fix: close final backtest contract boundaries

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": 5, "effective_from": "2026-09-01T00:00:00+08:00"},
+  "authority": {"scope": "module_metadata", "subject": "quant_engine", "owner": "quant-engine-owner", "source": "MODULE_SPEC.yaml", "revision": 3, "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",
@@ -11,7 +11,6 @@
       "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"
     ]
   },
@@ -19,8 +18,7 @@
     {"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": "backtest-evidence-contracts", "summary": "Identify governed offline backtest inputs and close existing research artifact evidence without 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": [
@@ -32,10 +30,7 @@
       {"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.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"},

README.md

@@ -28,7 +28,6 @@
 - `research_pipeline` — 因子日 → 下一真实交易日 → 显式执行价 → 日末估值 → 成本后绩效（防前视编排）
 - `governed_pipeline` — 数据快照 → 因子版本 → 策略版本 → 回测运行 → 目标组合 → 风险决策 → Paper 订单意图；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）
@@ -260,140 +259,6 @@ digest 等价，也不会把旧 run 静默升级为新合同。
 `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。

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

@@ -1,871 +0,0 @@
-{
-  "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",
-      "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": "sha256:729852af307fd4a94ac566ae45df2e57b3d45c4fbdf45820351b5ef19cdf0ad3",
-      "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",
-      "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": "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": [
-        {
-          "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
-        },
-        {
-          "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:d735abe76c28db429e107cb5ba6a1929c2c0f2084a6c4ac41b38ffbc02b2d2bd",
-      "performance_row_digest": "sha256:4e6329b0db4731513fe793491a163dd358e3a6f7baa84d3c94a4d4d2355c7a7d",
-      "performance_table_content_digest": "sha256:074b8511ff9a2154235e4dde8bac300658cb69f900e49f332d61560b82be9af5",
-      "performance_table_logical_name": "performance",
-      "performance_table_row_count": 1,
-      "performance_table_schema_digest": "sha256:16cef93a679761103ae405e622b7929abbfe07115bf276be4f164da5a16128d0",
-      "research_artifact_content_digest": "sha256:ad85ee1317bd8ce5fbef8fddc268b91be8678701ba5b8fbf6d61bf670f918bf9",
-      "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"
-    },
-    "estimable": {
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-      "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

@@ -1,129 +0,0 @@
-{
-  "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

@@ -16,7 +16,7 @@ def test_module_spec_declares_pure_research_engine_boundary() -> None:
     prohibited = " ".join(spec["bounded_context"]["prohibited_responsibilities"]).lower()
     for term in ("investment advice", "live order", "credentials", "source facts"):
         assert term in prohibited
-    assert spec["authority"]["revision"] == 5
+    assert spec["authority"]["revision"] == 3
     assert {
         (item["contract_id"], item["version"])
         for item in spec["contracts"]["provides"]
@@ -25,18 +25,12 @@ def test_module_spec_declares_pure_research_engine_boundary() -> None:
         ("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 {
@@ -54,19 +48,6 @@ def test_module_spec_declares_pure_research_engine_boundary() -> None:
         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"]

tests/test_performance_evidence_contract.py

@@ -1,727 +0,0 @@
-"""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