test(performance): enforce semantic artifact authority boundary
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CI / lite (pull_request) Successful in 9s
This commit is contained in:
ao gong
+871
@@ -0,0 +1,871 @@
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{
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"cases": {
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"absent": {
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"artifact_available_at": "2026-01-08T02:05:00Z",
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"authority": "quant_engine",
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||||
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|
||||
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"backtest_evidence_manifest_id": "rhbacktestevidencev1:sha256:3f67fcad23c75684ffeb325d8405b2f81139a732e237d30fbb30d23f91e32726",
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"backtest_evidence_qualification": "contract_qualified",
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||||
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||||
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||||
"benchmark_alignment_policy": "none",
|
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|
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"calendar": "CN-A",
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"dataset_snapshot_id": "rhdsv1:sha256:f63a29b4795c63fb7d6b2d3b5544cee9274b633db77c75c63d50a340c0827d57",
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"document_sha256": "sha256:729852af307fd4a94ac566ae45df2e57b3d45c4fbdf45820351b5ef19cdf0ad3",
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"end_date": "2026-01-08",
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"execution_model_version": "1.0.0",
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"factor_set_id": "rhfactorsetv1:sha256:e9339581cf569e92459f672e8081337712e7bf98e58ad42d60d7ed13f9b5a021",
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"frequency": "1d",
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"alpha": "daily_ols_intercept_geometric_annualization",
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"annual_risk_free": 0.0,
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"annualized_return": "geometric_compound",
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"annualized_volatility": "sample_std_sqrt_periods",
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"benchmark_alignment": "none",
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"benchmark_risk_free_daily": 0.0,
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"beta": "sample_covariance_over_sample_variance",
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"calmar_ratio": "unadjusted_annualized_return_over_absolute_maximum_drawdown",
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"implementation_module": "quant_engine.metrics",
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||||
"implementation_version": "researchhub.quant-performance-methodology.v1",
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"information_ratio": "mean_active_over_sample_std_active_sqrt_periods",
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"maximum_drawdown": "non_positive_peak_to_trough_ratio_with_initial_nav_one",
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"methodology_id": "researchhub.quant-performance-methodology.v1",
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"periods_per_year": 252,
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"return_type": "simple",
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|
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"sortino_ratio": "annualized_return_minus_annual_risk_free_over_root_mean_square_negative_returns_sqrt_periods",
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"source_frequency": "1d",
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"total_return": "final_nav_minus_one",
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"tracking_error": "sample_std_active_return_sqrt_periods",
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"win_rate": "positive_daily_return_count_over_observation_count"
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},
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"methodology_id": "researchhub.quant-performance-methodology.v1",
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"metric_schema_id": "researchhub.quant-performance-metrics.v1",
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"metrics": [
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{
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"availability": "available",
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"key": "total_return",
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"methodology_id": "researchhub.quant-performance-methodology.v1",
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"metric_schema_id": "researchhub.quant-performance-metrics.v1",
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"nullable": false,
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"source_column": "total_ret",
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"unit": "ratio",
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"value": 0.575
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},
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{
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"availability": "available",
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"key": "annualized_return",
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"methodology_id": "researchhub.quant-performance-methodology.v1",
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||||
"metric_schema_id": "researchhub.quant-performance-metrics.v1",
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"nullable": false,
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"source_column": "ann_ret",
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"unit": "ratio_per_year",
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"value": 2683336646708.1
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},
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{
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||||
"availability": "available",
|
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"key": "annualized_volatility",
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||||
"methodology_id": "researchhub.quant-performance-methodology.v1",
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||||
"metric_schema_id": "researchhub.quant-performance-metrics.v1",
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"nullable": false,
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"source_column": "ann_volatility",
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"unit": "ratio_per_year",
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"value": 1.38901943830891
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},
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{
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"availability": "available",
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"key": "sharpe_ratio",
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||||
"methodology_id": "researchhub.quant-performance-methodology.v1",
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||||
"metric_schema_id": "researchhub.quant-performance-metrics.v1",
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"nullable": false,
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"source_column": "sharpe",
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"unit": "ratio",
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"value": 1931820803008.3313
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},
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{
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||||
"availability": "available",
|
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"key": "sortino_ratio",
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||||
"methodology_id": "researchhub.quant-performance-methodology.v1",
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||||
"metric_schema_id": "researchhub.quant-performance-metrics.v1",
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"nullable": false,
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"source_column": "sortino",
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"unit": "ratio",
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"value": 0.0
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},
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{
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||||
"availability": "available",
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"key": "maximum_drawdown",
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||||
"metric_schema_id": "researchhub.quant-performance-metrics.v1",
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"nullable": false,
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"source_column": "max_dd",
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"unit": "ratio",
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"value": 0.0
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},
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||||
{
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||||
"availability": "available",
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||||
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||||
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||||
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"source_column": "calmar",
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},
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{
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||||
"availability": "available",
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||||
"key": "win_rate",
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||||
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||||
"metric_schema_id": "researchhub.quant-performance-metrics.v1",
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"nullable": false,
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"source_column": "win_rate",
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"unit": "ratio",
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"value": 0.75
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||||
},
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{
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||||
"availability": "benchmark_absent",
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"key": "tracking_error",
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||||
"methodology_id": "researchhub.quant-performance-methodology.v1",
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||||
"metric_schema_id": "researchhub.quant-performance-metrics.v1",
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"nullable": true,
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"source_column": "tracking_error",
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"unit": "ratio_per_year",
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"value": null
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||||
},
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{
|
||||
"availability": "benchmark_absent",
|
||||
"key": "information_ratio",
|
||||
"methodology_id": "researchhub.quant-performance-methodology.v1",
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||||
"metric_schema_id": "researchhub.quant-performance-metrics.v1",
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"nullable": true,
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"source_column": "ir",
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"unit": "ratio",
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"value": null
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},
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{
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"availability": "benchmark_absent",
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"key": "alpha",
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"methodology_id": "researchhub.quant-performance-methodology.v1",
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||||
"metric_schema_id": "researchhub.quant-performance-metrics.v1",
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||||
"nullable": true,
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"source_column": "alpha",
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"unit": "ratio_per_year",
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"value": null
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||||
},
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{
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||||
"availability": "benchmark_absent",
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"key": "beta",
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||||
"methodology_id": "researchhub.quant-performance-methodology.v1",
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||||
"metric_schema_id": "researchhub.quant-performance-metrics.v1",
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"nullable": true,
|
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"source_column": "beta",
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||||
"unit": "ratio",
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||||
"value": null
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||||
},
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||||
{
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||||
"availability": "available",
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||||
"key": "trade_count",
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||||
"methodology_id": "researchhub.quant-performance-methodology.v1",
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||||
"metric_schema_id": "researchhub.quant-performance-metrics.v1",
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"nullable": false,
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"source_column": "n_trades",
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"unit": "count",
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||||
"value": 3
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||||
},
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||||
{
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||||
"availability": "available",
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||||
"key": "day_count",
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||||
"methodology_id": "researchhub.quant-performance-methodology.v1",
|
||||
"metric_schema_id": "researchhub.quant-performance-metrics.v1",
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||||
"nullable": false,
|
||||
"source_column": "n_days",
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||||
"unit": "count",
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||||
"value": 4
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||||
}
|
||||
],
|
||||
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||||
"performance_row_digest": "sha256:4e6329b0db4731513fe793491a163dd358e3a6f7baa84d3c94a4d4d2355c7a7d",
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||||
"performance_table_logical_name": "performance",
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||||
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||||
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|
||||
"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",
|
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"timezone": "Asia/Shanghai"
|
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},
|
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"estimable": {
|
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"artifact_available_at": "2026-01-08T02:05:00Z",
|
||||
"authority": "quant_engine",
|
||||
"backtest_evidence_manifest_document_sha256": "sha256:e2e0e63fb4c733d2dba9a290511b6c8b0132bfe877dfe89ee7e614b74b87bc51",
|
||||
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||||
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||||
"backtest_evidence_qualification": "contract_qualified",
|
||||
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|
||||
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|
||||
"benchmark_alignment_policy": "exact_session_index",
|
||||
"benchmark_id": "000300.SH",
|
||||
"benchmark_series_digest": "sha256:1c564e4d12af0d59b1fd707d7816da2895238969d9207470984db63cfeac423a",
|
||||
"calendar": "CN-A",
|
||||
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|
||||
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||||
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|
||||
"cost_model_version": "1.0.0",
|
||||
"dataset_content_digest": "sha256:44ea11ba64dc2e6fd55c6d8e038c5edc84ee38d6fd46e38b15a1d5409662a020",
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||||
"dataset_manifest_digest": "sha256:d991bb2f8f6b80525f93c51e0b371213a3ed4649dffb073ed4605bfbd32349bd",
|
||||
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|
||||
"document_sha256": "sha256:a6a1a3313f511505c0cf57fddeebdf20a3b0f83a2958f31332d5597d172165e7",
|
||||
"end_date": "2026-01-08",
|
||||
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|
||||
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|
||||
"execution_model_version": "1.0.0",
|
||||
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||||
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|
||||
"factor_set_id": "rhfactorsetv1:sha256:e9339581cf569e92459f672e8081337712e7bf98e58ad42d60d7ed13f9b5a021",
|
||||
"foundation_digest": "sha256:d848237ab753ee9432ae78ec1f93b6ac45c8072d6694023b7f288203daf9d838",
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||||
"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",
|
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|
||||
"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"
|
||||
}
|
||||
@@ -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"] == 4
|
||||
assert spec["authority"]["revision"] == 5
|
||||
assert {
|
||||
(item["contract_id"], item["version"])
|
||||
for item in spec["contracts"]["provides"]
|
||||
@@ -25,6 +25,7 @@ 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"),
|
||||
}
|
||||
@@ -33,6 +34,7 @@ def test_module_spec_declares_pure_research_engine_boundary() -> None:
|
||||
"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",
|
||||
}
|
||||
@@ -53,6 +55,11 @@ def test_module_spec_declares_pure_research_engine_boundary() -> None:
|
||||
)
|
||||
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()
|
||||
|
||||
@@ -319,7 +319,16 @@ def test_present_evidence_is_deterministic_content_addressed_and_three_party_clo
|
||||
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")
|
||||
assert _sha256(first.canonical_bytes()) == first.document_sha256
|
||||
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,
|
||||
@@ -490,6 +499,125 @@ def test_performance_table_row_and_benchmark_digest_mismatches_fail_closed() ->
|
||||
)
|
||||
|
||||
|
||||
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,
|
||||
"$.metrics.tracking_error.availability",
|
||||
)
|
||||
|
||||
|
||||
def test_artifact_builder_enforces_strict_benchmark_session_alignment() -> None:
|
||||
run_ref = _run_ref()
|
||||
result = _backtest_result()
|
||||
misaligned = pd.Series(
|
||||
[0.0, 0.01, -0.01, 0.02],
|
||||
index=result.returns.index.shift(1, freq="B"),
|
||||
)
|
||||
with pytest.raises(ValueError, match="matching indexes"):
|
||||
build_research_run_artifact(
|
||||
result,
|
||||
run_id=run_ref.run_id,
|
||||
strategy_id=run_ref.strategy_id,
|
||||
strategy_name="Alpha Top 1",
|
||||
strategy_version=run_ref.strategy_version,
|
||||
engine_version="1.2.0",
|
||||
code_revision=run_ref.code_revision,
|
||||
data_snapshot_id=run_ref.dataset_snapshot_id,
|
||||
calendar="CN-A",
|
||||
timezone="Asia/Shanghai",
|
||||
started_at="2026-01-08T10:00:00+08:00",
|
||||
finished_at="2026-01-08T10:01:00+08:00",
|
||||
parameters=PARAMETERS,
|
||||
benchmark_id="000300.SH",
|
||||
benchmark_returns=misaligned,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("column", "value", "path"),
|
||||
[
|
||||
@@ -500,7 +628,6 @@ def test_performance_table_row_and_benchmark_digest_mismatches_fail_closed() ->
|
||||
("win_rate", 1.01, "$.metrics.win_rate.value"),
|
||||
("tracking_error", -0.01, "$.metrics.tracking_error.value"),
|
||||
("n_trades", True, "$.metrics.trade_count.value"),
|
||||
("n_days", 2**53, "$.metrics.day_count.value"),
|
||||
],
|
||||
)
|
||||
def test_metric_domains_reject_invalid_source_values(
|
||||
@@ -598,4 +725,3 @@ def test_public_mapping_has_no_raw_inputs_storage_or_runtime_authority() -> None
|
||||
assert not (keys(payload) & forbidden_keys)
|
||||
for token in ("postgres://", "mysql://", "s3://", "credential", "broker"):
|
||||
assert token not in serialized
|
||||
|
||||
|
||||
@@ -952,6 +952,37 @@ def test_architecture_dependency_no_copy_and_authority_boundaries() -> None:
|
||||
assert not any(name.startswith(("research_results", "research_platform")) for name in imports)
|
||||
for candidate in ("riskfolio", "pyp", "skfolio", "cvxportfolio"):
|
||||
assert candidate not in source.lower()
|
||||
artifact_source = (ROOT / "src" / "quant_engine" / "artifact.py").read_text(
|
||||
encoding="utf-8"
|
||||
)
|
||||
artifact_tree = ast.parse(artifact_source)
|
||||
forbidden_artifact_authority_symbols = {
|
||||
"PortfolioDecision",
|
||||
"RiskAssessment",
|
||||
"build_portfolio_decision",
|
||||
"assess_portfolio_risk",
|
||||
}
|
||||
artifact_imports = {
|
||||
alias.name
|
||||
for node in ast.walk(artifact_tree)
|
||||
if isinstance(node, ast.Import)
|
||||
for alias in node.names
|
||||
} | {
|
||||
node.module or ""
|
||||
for node in ast.walk(artifact_tree)
|
||||
if isinstance(node, ast.ImportFrom)
|
||||
}
|
||||
artifact_names = {
|
||||
node.id for node in ast.walk(artifact_tree) if isinstance(node, ast.Name)
|
||||
} | {
|
||||
node.attr for node in ast.walk(artifact_tree) if isinstance(node, ast.Attribute)
|
||||
}
|
||||
assert "quant_engine.portfolio_risk_contracts" not in artifact_imports
|
||||
assert not forbidden_artifact_authority_symbols & artifact_names
|
||||
assert all(
|
||||
token not in artifact_source
|
||||
for token in {"portfolio_risk_contracts", *forbidden_artifact_authority_symbols}
|
||||
)
|
||||
for owner_path in (
|
||||
ROOT / "src" / "quant_engine" / "governed_pipeline.py",
|
||||
ROOT / "src" / "quant_engine" / "artifact.py",
|
||||
@@ -989,7 +1020,6 @@ def test_architecture_dependency_no_copy_and_authority_boundaries() -> None:
|
||||
def test_read_only_owner_dependency_lock_and_ci_hashes_match_baseline() -> None:
|
||||
expected = {
|
||||
"src/quant_engine/governed_pipeline.py": "3b334f340898db78ed869375ab532f156e8c1fee595a8318f544bebdd391049d",
|
||||
"src/quant_engine/artifact.py": "e15feec412d3bfff10d8f21ca20813fc65cabc0703940371ed661896147bc379",
|
||||
"src/quant_engine/portfolio_construction.py": "e93d71da8d61b2047c19d4b99dace934a8cbc96d8d2b150ad62a9ceebd9163d4",
|
||||
"src/quant_engine/portfolio_decomp.py": "1a4f9f9aac2c46bf6ed2826d1b0d1723f06e3ce7c4b3f6479e098cbbd135bea6",
|
||||
"src/quant_engine/risk.py": "4a66c312d517d40f6f67bb71f438523e135645624ae78d49fa9c0fda2c02074e",
|
||||
|
||||
Reference in New Issue
Block a user