRef.create(
+        **_factor_set_arguments(
+            fixture=fixture,
+            snapshot=snapshot,
+            foundation=foundation,
+            definition=definition,
+        )
+    )
+    binding = LegacyFactorBinding.create(
+        definition=definition,
+        legacy_factor_id="factor:demo-momentum",
+        legacy_version="1.0.0",
+        legacy_definition_sha256="b" * 64,
+        legacy_dataset_schema_version="1.0.0",
+        canonical_input_schema_digest=definition.input_schema_digest,
+        correspondence_evidence_digest="sha256:" + "5" * 64,
+    )
+
+    def non_canonical(value: str) -> str:
+        if variant == "whitespace":
+            return value + "\n"
+        loaded = json.loads(value)
+        reversed_items = dict(reversed(tuple(loaded.items())))
+        return json.dumps(reversed_items, ensure_ascii=False, separators=(",", ":"))
+
+    decoders = (
+        lambda value: FactorDefinition.from_json(value),
+        lambda value: FactorSetRef.from_json(
+            value,
+            definitions=(definition,),
+            dataset_snapshot=snapshot,
+            foundation=foundation,
+        ),
+        lambda value: LegacyFactorBinding.from_json(value, definition=definition),
+    )
+    for decoder, encoded in zip(
+        decoders,
+        (definition.to_json(), factor_set.to_json(), binding.to_json()),
+        strict=True,
+    ):
+        with pytest.raises(FactorContractError) as exc_info:
+            decoder(non_canonical(encoded))
+        assert exc_info.value.code is ContractErrorCode.INVALID_FORMAT
+        assert exc_info.value.path == "$"
+
+
+def test_factor_definition_identity_is_order_independent_where_semantics_are_unordered() -> None:
+    first_input = FactorInput("prices", "sha256:" + "6" * 64, ("close",))
+    second_input = FactorInput("volumes", "sha256:" + "7" * 64, ("volume",))
+    inputs = (first_input, second_input)
+    parameters_a = {
+        "window": TypedParameter("integer", 10),
+        "weights": TypedParameter("json", {"fast": [1, 2], "slow": [3, 4]}),
+    }
+    parameters_b = {
+        "weights": TypedParameter("json", {"slow": [3, 4], "fast": [1, 2]}),
+        "window": TypedParameter("integer", 10),
+    }
+    first = _definition(
+        inputs=inputs,
+        parameters=parameters_a,
+        input_schema_digest=factor_input_schema_digest(inputs),
+    )
+    second = _definition(
+        inputs=tuple(reversed(inputs)),
+        parameters=parameters_b,
+        input_schema_digest=factor_input_schema_digest(tuple(reversed(inputs))),
+    )
+    assert first.definition_id == second.definition_id
+    assert first.to_json() == second.to_json()
+
+    semantic_changes = (
+        _definition(factor_id="alpha_006"),
+        _definition(version="1.0.1"),
+        _definition(formula="correlation(close, volume, 11)"),
+        _definition(parameters={"window": TypedParameter("integer", 10)}),
+        _definition(implementation_digest="sha256:" + "9" * 64),
+        _definition(valid_until="2027-01-02T00:00:00Z"),
+        _definition(warmup_sessions=11),
+        _definition(lag_sessions=2),
+        _definition(producer=ProducerIdentity("quant_engine", "1.0.1")),
+        _definition(code_revision="d" * 40),
+    )
+    assert all(changed.definition_id != _golden_definition().definition_id for changed in semantic_changes)
+    assert len({changed.definition_id for changed in semantic_changes}) == len(semantic_changes)
+
+
+def test_parameter_types_decimal_profile_and_detached_nested_values_are_strict() -> None:
+    nested = {"ordered": [1, {"flag": True}]}
+    parameter = TypedParameter("json", nested)
+    nested["ordered"].append(2)
+    definition = _definition(parameters={"payload": parameter})
+    assert definition.to_dict()["parameters"]["payload"]["value"] == {
+        "ordered": [1, {"flag": True}]
+    }
+    integer_definition = _definition(parameters={"value": TypedParameter("integer", 1)})
+    string_definition = _definition(parameters={"value": TypedParameter("string", "1")})
+    assert integer_definition.definition_id != string_definition.definition_id
+
+    for parameter_type, value, code in (
+        ("decimal", "1.0", ContractErrorCode.INVALID_FORMAT),
+        ("decimal", "1e3", ContractErrorCode.INVALID_FORMAT),
+        ("decimal", "-0", ContractErrorCode.INVALID_FORMAT),
+        ("integer", True, ContractErrorCode.TYPE_ERROR),
+        ("json", 1.5, ContractErrorCode.TYPE_ERROR),
+        ("json", {"é": "bad-key"}, ContractErrorCode.INVALID_FORMAT),
+        ("json", 9_007_199_254_740_992, ContractErrorCode.INVALID_VALUE),
+    ):
+        with pytest.raises(FactorContractError) as error:
+            TypedParameter(parameter_type, value)
+        assert error.value.code is code
+    assert TypedParameter("decimal", "10.25").to_dict()["value"] == "10.25"
+
+
+def test_catalog_rejects_duplicate_and_overlapping_logical_validity_but_allows_adjacency() -> None:
+    base = _golden_definition()
+    adjacent = _definition(valid_from="2027-01-01T00:00:00Z", valid_until="2028-01-01T00:00:00Z")
+    assert len(validate_factor_catalog((adjacent, base))) == 2
+    with pytest.raises(FactorContractError) as duplicate:
+        validate_factor_catalog((base, base))
+    _assert_error(duplicate, ContractErrorCode.INVALID_VALUE, "$.definitions")
+    overlapping = _definition(valid_from="2026-06-01T00:00:00Z", valid_until="2028-01-01T00:00:00Z")
+    with pytest.raises(FactorContractError) as overlap:
+        validate_factor_catalog((base, overlapping))
+    _assert_error(overlap, ContractErrorCode.TIME_ORDER_VIOLATION, "$.definitions")
+
+
+def test_upstream_contracts_reject_unknown_fields_identity_forgery_and_unqualified_input() -> None:
+    unknown = _golden()["dataset_snapshot"]
+    unknown["provider"] = "forbidden"
+    with pytest.raises(FactorContractError) as unknown_error:
+        DatasetSnapshotEnvelope.from_dict(unknown)
+    _assert_error(unknown_error, ContractErrorCode.UNKNOWN_FIELD, "$.provider")
+
+    forged = _golden()["data_foundation"]
+    forged["standardized_views"][0]["schema_digest"] = "sha256:" + "0" * 64
+    with pytest.raises(FactorContractError) as forged_error:
+        DataFoundationEnvelope.from_dict(forged)
+    assert forged_error.value.code is ContractErrorCode.IDENTITY_MISMATCH
+    assert forged_error.value.path.endswith("view_ref_id")
+
+    rejected_source = _golden()
+    rejected_source["dataset_snapshot"]["descriptor"]["qualification"]["status"] = "rejected"
+    _reidentify(rejected_source["dataset_snapshot"], "snapshot_id", "rhdsv1:sha256:")
+    rejected_snapshot = DatasetSnapshotEnvelope.from_dict(rejected_source["dataset_snapshot"])
+    _, foundation = _snapshot_and_foundation()
+    with pytest.raises(FactorContractError) as rejected_error:
+        FactorSetRef.create(
+            **_factor_set_arguments(snapshot=rejected_snapshot, foundation=foundation)
+        )
+    _assert_error(
+        rejected_error,
+        ContractErrorCode.QUALIFICATION_REJECTED,
+        "$.dataset_snapshot.descriptor.qualification",
+    )
+
+
+def test_foundation_rejects_future_knowledge_and_per_view_calendar_borrowing() -> None:
+    future = _golden()["data_foundation"]
+    action = future["corporate_action_revisions"][0]
+    old_action_id = action["action_revision_id"]
+    action["knowledge_time"] = "2026-01-03T00:00:01Z"
+    _reidentify(action, "action_revision_id", "rhcav1:sha256:")
+    future["standardized_views"][0]["corporate_action_revision_ids"] = [action["action_revision_id"]]
+    lineage = next(item for item in future["revision_lineage"] if item["revision_id"] == old_action_id)
+    lineage["revision_id"] = action["action_revision_id"]
+    lineage["knowledge_time"] = action["knowledge_time"]
+    _reidentify(future["standardized_views"][0], "view_ref_id", "rhviewrefv1:sha256:")
+    _reidentify(future, "foundation_id", "rhdfv1:sha256:")
+    with pytest.raises(FactorContractError) as future_error:
+        DataFoundationEnvelope.from_dict(future)
+    _assert_error(
+        future_error,
+        ContractErrorCode.TIME_ORDER_VIOLATION,
+        "$.revision_lineage.knowledge_time",
+    )
+
+    uncovered = _golden()["data_foundation"]
+    original_route_id = uncovered["instrument_routes"][0]["route_revision_id"]
+    second_calendar = copy.deepcopy(uncovered["trading_calendar_revisions"][0])
+    second_calendar["calendar_id"] = "rhcalendar:99990000111122223333444455556666"
+    _reidentify(second_calendar, "calendar_revision_id", "rhcalv1:sha256:")
+    uncovered["trading_calendar_revisions"].append(second_calendar)
+    route = uncovered["instrument_routes"][0]
+    route["calendar_id"] = second_calendar["calendar_id"]
+    _reidentify(route, "route_revision_id", "rhroutev1:sha256:")
+    route_lineage = next(item for item in uncovered["revision_lineage"] if item["revision_id"] == original_route_id)
+    route_lineage["revision_id"] = route["route_revision_id"]
+    uncovered["revision_lineage"].append(
+        {
+            "revision_kind": "trading_calendar",
+            "revision_id": second_calendar["calendar_revision_id"],
+            "revision_number": 1,
+            "knowledge_time": second_calendar["knowledge_time"],
+            "evidence_digest": second_calendar["evidence_digest"],
+        }
+    )
+    view = uncovered["standardized_views"][0]
+    view["instrument_route_revision_ids"] = [route["route_revision_id"]]
+    _reidentify(view, "view_ref_id", "rhviewrefv1:sha256:")
+    _reidentify(uncovered, "foundation_id", "rhdfv1:sha256:")
+    with pytest.raises(FactorContractError) as calendar_error:
+        DataFoundationEnvelope.from_dict(uncovered)
+    assert calendar_error.value.code is ContractErrorCode.INPUT_CLOSURE_VIOLATION
+    assert "selected route calendar" in calendar_error.value.detail
+
+
+def _replay_fixture() -> dict[str, Any]:
+    fixture = _golden()
+    snapshot = fixture["dataset_snapshot"]
+    snapshot["descriptor"]["published_at"] = "2026-01-04T00:00:00Z"
+    _reidentify(snapshot, "snapshot_id", "rhdsv1:sha256:")
+    foundation = fixture["data_foundation"]
+    foundation["dataset_snapshot_id"] = snapshot["snapshot_id"]
+    for view in foundation["standardized_views"]:
+        view["dataset_snapshot_id"] = snapshot["snapshot_id"]
+        _reidentify(view, "view_ref_id", "rhviewrefv1:sha256:")
+    _reidentify(foundation, "foundation_id", "rhdfv1:sha256:")
+    return fixture
+
+
+def test_as_available_and_retrospective_replay_keep_distinct_time_claims() -> None:
+    as_available = _factor_set()
+    assert as_available.historical_availability is HistoricalAvailability.DECLARED_AS_AVAILABLE
+
+    replay_source = _replay_fixture()
+    snapshot, foundation = _snapshot_and_foundation(replay_source)
+    replay_view_id = next(iter(foundation.views))
+    arguments = _factor_set_arguments(
+        fixture=replay_source,
+        snapshot=snapshot,
+        foundation=foundation,
+    )
+    arguments.update(
+        selected_view_ref_ids=(replay_view_id,),
+        input_bindings=(
+            InputBinding(
+                arguments["definitions"][0].definition_id,
+                "market",
+                replay_view_id,
+                VIEW_SCHEMA_DIGEST,
+            ),
+        ),
+        view_availability=(
+            ViewAvailability(replay_view_id, "2026-01-04T00:10:00Z", "sha256:" + "2" * 64),
+        ),
+        availability_mode=AvailabilityMode.RETROSPECTIVE_REPLAY,
+        computed_at="2026-01-04T00:20:00Z",
+        artifact_available_at="2026-01-04T00:25:00Z",
+        causation=Causation("foundation", foundation.foundation_id),
+    )
+    replay = FactorSetRef.create(**arguments)
+    assert replay.evaluation_at == "2026-01-03T11:00:00Z"
+    assert replay.computed_at == "2026-01-04T00:20:00Z"
+    assert replay.historical_availability is HistoricalAvailability.NOT_ESTABLISHED
+
+    replay_source_args = _factor_set_arguments(
+        fixture=replay_source,
+        snapshot=snapshot,
+        foundation=foundation,
+    )
+    replay_source_args.update(
+        selected_view_ref_ids=(replay_view_id,),
+        input_bindings=(
+            InputBinding(
+                replay_source_args["definitions"][0].definition_id,
+                "market",
+                replay_view_id,
+                VIEW_SCHEMA_DIGEST,
+            ),
+        ),
+        view_availability=(
+            ViewAvailability(replay_view_id, "2026-01-02T23:50:00Z", "sha256:" + "2" * 64),
+        ),
+        causation=Causation("foundation", foundation.foundation_id),
+    )
+    with pytest.raises(FactorContractError) as late_publication:
+        FactorSetRef.create(**replay_source_args)
+    _assert_error(
+        late_publication,
+        ContractErrorCode.TIME_ORDER_VIOLATION,
+        "$.dataset_snapshot.descriptor.published_at",
+    )
+
+
+@pytest.mark.parametrize(
+    ("overrides", "path"),
+    [
+        ({"view_availability": (ViewAvailability(VIEW_REF_ID, "2026-01-03T00:00:01Z", "sha256:" + "2" * 64),)}, "$.view_availability"),
+        ({"computed_at": "2026-01-02T23:40:00Z"}, "$.computed_at"),
+        ({"artifact_available_at": "2026-01-03T10:14:00Z"}, "$.artifact_available_at"),
+        ({"artifact_available_at": "2026-01-03T11:00:01Z"}, "$.artifact_available_at"),
+        ({"evaluation_at": "2026-01-03T11:00:00"}, "$.evaluation_at"),
+    ],
+)
+def test_as_available_time_failures_are_typed(overrides: dict[str, Any], path: str) -> None:
+    with pytest.raises(FactorContractError) as error:
+        _factor_set(**overrides)
+    assert error.value.code in {
+        ContractErrorCode.INVALID_FORMAT,
+        ContractErrorCode.TIME_ORDER_VIOLATION,
+    }
+    assert error.value.path == path
+
+
+def test_replay_rejects_backdating_and_historical_availability_promotion() -> None:
+    source = _replay_fixture()
+    snapshot, foundation = _snapshot_and_foundation(source)
+    view_id = next(iter(foundation.views))
+    arguments = _factor_set_arguments(fixture=source, snapshot=snapshot, foundation=foundation)
+    definition = arguments["definitions"][0]
+    arguments.update(
+        selected_view_ref_ids=(view_id,),
+        input_bindings=(InputBinding(definition.definition_id, "market", view_id, VIEW_SCHEMA_DIGEST),),
+        view_availability=(ViewAvailability(view_id, "2026-01-04T00:10:00Z", "sha256:" + "2" * 64),),
+        availability_mode=AvailabilityMode.RETROSPECTIVE_REPLAY,
+        computed_at="2026-01-04T00:20:00Z",
+        artifact_available_at="2026-01-04T00:25:00Z",
+        causation=Causation("foundation", foundation.foundation_id),
+    )
+    replay = FactorSetRef.create(**arguments)
+    promoted = replay.to_dict()
+    promoted["historical_availability"] = "declared_as_available"
+    _reidentify(promoted, "factor_set_id", "rhfactorsetv1:sha256:")
+    with pytest.raises(FactorContractError) as promotion_error:
+        FactorSetRef.from_dict(
+            promoted,
+            definitions=(definition,),
+            dataset_snapshot=snapshot,
+            foundation=foundation,
+        )
+    _assert_error(
+        promotion_error,
+        ContractErrorCode.READINESS_ESCALATION,
+        "$.historical_availability",
+    )
+    arguments["computed_at"] = "2026-01-03T11:30:00Z"
+    with pytest.raises(FactorContractError) as backdated_error:
+        FactorSetRef.create(**arguments)
+    _assert_error(backdated_error, ContractErrorCode.TIME_ORDER_VIOLATION, "$.computed_at")
+
+
+def _multi_view_fixture() -> tuple[dict[str, Any], str]:
+    fixture = _golden()
+    foundation = fixture["data_foundation"]
+    second = copy.deepcopy(foundation["standardized_views"][0])
+    second["view_id"] = "rhview:11111111222222223333333344444444"
+    second["schema_digest"] = "sha256:" + "6" * 64
+    second["content_digest"] = "sha256:" + "7" * 64
+    second["transformation_digest"] = "sha256:" + "8" * 64
+    _reidentify(second, "view_ref_id", "rhviewrefv1:sha256:")
+    foundation["standardized_views"].append(second)
+    _reidentify(foundation, "foundation_id", "rhdfv1:sha256:")
+    return fixture, second["view_ref_id"]
+
+
+def test_multi_input_mapping_requires_exact_consumption_closure_and_is_order_independent() -> None:
+    fixture, second_view_id = _multi_view_fixture()
+    snapshot, foundation = _snapshot_and_foundation(fixture)
+    inputs = (
+        FactorInput("prices", VIEW_SCHEMA_DIGEST, ("close",)),
+        FactorInput("volumes", "sha256:" + "6" * 64, ("volume",)),
+    )
+    definition = _definition(
+        inputs=inputs,
+        formula="correlation(close, volume, 10)",
+        input_schema_digest=factor_input_schema_digest(inputs),
+    )
+    first_binding = InputBinding(definition.definition_id, "prices", VIEW_REF_ID, VIEW_SCHEMA_DIGEST)
+    second_binding = InputBinding(definition.definition_id, "volumes", second_view_id, "sha256:" + "6" * 64)
+    first_availability = ViewAvailability(VIEW_REF_ID, "2026-01-02T23:40:00Z", "sha256:" + "2" * 64)
+    second_availability = ViewAvailability(second_view_id, "2026-01-02T23:50:00Z", "sha256:" + "6" * 64)
+    base = _factor_set_arguments(fixture=fixture, snapshot=snapshot, foundation=foundation, definition=definition)
+    base.update(
+        selected_view_ref_ids=(VIEW_REF_ID, second_view_id),
+        input_bindings=(first_binding, second_binding),
+        view_availability=(first_availability, second_availability),
+        causation=Causation("foundation", foundation.foundation_id),
+    )
+    first = FactorSetRef.create(**base)
+    reordered = dict(base)
+    reordered.update(
+        selected_view_ref_ids=(second_view_id, VIEW_REF_ID),
+        input_bindings=(second_binding, first_binding),
+        view_availability=(second_availability, first_availability),
+    )
+    assert FactorSetRef.create(**reordered).factor_set_id == first.factor_set_id
+
+    for invalid_bindings, invalid_views in (
+        ((first_binding,), (VIEW_REF_ID, second_view_id)),
+        ((first_binding, second_binding), (VIEW_REF_ID,)),
+        ((first_binding, second_binding), (VIEW_REF_ID, second_view_id, VIEW_REF_ID)),
+    ):
+        invalid = dict(base)
+        invalid.update(input_bindings=invalid_bindings, selected_view_ref_ids=invalid_views)
+        with pytest.raises(FactorContractError) as error:
+            FactorSetRef.create(**invalid)
+        assert error.value.code in {
+            ContractErrorCode.INPUT_CLOSURE_VIOLATION,
+            ContractErrorCode.INVALID_VALUE,
+        }
+
+
+@pytest.mark.parametrize(
+    ("mutate", "code", "path"),
+    [
+        (lambda value: value["producer"].pop("id"), ContractErrorCode.MISSING_FIELD, "$.producer.id"),
+        (lambda value: value["producer"].pop("version"), ContractErrorCode.MISSING_FIELD, "$.producer.version"),
+        (lambda value: value["producer"].update(id="other_engine"), ContractErrorCode.LINEAGE_VIOLATION, "$.producer.id"),
+        (lambda value: value["producer"].update(version="latest"), ContractErrorCode.INVALID_FORMAT, "$.producer.version"),
+        (lambda value: value.update(code_revision="bad"), ContractErrorCode.INVALID_FORMAT, "$.code_revision"),
+        (lambda value: value["actor"].pop("kind"), ContractErrorCode.MISSING_FIELD, "$.actor.kind"),
+        (lambda value: value["actor"].pop("id"), ContractErrorCode.MISSING_FIELD, "$.actor.id"),
+        (lambda value: value["actor"].update(kind="robot"), ContractErrorCode.INVALID_VALUE, "$.actor.kind"),
+        (lambda value: value["actor"].update(id="latest"), ContractErrorCode.INVALID_VALUE, "$.actor.id"),
+        (lambda value: value.pop("correlation_id"), ContractErrorCode.MISSING_FIELD, "$.correlation_id"),
+        (lambda value: value.update(correlation_id="latest"), ContractErrorCode.INVALID_VALUE, "$.correlation_id"),
+        (lambda value: value["causation"].pop("kind"), ContractErrorCode.MISSING_FIELD, "$.causation.kind"),
+        (lambda value: value["causation"].update(kind="run"), ContractErrorCode.INVALID_VALUE, "$.causation.kind"),
+        (lambda value: value["causation"].update(id="rhdfv1:sha256:" + "0" * 64), ContractErrorCode.LINEAGE_VIOLATION, "$.causation.id"),
+        (lambda value: value.pop("output_artifact_ref"), ContractErrorCode.MISSING_FIELD, "$.output_artifact_ref"),
+        (lambda value: value["output_artifact_ref"].update(artifact_id="rhfactoroutputv1:sha256:" + "0" * 64), ContractErrorCode.IDENTITY_MISMATCH, "$.output_artifact_ref.artifact_id"),
+        (_mutate_artifact_schema_binding, ContractErrorCode.ARTIFACT_MISMATCH, "$.output_artifact_ref"),
+        (lambda value: value.update(availability_mode="implicit_fallback"), ContractErrorCode.INVALID_VALUE, "$.availability_mode"),
+        (lambda value: value.pop("computed_at"), ContractErrorCode.MISSING_FIELD, "$.computed_at"),
+        (lambda value: value.update(decision_eligible=True), ContractErrorCode.READINESS_ESCALATION, "$.decision_eligible"),
+        (lambda value: value.update(evidence_scope="real_data"), ContractErrorCode.READINESS_ESCALATION, "$.evidence_scope"),
+        (lambda value: value["upstream_evidence"].update(qualification_evidence_digest="sha256:" + "0" * 64), ContractErrorCode.IDENTITY_MISMATCH, "$.upstream_evidence"),
+    ],
+)
+def test_lineage_artifact_and_readiness_fields_have_independent_typed_negatives(
+    mutate: Callable[[dict[str, Any]], Any],
+    code: ContractErrorCode,
+    path: str,
+) -> None:
+    factor_set = _factor_set()
+    value = factor_set.to_dict()
+    mutate(value)
+    if "factor_set_id" in value:
+        _reidentify(value, "factor_set_id", "rhfactorsetv1:sha256:")
+    snapshot, foundation = _snapshot_and_foundation()
+    with pytest.raises(FactorContractError) as error:
+        FactorSetRef.from_dict(
+            value,
+            definitions=(_golden_definition(),),
+            dataset_snapshot=snapshot,
+            foundation=foundation,
+        )
+    _assert_error(error, code, path)
+
+
+def test_output_schema_content_bytes_cannot_be_swapped_or_forged() -> None:
+    factor_set = _factor_set()
+    fixture = _golden()
+    snapshot, foundation = _snapshot_and_foundation()
+    schema_bytes = canonical_json_bytes(fixture["output_schema"])
+    content_bytes = canonical_json_bytes(fixture["output_content"])
+    with pytest.raises(FactorContractError) as swapped:
+        FactorSetRef.from_dict(
+            factor_set.to_dict(),
+            definitions=(_golden_definition(),),
+            dataset_snapshot=snapshot,
+            foundation=foundation,
+            output_schema_bytes=content_bytes,
+            output_content_bytes=schema_bytes,
+        )
+    _assert_error(swapped, ContractErrorCode.ARTIFACT_MISMATCH, "$.output_artifact_ref")
+    with pytest.raises(FactorContractError) as noncanonical:
+        FactorSetRef.create(
+            **{
+                **_factor_set_arguments(),
+                "output_schema_bytes": json.dumps(fixture["output_schema"], indent=2).encode(),
+            }
+        )
+    _assert_error(noncanonical, ContractErrorCode.INVALID_FORMAT, "$.output_schema_bytes")
+
+
+def test_unsuccessful_output_quality_or_coverage_cannot_form_a_factor_set() -> None:
+    with pytest.raises(FactorContractError) as failed_quality:
+        _factor_set(
+            output_quality=OutputQuality(
+                "failed",
+                (OutputQualityCheck("finite_values", "failed", "sha256:" + "3" * 64),),
+            )
+        )
+    _assert_error(failed_quality, ContractErrorCode.INVALID_VALUE, "$.output_quality")
+
+    for coverage in (
+        OutputCoverage("incomplete", 2, 1, "row", "alpha_005.cn_a", "sha256:" + "4" * 64),
+        OutputCoverage("complete", 2, 1, "row", "alpha_005.cn_a", "sha256:" + "4" * 64),
+    ):
+        with pytest.raises(FactorContractError) as incomplete:
+            _factor_set(output_coverage=coverage)
+        _assert_error(incomplete, ContractErrorCode.INVALID_VALUE, "$.output_coverage")
+
+
+def test_external_snapshot_definition_and_view_references_cannot_be_substituted() -> None:
+    factor_set = _factor_set()
+    snapshot, foundation = _snapshot_and_foundation()
+    value = factor_set.to_dict()
+    value["dataset_snapshot_id"] = "rhdsv1:sha256:" + "0" * 64
+    _reidentify(value, "factor_set_id", "rhfactorsetv1:sha256:")
+    with pytest.raises(FactorContractError) as snapshot_error:
+        FactorSetRef.from_dict(
+            value,
+            definitions=(_golden_definition(),),
+            dataset_snapshot=snapshot,
+            foundation=foundation,
+        )
+    _assert_error(
+        snapshot_error,
+        ContractErrorCode.INPUT_CLOSURE_VIOLATION,
+        "$.dataset_snapshot_id",
+    )
+
+    value = factor_set.to_dict()
+    value["definition_ids"] = ["rhfactorv1:sha256:" + "0" * 64]
+    _reidentify(value, "factor_set_id", "rhfactorsetv1:sha256:")
+    with pytest.raises(FactorContractError) as definition_error:
+        FactorSetRef.from_dict(
+            value,
+            definitions=(_golden_definition(),),
+            dataset_snapshot=snapshot,
+            foundation=foundation,
+        )
+    _assert_error(
+        definition_error,
+        ContractErrorCode.INPUT_CLOSURE_VIOLATION,
+        "$.definition_ids",
+    )
+
+    arguments = _factor_set_arguments()
+    arguments["selected_view_ref_ids"] = ("rhviewrefv1:sha256:" + "0" * 64,)
+    with pytest.raises(FactorContractError) as view_error:
+        FactorSetRef.create(**arguments)
+    _assert_error(
+        view_error,
+        ContractErrorCode.INPUT_CLOSURE_VIOLATION,
+        "$.selected_view_ref_ids",
+    )
+
+
+@pytest.mark.parametrize(
+    "invalid_definition_id",
+    [
+        {"unexpected": "object"},
+        ["array"],
+        42,
+        True,
+        None,
+    ],
+)
+def test_factor_set_ref_definition_ids_reject_non_string_types(
+    invalid_definition_id: Any,
+) -> None:
+    factor_set = _factor_set()
+    definition = _golden_definition()
+    value = factor_set.to_dict()
+    value["definition_ids"] = [definition.definition_id, invalid_definition_id]
+    _reidentify(value, "factor_set_id", "rhfactorsetv1:sha256:")
+    snapshot, foundation = _snapshot_and_foundation()
+
+    with pytest.raises(FactorContractError) as error:
+        FactorSetRef.from_json(
+            canonical_json_bytes(value),
+            definitions=(definition,),
+            dataset_snapshot=snapshot,
+            foundation=foundation,
+        )
+
+    _assert_error(error, ContractErrorCode.TYPE_ERROR, "$.definition_ids[1]")
+
+
+def test_factor_set_ref_definition_ids_still_reject_duplicate_strings() -> None:
+    factor_set = _factor_set()
+    definition = _golden_definition()
+    value = factor_set.to_dict()
+    value["definition_ids"] = [definition.definition_id, definition.definition_id]
+    _reidentify(value, "factor_set_id", "rhfactorsetv1:sha256:")
+    snapshot, foundation = _snapshot_and_foundation()
+
+    with pytest.raises(FactorContractError) as error:
+        FactorSetRef.from_json(
+            canonical_json_bytes(value),
+            definitions=(definition,),
+            dataset_snapshot=snapshot,
+            foundation=foundation,
+        )
+
+    _assert_error(error, ContractErrorCode.INVALID_VALUE, "$.definition_ids")
+
+
+def test_factor_set_parent_requires_exact_identity_and_correlation() -> None:
+    parent = _factor_set()
+    child_arguments = _factor_set_arguments()
+    child_arguments.update(
+        output_content_bytes=canonical_json_bytes({"rows": [{"value": "0.250"}]}),
+        causation=Causation("factor_set", parent.factor_set_id),
+        parent=parent,
+    )
+    child_arguments["output_artifact_ref"] = OutputArtifactRef.create(
+        schema_digest=_sha256(child_arguments["output_schema_bytes"]),
+        content_digest=_sha256(child_arguments["output_content_bytes"]),
+    )
+    child = FactorSetRef.create(**child_arguments)
+    assert child.causation.id == parent.factor_set_id
+    missing_parent = child.to_dict()
+    snapshot, foundation = _snapshot_and_foundation()
+    with pytest.raises(FactorContractError) as missing_error:
+        FactorSetRef.from_dict(
+            missing_parent,
+            definitions=(_golden_definition(),),
+            dataset_snapshot=snapshot,
+            foundation=foundation,
+        )
+    _assert_error(missing_error, ContractErrorCode.LINEAGE_VIOLATION, "$.causation")
+    wrong_correlation = dict(child_arguments)
+    wrong_correlation["correlation_id"] = "different_run"
+    with pytest.raises(FactorContractError) as correlation_error:
+        FactorSetRef.create(**wrong_correlation)
+    _assert_error(correlation_error, ContractErrorCode.LINEAGE_VIOLATION, "$.correlation_id")
+
+
+def test_legacy_bridge_is_explicit_lossy_and_preserves_all_four_historical_fields() -> None:
+    definition = _golden_definition()
+    legacy = FactorVersion(
+        factor_id="factor:demo-momentum",
+        version="1.0.0",
+        definition_sha256="b" * 64,
+        dataset_schema_version="1.0.0",
+    )
+    binding = LegacyFactorBinding.create(
+        definition=definition,
+        legacy_factor_id=legacy.factor_id,
+        legacy_version=legacy.version,
+        legacy_definition_sha256=legacy.definition_sha256,
+        legacy_dataset_schema_version=legacy.dataset_schema_version,
+        canonical_input_schema_digest=definition.input_schema_digest,
+        correspondence_evidence_digest="sha256:" + "5" * 64,
+    )
+    assert bind_legacy_factor(legacy, definition, binding) is definition
+    assert project_legacy_factor(definition, binding) == legacy
+    assert legacy.version_id == "factor:demo-momentum@1.0.0"
+    assert legacy.definition_sha256 != definition.definition_id.rsplit(":", maxsplit=1)[-1]
+    assert LegacyFactorBinding.from_json(binding.to_json(), definition=definition) == binding
+
+    mismatched = FactorVersion(
+        factor_id="factor:different",
+        version=legacy.version,
+        definition_sha256=legacy.definition_sha256,
+        dataset_schema_version=legacy.dataset_schema_version,
+    )
+    with pytest.raises(FactorContractError) as mismatch_error:
+        bind_legacy_factor(mismatched, definition, binding)
+    _assert_error(mismatch_error, ContractErrorCode.LEGACY_BINDING_MISMATCH, "$.binding")
+
+
+def test_bare_legacy_factor_or_id_cannot_enter_factor_set_contract() -> None:
+    legacy = FactorVersion("factor:demo-momentum", "1.0.0", "b" * 64, "1.0.0")
+    arguments = _factor_set_arguments()
+    arguments["definitions"] = (legacy,)
+    with pytest.raises(FactorContractError) as legacy_error:
+        FactorSetRef.create(**arguments)
+    _assert_error(legacy_error, ContractErrorCode.TYPE_ERROR, "$.definitions[0]")
+    arguments["definitions"] = (legacy.version_id,)
+    with pytest.raises(FactorContractError) as id_error:
+        FactorSetRef.create(**arguments)
+    _assert_error(id_error, ContractErrorCode.TYPE_ERROR, "$.definitions[0]")

tests/test_governed_pipeline.py

@@ -0,0 +1,338 @@
+"""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
+    assert result.factor_version.version_id == "factor:demo-momentum@1.0.0"
+    assert result.factor_version.definition_sha256 == "b" * 64
+    assert result.strategy_version.version_id == "strategy:demo-top2@1.0.0"
+    assert result.backtest_run.run_id == (
+        "backtest-run:e74403571f6a73c98b380220748957a422518c0bed883fabc4b93ebe13f05a37"
+    )
+    assert result.backtest_run.config_hash == (
+        "40a3c804a2dc940161a626d1a5d25817c13463e685005e37fc48c41d1e20b87b"
+    )
+    assert result.portfolio_target.target_id == (
+        "portfolio-target:ab2d398489aa9a292ee155a1098e9340beac0a924874cdaeb5b7b4d379ac9ce8"
+    )
+    assert result.risk_decision.decision_id == (
+        "risk-decision:95924926bd327e44beeb15a63f47d14c5e77b97533fc3227fba2eda68e9b423d"
+    )
+    assert result.order_intent.intent_id == (
+        "order-intent:73c349086c2c05b68424ace9286896eb21507b9080da5ff9b96b58c88d8f6ac1"
+    )
+
+    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(),
+        )

tests/test_performance_evidence_contract.py

@@ -0,0 +1,727 @@
+"""Closed performance-evidence contract conformance tests."""
+
+from __future__ import annotations
+
+import copy
+import hashlib
+import json
+from dataclasses import replace
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from quant_engine.artifact import (
+    PERFORMANCE_EVIDENCE_SCHEMA_VERSION,
+    PERFORMANCE_METRIC_SCHEMA_ID,
+    PERFORMANCE_METHODOLOGY_ID,
+    BacktestEvidenceManifest,
+    EvidenceQualification,
+    PerformanceEvidenceError,
+    PerformanceEvidenceErrorCode,
+    PerformanceEvidenceV1,
+    PerformanceMetricAvailability,
+    ResearchRunArtifact,
+    build_backtest_evidence_manifest,
+    build_performance_evidence,
+    build_research_run_artifact,
+)
+from quant_engine.execution import ExecutionConfig
+from quant_engine.factor_contracts import (
+    ActorIdentity,
+    AvailabilityMode,
+    Causation,
+    DataFoundationEnvelope,
+    DatasetSnapshotEnvelope,
+    FactorInput,
+    FactorSetRef,
+    InputBinding,
+    OutputArtifactRef,
+    OutputCoverage,
+    OutputQuality,
+    OutputQualityCheck,
+    ProducerIdentity,
+    ViewAvailability,
+    canonical_json_bytes,
+    factor_definition_from_alpha158,
+    factor_input_schema_digest,
+)
+from quant_engine.governed_pipeline import BacktestRunRef
+from quant_engine.metrics import TRADING_DAYS_PER_YEAR, benchmark_summary, summary
+from quant_engine.research_pipeline import FactorBacktestResult, run_factor_backtest_research
+
+
+ROOT = Path(__file__).resolve().parents[1]
+FACTOR_FIXTURE = ROOT / "tests" / "fixtures" / "factor-contracts-v1.golden.json"
+PERFORMANCE_FIXTURE = (
+    ROOT / "tests" / "fixtures" / "performance-evidence-v1.golden.json"
+)
+VIEW_REF_ID = "rhviewrefv1:sha256:bf776bcd26d940fafde1d650776a5505fb3fe8b5b068c351622bf2c42385629c"
+VIEW_SCHEMA_DIGEST = "sha256:0123456789abcdef0123456789abcdef0123456789abcdef0123456789abcdef"
+CALENDAR_REVISION_ID = "rhcalv1:sha256:1f4ca22557063389badd669cf774bb35234066e847646682dccfe411e252a078"
+ACTION_REVISION_ID = "rhcav1:sha256:0f947df29f152bfa2c6ab0da7a0d464670c7ad2526cd10f2a7939b42fee5c275"
+PARAMETERS = {"lag_sessions": 1, "top_k": 1}
+
+
+def _sha256(value: bytes) -> str:
+    return f"sha256:{hashlib.sha256(value).hexdigest()}"
+
+
+def _accepted_authorities() -> tuple[
+    DatasetSnapshotEnvelope,
+    DataFoundationEnvelope,
+    FactorSetRef,
+]:
+    fixture = json.loads(FACTOR_FIXTURE.read_text(encoding="utf-8"))
+    snapshot = DatasetSnapshotEnvelope.from_dict(fixture["dataset_snapshot"])
+    foundation = DataFoundationEnvelope.from_dict(fixture["data_foundation"])
+    factor_input = FactorInput("market", VIEW_SCHEMA_DIGEST, ("close", "volume"))
+    definition = factor_definition_from_alpha158(
+        "alpha_005",
+        version="1.0.0",
+        parameters={},
+        inputs=(factor_input,),
+        implementation_digest="sha256:" + "1" * 64,
+        input_schema_digest=factor_input_schema_digest((factor_input,)),
+        valid_from="2026-01-01T00:00:00.000000Z",
+        valid_until="2027-01-01T00:00:00Z",
+        warmup_sessions=10,
+        lag_sessions=1,
+        producer=ProducerIdentity("quant_engine", "1.0.0"),
+        code_revision="c" * 40,
+    )
+    output_schema_bytes = canonical_json_bytes(fixture["output_schema"])
+    output_content_bytes = canonical_json_bytes(fixture["output_content"])
+    artifact_ref = OutputArtifactRef.create(
+        schema_digest=_sha256(output_schema_bytes),
+        content_digest=_sha256(output_content_bytes),
+    )
+    factor_set = FactorSetRef.create(
+        definitions=(definition,),
+        dataset_snapshot=snapshot,
+        foundation=foundation,
+        selected_view_ref_ids=(VIEW_REF_ID,),
+        input_bindings=(
+            InputBinding(
+                definition.definition_id,
+                "market",
+                VIEW_REF_ID,
+                VIEW_SCHEMA_DIGEST,
+            ),
+        ),
+        view_availability=(
+            ViewAvailability(VIEW_REF_ID, "2026-01-02T23:50:00Z", "sha256:" + "2" * 64),
+        ),
+        output_quality=OutputQuality(
+            "passed",
+            (OutputQualityCheck("finite_values", "passed", "sha256:" + "3" * 64),),
+        ),
+        output_coverage=OutputCoverage(
+            "complete",
+            1,
+            1,
+            "row",
+            "alpha_005.cn_a",
+            "sha256:" + "4" * 64,
+        ),
+        output_schema_bytes=output_schema_bytes,
+        output_content_bytes=output_content_bytes,
+        output_artifact_ref=artifact_ref,
+        availability_mode=AvailabilityMode.AS_AVAILABLE,
+        evaluation_at="2026-01-03T11:00:00Z",
+        computed_at="2026-01-03T10:15:00Z",
+        artifact_available_at="2026-01-03T10:20:00Z",
+        producer=ProducerIdentity("quant_engine", "1.0.0"),
+        code_revision="c" * 40,
+        actor=ActorIdentity("service", "factor_worker_v1"),
+        correlation_id="research_run_001",
+        causation=Causation("foundation", foundation.foundation_id),
+        evidence_scope="synthetic_fixture",
+        decision_eligible=False,
+    )
+    return snapshot, foundation, factor_set
+
+
+def _configuration_digest() -> str:
+    return _sha256(
+        json.dumps(
+            PARAMETERS,
+            ensure_ascii=False,
+            sort_keys=True,
+            separators=(",", ":"),
+            allow_nan=False,
+        ).encode("utf-8")
+    )
+
+
+def _run_ref(**overrides: Any) -> BacktestRunRef:
+    snapshot, foundation, factor_set = _accepted_authorities()
+    arguments: dict[str, Any] = {
+        "dataset_snapshot": snapshot,
+        "foundation": foundation,
+        "factor_set": factor_set,
+        "universe_digest": "sha256:" + "5" * 64,
+        "trading_calendar_revision_ids": (CALENDAR_REVISION_ID,),
+        "corporate_action_revision_ids": (ACTION_REVISION_ID,),
+        "strategy_id": "alpha-top1",
+        "strategy_version": "1.0.0",
+        "strategy_digest": "sha256:" + "6" * 64,
+        "execution_model_version": "1.0.0",
+        "execution_model_digest": "sha256:" + "7" * 64,
+        "cost_model_version": "1.0.0",
+        "cost_model_digest": "sha256:" + "8" * 64,
+        "random_seed": 7,
+        "code_revision": "d" * 40,
+        "environment_lock_digest": "sha256:" + "9" * 64,
+        "configuration_digest": _configuration_digest(),
+        "evaluation_at": "2026-01-08T01:00:00Z",
+        "computed_at": "2026-01-08T02:00:00Z",
+    }
+    arguments.update(overrides)
+    return BacktestRunRef.create(**arguments)
+
+
+def _backtest_result() -> FactorBacktestResult:
+    dates = pd.date_range("2026-01-05", periods=4, freq="B")
+    scores = pd.DataFrame({"A": [2.0, 0.0], "B": [1.0, 3.0]}, index=dates[:2])
+    opens = pd.DataFrame(
+        {"A": [10.0, 10.0, 15.0, 15.0], "B": [20.0, 20.0, 20.0, 21.0]},
+        index=dates,
+    )
+    closes = pd.DataFrame(
+        {"A": [10.0, 12.0, 15.0, 15.0], "B": [20.0, 20.0, 18.0, 21.0]},
+        index=dates,
+    )
+    return run_factor_backtest_research(
+        scores,
+        opens,
+        closes,
+        top_k=1,
+        execution_price_field="open",
+        valuation_price_field="close",
+        initial_cash=1_000.0,
+        config=ExecutionConfig(
+            commission_bps=0,
+            stamp_tax_bps=0,
+            slippage_bps=0,
+            min_trade_amount=0,
+        ),
+    )
+
+
+def _artifact(
+    run_ref: BacktestRunRef,
+    benchmark_kind: str,
+) -> tuple[ResearchRunArtifact, FactorBacktestResult]:
+    result = _backtest_result()
+    benchmark_id: str | None
+    benchmark_returns: pd.Series | None
+    if benchmark_kind == "absent":
+        benchmark_id = None
+        benchmark_returns = None
+    elif benchmark_kind == "estimable":
+        benchmark_id = "000300.SH"
+        benchmark_returns = pd.Series(
+            [0.0, 0.01, -0.01, 0.02],
+            index=result.returns.index,
+            name="benchmark_return",
+        )
+    elif benchmark_kind == "zero_active_variance":
+        benchmark_id = "000300.SH"
+        benchmark_returns = result.returns.rename("benchmark_return")
+    elif benchmark_kind == "zero_benchmark_variance":
+        benchmark_id = "000300.SH"
+        benchmark_returns = pd.Series(
+            np.zeros(len(result.returns)),
+            index=result.returns.index,
+            name="benchmark_return",
+        )
+    else:
+        raise AssertionError(f"unknown benchmark_kind: {benchmark_kind}")
+    artifact = build_research_run_artifact(
+        result,
+        run_id=run_ref.run_id,
+        strategy_id=run_ref.strategy_id,
+        strategy_name="Alpha Top 1",
+        strategy_version=run_ref.strategy_version,
+        engine_version="1.2.0",
+        code_revision=run_ref.code_revision,
+        data_snapshot_id=run_ref.dataset_snapshot_id,
+        calendar="CN-A",
+        timezone="Asia/Shanghai",
+        started_at="2026-01-08T10:00:00+08:00",
+        finished_at="2026-01-08T10:01:00+08:00",
+        parameters=PARAMETERS,
+        benchmark_id=benchmark_id,
+        benchmark_returns=benchmark_returns,
+    )
+    return artifact, result
+
+
+def _case(
+    benchmark_kind: str,
+) -> tuple[PerformanceEvidenceV1, ResearchRunArtifact, BacktestRunRef, BacktestEvidenceManifest]:
+    run_ref = _run_ref()
+    artifact, _ = _artifact(run_ref, benchmark_kind)
+    manifest = build_backtest_evidence_manifest(
+        run_ref,
+        artifact,
+        artifact_available_at="2026-01-08T02:05:00Z",
+        qualification=EvidenceQualification.CONTRACT_QUALIFIED,
+    )
+    return (
+        build_performance_evidence(artifact, run_ref, manifest),
+        artifact,
+        run_ref,
+        manifest,
+    )
+
+
+def _metric_map(evidence: PerformanceEvidenceV1) -> dict[str, Any]:
+    return {metric.key: metric for metric in evidence.metrics}
+
+
+def _mutate_frozen(value: Any, field: str, replacement: object) -> Any:
+    changed = copy.copy(value)
+    object.__setattr__(changed, field, replacement)
+    return changed
+
+
+def _assert_error(
+    error: pytest.ExceptionInfo[PerformanceEvidenceError],
+    code: PerformanceEvidenceErrorCode,
+    path: str,
+) -> None:
+    assert error.value.code is code
+    assert error.value.path == path
+
+
+def test_present_evidence_is_deterministic_content_addressed_and_three_party_closed() -> None:
+    first, artifact, run_ref, manifest = _case("estimable")
+    second = build_performance_evidence(artifact, run_ref, manifest)
+
+    assert first == second
+    assert first.schema_version == PERFORMANCE_EVIDENCE_SCHEMA_VERSION
+    assert first.performance_evidence_id.startswith("rhperformanceevidencev1:sha256:")
+    assert first.document_sha256.startswith("sha256:")
+    assert first.authority == "quant_engine"
+    assert first.scope == "offline_research_only"
+    assert first.run_id == first.backtest_run_ref_id == run_ref.run_id == manifest.run_id
+    assert first.backtest_evidence_manifest_id == manifest.manifest_id
+    assert first.backtest_evidence_manifest_evidence_digest == manifest.evidence_digest
+    assert first.backtest_evidence_qualification == "contract_qualified"
+    assert first.research_artifact_content_digest == f"sha256:{artifact.content_sha256}"
+    assert first.performance_table_logical_name == "performance"
+    assert first.performance_table_row_count == 1
+    assert first.performance_row_digest.startswith("sha256:")
+    assert first.benchmark_series_digest is not None
+    assert first.canonical_bytes() == first.to_json().encode("utf-8")
+    assert not first.canonical_bytes().endswith(b"\n")
+    document_payload = first.to_dict()
+    document_payload.pop("document_sha256")
+    expected_document = json.dumps(
+        document_payload,
+        ensure_ascii=False,
+        sort_keys=True,
+        separators=(",", ":"),
+        allow_nan=False,
+    ).encode("utf-8")
+    assert _sha256(expected_document) == first.document_sha256
+    assert PerformanceEvidenceV1.from_dict(
+        first.to_dict(),
+        artifact=artifact,
+        run_ref=run_ref,
+        evidence_manifest=manifest,
+    ) == first
+
+
+def test_methodology_and_metrics_bind_the_actual_artifact_builder_path() -> None:
+    evidence, artifact, _, _ = _case("estimable")
+    result = _backtest_result()
+    expected_absolute = summary(result.returns, rf=0.0)
+    benchmark = artifact.nav.set_index("trade_date")["benchmark_return"]
+    benchmark.index = result.returns.index
+    expected_relative = benchmark_summary(
+        result.returns,
+        benchmark,
+        risk_free_daily=0.0,
+        annualization=TRADING_DAYS_PER_YEAR,
+    )
+    metrics = _metric_map(evidence)
+
+    assert evidence.methodology.methodology_id == PERFORMANCE_METHODOLOGY_ID
+    assert evidence.metric_schema_id == PERFORMANCE_METRIC_SCHEMA_ID
+    assert evidence.methodology.return_type == "simple"
+    assert evidence.methodology.source_frequency == "1d"
+    assert evidence.methodology.periods_per_year == TRADING_DAYS_PER_YEAR == 252
+    assert evidence.methodology.annual_risk_free == 0.0
+    assert evidence.methodology.benchmark_risk_free_daily == 0.0
+    assert evidence.methodology.benchmark_alignment == "exact_session_index"
+    assert metrics["annualized_return"].value == pytest.approx(
+        expected_absolute["ann_return"]
+    )
+    assert metrics["sharpe_ratio"].value == pytest.approx(expected_absolute["sharpe"])
+    assert metrics["tracking_error"].value == pytest.approx(
+        expected_relative["tracking_error"]
+    )
+    assert metrics["alpha"].value == pytest.approx(expected_relative["alpha"])
+    assert all(metric.methodology_id == PERFORMANCE_METHODOLOGY_ID for metric in metrics.values())
+    assert all(metric.metric_schema_id == PERFORMANCE_METRIC_SCHEMA_ID for metric in metrics.values())
+
+
+def test_relative_metric_availability_is_closed_for_present_absent_and_unestimable() -> None:
+    present, *_ = _case("estimable")
+    absent, *_ = _case("absent")
+    zero_active, *_ = _case("zero_active_variance")
+    zero_benchmark, *_ = _case("zero_benchmark_variance")
+
+    present_metrics = _metric_map(present)
+    assert all(
+        present_metrics[key].availability is PerformanceMetricAvailability.AVAILABLE
+        for key in ("tracking_error", "information_ratio", "alpha", "beta")
+    )
+    absent_metrics = _metric_map(absent)
+    assert absent.benchmark_series_digest is None
+    assert absent.benchmark_id == ""
+    assert absent.benchmark_alignment_policy == "none"
+    assert all(
+        absent_metrics[key].value is None
+        and absent_metrics[key].availability
+        is PerformanceMetricAvailability.BENCHMARK_ABSENT
+        for key in ("tracking_error", "information_ratio", "alpha", "beta")
+    )
+    zero_active_metrics = _metric_map(zero_active)
+    assert zero_active_metrics["tracking_error"].value == pytest.approx(0.0)
+    assert (
+        zero_active_metrics["information_ratio"].availability
+        is PerformanceMetricAvailability.NOT_ESTIMABLE_ACTIVE_VARIANCE
+    )
+    assert zero_active_metrics["information_ratio"].value is None
+    zero_benchmark_metrics = _metric_map(zero_benchmark)
+    assert np.isfinite(zero_benchmark_metrics["tracking_error"].value)
+    for key in ("alpha", "beta"):
+        assert zero_benchmark_metrics[key].value is None
+        assert (
+            zero_benchmark_metrics[key].availability
+            is PerformanceMetricAvailability.NOT_ESTIMABLE_BENCHMARK_VARIANCE
+        )
+
+
+def test_golden_covers_present_absent_and_both_unestimable_states() -> None:
+    expected = {
+        "schema_version": 1,
+        "source_commit": "a724e1e57a99d1304a932d01ee836bac56c5c15c",
+        "source_tree": "4774e88442d25bf79a54eab3d7106ff4d0ba9603",
+        "cases": {
+            name: _case(name)[0].to_dict()
+            for name in (
+                "estimable",
+                "zero_active_variance",
+                "zero_benchmark_variance",
+                "absent",
+            )
+        },
+    }
+    assert json.loads(PERFORMANCE_FIXTURE.read_text(encoding="utf-8")) == expected
+
+
+@pytest.mark.parametrize(
+    ("owner", "field", "replacement", "code", "path"),
+    [
+        (
+            "run_ref",
+            "run_id",
+            "rhbacktestrunv1:sha256:" + "0" * 64,
+            PerformanceEvidenceErrorCode.IDENTITY_MISMATCH,
+            "$.backtest_run_ref.run_id",
+        ),
+        (
+            "manifest",
+            "manifest_id",
+            "rhbacktestevidencev1:sha256:" + "0" * 64,
+            PerformanceEvidenceErrorCode.EVIDENCE_MISMATCH,
+            "$.backtest_evidence_manifest.manifest_id",
+        ),
+        (
+            "manifest",
+            "qualification",
+            EvidenceQualification.EXPLORATORY,
+            PerformanceEvidenceErrorCode.AUTHORITY_REJECTED,
+            "$.backtest_evidence_manifest.qualification",
+        ),
+    ],
+)
+def test_owner_identity_and_authority_mismatches_fail_closed(
+    owner: str,
+    field: str,
+    replacement: object,
+    code: PerformanceEvidenceErrorCode,
+    path: str,
+) -> None:
+    _, artifact, run_ref, manifest = _case("estimable")
+    changed_run_ref = _mutate_frozen(run_ref, field, replacement) if owner == "run_ref" else run_ref
+    changed_manifest = (
+        _mutate_frozen(manifest, field, replacement) if owner == "manifest" else manifest
+    )
+    with pytest.raises(PerformanceEvidenceError) as rejected:
+        build_performance_evidence(artifact, changed_run_ref, changed_manifest)
+    _assert_error(rejected, code, path)
+
+
+def test_performance_table_row_and_benchmark_digest_mismatches_fail_closed() -> None:
+    evidence, artifact, run_ref, manifest = _case("estimable")
+    performance = artifact.performance
+    performance.loc[0, "n_days"] += 1
+    changed_artifact = replace(artifact, _performance=performance)
+    with pytest.raises(PerformanceEvidenceError) as table_mismatch:
+        build_performance_evidence(changed_artifact, run_ref, manifest)
+    _assert_error(
+        table_mismatch,
+        PerformanceEvidenceErrorCode.EVIDENCE_MISMATCH,
+        "$.artifact.tables.performance.content_digest",
+    )
+
+    payload = evidence.to_dict()
+    payload["benchmark_series_digest"] = "sha256:" + "0" * 64
+    with pytest.raises(PerformanceEvidenceError) as benchmark_mismatch:
+        PerformanceEvidenceV1.from_dict(
+            payload,
+            artifact=artifact,
+            run_ref=run_ref,
+            evidence_manifest=manifest,
+        )
+    _assert_error(
+        benchmark_mismatch,
+        PerformanceEvidenceErrorCode.BENCHMARK_INVALID,
+        "$.benchmark_series_digest",
+    )
+
+
+def test_manifest_closure_covers_non_performance_artifact_tables() -> None:
+    _, artifact, run_ref, manifest = _case("estimable")
+    nav = artifact.nav
+    nav.loc[0, "nav"] += 0.01
+    changed_artifact = replace(artifact, _nav=nav)
+
+    with pytest.raises(PerformanceEvidenceError) as rejected:
+        build_performance_evidence(changed_artifact, run_ref, manifest)
+
+    _assert_error(
+        rejected,
+        PerformanceEvidenceErrorCode.EVIDENCE_MISMATCH,
+        "$.artifact.tables.nav.content_digest",
+    )
+
+
+def test_row_and_benchmark_mutations_change_their_digests_and_document_identity() -> None:
+    original, artifact, run_ref, _ = _case("estimable")
+    performance = artifact.performance
+    performance.loc[0, "sharpe"] += 0.01
+    changed_performance_artifact = replace(artifact, _performance=performance)
+    changed_performance_manifest = build_backtest_evidence_manifest(
+        run_ref,
+        changed_performance_artifact,
+        artifact_available_at="2026-01-08T02:05:00Z",
+    )
+    changed_performance = build_performance_evidence(
+        changed_performance_artifact,
+        run_ref,
+        changed_performance_manifest,
+    )
+    assert changed_performance.performance_row_digest != original.performance_row_digest
+    assert changed_performance.performance_evidence_id != original.performance_evidence_id
+
+    nav = artifact.nav
+    nav.loc[0, "benchmark_nav"] += 0.01
+    changed_benchmark_artifact = replace(artifact, _nav=nav)
+    changed_benchmark_manifest = build_backtest_evidence_manifest(
+        run_ref,
+        changed_benchmark_artifact,
+        artifact_available_at="2026-01-08T02:05:00Z",
+    )
+    changed_benchmark = build_performance_evidence(
+        changed_benchmark_artifact,
+        run_ref,
+        changed_benchmark_manifest,
+    )
+    assert changed_benchmark.benchmark_series_digest != original.benchmark_series_digest
+    assert changed_benchmark.performance_row_digest == original.performance_row_digest
+    assert changed_benchmark.performance_evidence_id != original.performance_evidence_id
+
+
+def test_relative_metric_null_reasons_cannot_be_invented() -> None:
+    _, artifact, run_ref, _ = _case("estimable")
+    performance = artifact.performance
+    performance.loc[0, "alpha"] = float("nan")
+    changed_artifact = replace(artifact, _performance=performance)
+    changed_manifest = build_backtest_evidence_manifest(
+        run_ref,
+        changed_artifact,
+        artifact_available_at="2026-01-08T02:05:00Z",
+    )
+    with pytest.raises(PerformanceEvidenceError) as false_alpha_domain:
+        build_performance_evidence(changed_artifact, run_ref, changed_manifest)
+    _assert_error(
+        false_alpha_domain,
+        PerformanceEvidenceErrorCode.METRIC_INVALID,
+        "$.metrics.alpha.value",
+    )
+
+    _, absent_artifact, absent_run_ref, _ = _case("absent")
+    absent_performance = absent_artifact.performance
+    absent_performance.loc[0, "tracking_error"] = 0.0
+    changed_absent = replace(absent_artifact, _performance=absent_performance)
+    changed_absent_manifest = build_backtest_evidence_manifest(
+        absent_run_ref,
+        changed_absent,
+        artifact_available_at="2026-01-08T02:05:00Z",
+    )
+    with pytest.raises(PerformanceEvidenceError) as false_absence:
+        build_performance_evidence(
+            changed_absent,
+            absent_run_ref,
+            changed_absent_manifest,
+        )
+    _assert_error(
+        false_absence,
+        PerformanceEvidenceErrorCode.BENCHMARK_INVALID,
+        "$.metrics.tracking_error.availability",
+    )
+
+
+def test_artifact_builder_enforces_strict_benchmark_session_alignment() -> None:
+    run_ref = _run_ref()
+    result = _backtest_result()
+    misaligned = pd.Series(
+        [0.0, 0.01, -0.01, 0.02],
+        index=result.returns.index.shift(1, freq="B"),
+    )
+    with pytest.raises(ValueError, match="matching indexes"):
+        build_research_run_artifact(
+            result,
+            run_id=run_ref.run_id,
+            strategy_id=run_ref.strategy_id,
+            strategy_name="Alpha Top 1",
+            strategy_version=run_ref.strategy_version,
+            engine_version="1.2.0",
+            code_revision=run_ref.code_revision,
+            data_snapshot_id=run_ref.dataset_snapshot_id,
+            calendar="CN-A",
+            timezone="Asia/Shanghai",
+            started_at="2026-01-08T10:00:00+08:00",
+            finished_at="2026-01-08T10:01:00+08:00",
+            parameters=PARAMETERS,
+            benchmark_id="000300.SH",
+            benchmark_returns=misaligned,
+        )
+
+
+@pytest.mark.parametrize(
+    ("column", "value", "path"),
+    [
+        ("total_ret", -1.01, "$.metrics.total_return.value"),
+        ("ann_ret", -1.01, "$.metrics.annualized_return.value"),
+        ("ann_volatility", -0.01, "$.metrics.annualized_volatility.value"),
+        ("max_dd", 0.01, "$.metrics.maximum_drawdown.value"),
+        ("win_rate", 1.01, "$.metrics.win_rate.value"),
+        ("tracking_error", -0.01, "$.metrics.tracking_error.value"),
+        ("n_trades", True, "$.metrics.trade_count.value"),
+    ],
+)
+def test_metric_domains_reject_invalid_source_values(
+    column: str,
+    value: object,
+    path: str,
+) -> None:
+    _, artifact, run_ref, _ = _case("estimable")
+    performance = artifact.performance.astype(object)
+    performance.at[0, column] = value
+    changed_artifact = replace(artifact, _performance=performance)
+    changed_manifest = build_backtest_evidence_manifest(
+        run_ref,
+        changed_artifact,
+        artifact_available_at="2026-01-08T02:05:00Z",
+    )
+    with pytest.raises(PerformanceEvidenceError) as rejected:
+        build_performance_evidence(changed_artifact, run_ref, changed_manifest)
+    _assert_error(rejected, PerformanceEvidenceErrorCode.METRIC_INVALID, path)
+
+
+def test_closed_parser_rejects_unknown_non_ascii_non_finite_bool_and_unsafe_integer() -> None:
+    evidence, artifact, run_ref, manifest = _case("estimable")
+
+    mutations: list[tuple[dict[str, Any], PerformanceEvidenceErrorCode, str]] = []
+    unknown = evidence.to_dict()
+    unknown["unexpected"] = "value"
+    mutations.append((unknown, PerformanceEvidenceErrorCode.TYPE_ERROR, "$.unexpected"))
+    non_ascii = evidence.to_dict()
+    non_ascii["métric"] = "value"
+    mutations.append((non_ascii, PerformanceEvidenceErrorCode.TYPE_ERROR, "$.métric"))
+    non_finite = evidence.to_dict()
+    non_finite["metrics"][0]["value"] = float("inf")
+    mutations.append(
+        (non_finite, PerformanceEvidenceErrorCode.METRIC_INVALID, "$.metrics[0].value")
+    )
+    bool_number = evidence.to_dict()
+    bool_number["methodology"]["periods_per_year"] = True
+    mutations.append(
+        (
+            bool_number,
+            PerformanceEvidenceErrorCode.METHODOLOGY_MISMATCH,
+            "$.methodology.periods_per_year",
+        )
+    )
+    unsafe = evidence.to_dict()
+    unsafe["performance_table_row_count"] = 2**53
+    mutations.append(
+        (
+            unsafe,
+            PerformanceEvidenceErrorCode.EVIDENCE_MISMATCH,
+            "$.performance_table_row_count",
+        )
+    )
+
+    for payload, code, path in mutations:
+        with pytest.raises(PerformanceEvidenceError) as rejected:
+            PerformanceEvidenceV1.from_dict(
+                payload,
+                artifact=artifact,
+                run_ref=run_ref,
+                evidence_manifest=manifest,
+            )
+        _assert_error(rejected, code, path)
+
+
+def test_public_mapping_has_no_raw_inputs_storage_or_runtime_authority() -> None:
+    evidence, *_ = _case("estimable")
+    payload = evidence.to_dict()
+    serialized = evidence.to_json().lower()
+    forbidden_keys = {
+        "parameters",
+        "params_json",
+        "returns",
+        "nav",
+        "benchmark_series",
+        "table_bytes",
+        "locator",
+        "uri",
+        "credential",
+        "decision_eligible",
+        "publication_eligible",
+        "paper_trading",
+        "live_trading",
+        "investment_advice",
+    }
+
+    def keys(value: object) -> set[str]:
+        if isinstance(value, dict):
+            return set(value) | {key for item in value.values() for key in keys(item)}
+        if isinstance(value, list):
+            return {key for item in value for key in keys(item)}
+        return set()
+
+    assert not (keys(payload) & forbidden_keys)
+    for token in ("postgres://", "mysql://", "s3://", "credential", "broker"):
+        assert token not in serialized