Regras de auditoria e evidências de paridade entre mecanismos de backtest
Código Machine Learning for Trading
Resumo
Este registro de auditoria define como várias estruturas de backtesting são comparadas com ML4T em estudos de caso com estratégias reais e uma operação sintética de estresse. Especifica regras de comparação para execuções ordenadas, horários, valores monetários da conta, quantidades e outros campos numéricos. Também registra edições das estruturas, identificadores imutáveis, status de compatibilidade, políticas de desempenho e referências a evidências de correção, tempo e escala. A medição do tempo fica restrita à chamada do mecanismo; carregamento de entradas, inferência, preparação, extração e apresentação de resultados são excluídos.
Os registros mostram por que as alegações de paridade devem se limitar a cada modelo de ativo e implementação. Por exemplo, o VectorBT OSS é marcado como incompatível com a operação de futuros da CME por não ter multiplicadores de contrato nativos nem contabilização de margem, enquanto as duas edições do VectorBT são incompatíveis com a contabilização de funding e margem de perpétuos de cripto. Os tempos da auditoria se baseiam em processos isolados e pares aprovados nas verificações de correção; portanto, não comprovam desempenho universal em diferentes conjuntos de dados ou máquinas. Este arquivo documenta evidências e políticas, em vez de ensinar um sinal de trading ou uma estratégia de alocação.
Ideias principais
- A paridade de backtests depende de regras explícitas para ordem de execução, horários e tolerâncias numéricas.
- As evidências de tempo abrangem chamadas do mecanismo e excluem a preparação de dados e adaptadores, além do processamento dos resultados.
- A compatibilidade de uma estrutura deve ser avaliada conforme os requisitos contábeis do ativo.
- A auditoria limita as comparações de tempo publicadas a pares de estruturas e estudos de caso que passam nas verificações de correção.
- Os resultados das operações registradas não estabelecem uma classificação universal de velocidade.
Tags
Texto completo
# framework_parity_audit.json
```json
{
"audit_generated_at": "2026-09-03T11:44:14.344493+00:00",
"comparison_policy": {
"account_money_fields": [
"cash",
"commission",
"equity",
"final_value"
],
"account_money_quantum": "0.01",
"fill_order": "canonical timestamp, asset, side, quantity, price, commission",
"meaning": "account-money gaps round to zero cents; quantity gaps round to zero at 1e-5 shares or contracts; all other numeric gaps round to zero at 1e-8",
"quantity_quantum": "0.00001",
"record_numeric_quantum": "0.00000001",
"rounding": "ROUND_HALF_EVEN",
"timestamp_domain": {
"cme_futures": "session date",
"crypto_perps_funding": "exact UTC event timestamp",
"etfs": "session date",
"fx_pairs": "session date",
"us_equities_panel": "session date"
}
},
"engine_commit": "7034236519cc0a99df6ef34a21d07ec2a81fc87c",
"engine_source_sha256": "8011ecdaf90807b4fe4fb5ada518a237311774b32891179bf1fad0a0baad8a58",
"evidence": {
"correctness": "https://github.com/ml4t/backtest/blob/0c3de46b1881ce6cb9fd72b672a1dba38ebc383b/validation/REAL_STRATEGY_RESULTS.json",
"performance": "https://github.com/ml4t/backtest/blob/0c3de46b1881ce6cb9fd72b672a1dba38ebc383b/validation/REAL_STRATEGY_PERFORMANCE.json",
"source_artifacts": [
{
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"sha256": "d7c6460fcb5ebf9ecf74c31d06e227996c66f488de46a5beaa2145104209916b"
},
{
"path": "validation/REAL_STRATEGY_PERFORMANCE.json",
"sha256": "7276dfba4356c4ce2e6e45c256e4431a4e01cd47ca5710e9d341893286929c40"
},
{
"path": "validation/LARGE_SCALE_RESULTS.json",
"sha256": "fb3a06a7c485bfb131dfef286ff743af18fa4ba673f5f1075245d7a9123cb3f5"
}
],
"synthetic_stress": "https://github.com/ml4t/backtest/blob/0c3de46b1881ce6cb9fd72b672a1dba38ebc383b/validation/LARGE_SCALE_RESULTS.json"
},
"frameworks": {
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}
```Exibido na íntegra, com atribuição conforme a licença da fonte. Licença: MIT
Este resumo foi escrito pelo agente de pesquisa da Stratmill com base no original; não é uma cópia da fonte.