Правила аудита и данные о паритете движков бэктестинга
Код Machine Learning for Trading
Сводка
В этой записи аудита определено, как несколько фреймворков для бэктестинга сравниваются с ML4T на реальных практических примерах торговых стратегий и синтетической стрессовой нагрузке. В ней заданы правила сравнения упорядоченных исполнений, временных меток, денежных значений счёта, количеств и других числовых полей. Также указаны редакции фреймворков, неизменяемые идентификаторы, статус поддержки, правила оценки производительности и ссылки на данные о корректности, времени и масштабе. Время измеряется только для вызова движка; загрузка входных данных, инференс, подготовка, извлечение и представление результатов исключены.
Записи показывают, почему заявления о паритете нужно ограничивать конкретной моделью актива и реализацией. Например, VectorBT OSS отмечен как неподдерживаемый для нагрузки с фьючерсами CME, поскольку в нём нет встроенных множителей контрактов и учёта маржи; обе редакции VectorBT также не поддерживают учёт финансирования криптовалютных бессрочных контрактов и маржи. Замеры времени выполнены в изолированных процессах для пар, прошедших проверку корректности, поэтому они не устанавливают универсальную производительность на разных наборах данных и компьютерах. Этот файл документирует данные и правила, а не обучает торговому сигналу или распределению активов.
Ключевые идеи
- Паритет бэктестов зависит от явных правил упорядочивания исполнений, временных меток и числовых допусков.
- Данные о времени охватывают вызовы движка и не включают подготовку данных и адаптера, а также обработку результатов.
- Поддержку фреймворка нужно оценивать с учётом требований к учёту конкретного актива.
- В аудите сравнения времени ограничены парами фреймворков и практических примеров, прошедшими проверки корректности.
- Результаты зафиксированных нагрузок не устанавливают универсальный рейтинг скорости.
Теги
Полный текст
# 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": [
{
"path": "validation/REAL_STRATEGY_RESULTS.json",
"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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"access": "public",
"artifact": "backtrader-1.9.78.123-py2.py3-none-any.whl",
"display_name": "Backtrader",
"environment": ".venv-backtrader",
"immutable_id": "sha256:9a07a516b0de9155539a35c56e9404d8711dd7020b3d37b30495e83e1b9d5dfd",
"license": "GPL-3.0-or-later",
"package": "backtrader",
"profile": "backtrader_strict",
"python": "3.12 audit interpreter; upstream package metadata does not declare a range",
"python_env_var": "ML4T_BACKTRADER_PYTHON",
"required_scenarios": 17,
"scenario_matrix": true,
"source": "https://pypi.org/project/backtrader/1.9.78.123/",
"unsupported_scenarios": 0,
"version": "1.9.78.123"
},
"lean": {
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"cli_immutable_id": "sha256:eaa4c08f16295b76f005e429d9ca0d0453784dc1a40c1f5cbe5e50c02a05bd7c",
"cli_version": "1.0.228",
"display_name": "LEAN",
"environment": ".venv-lean",
"immutable_id": "sha256:ecd62b0e418d40d1d7c0cd95e90a94e397642a21d2c8810614830c8a4e9a8f70",
"license": "Apache-2.0",
"package": "lean",
"platform_artifact": "linux/amd64@sha256:cbe3f26b3f16c57be836b2cf913253d434f58e010c84ed038360d26b9df88307",
"profile": "lean",
"python": "3.11",
"python_env_var": "ML4T_LEAN_PYTHON",
"required_scenarios": 0,
"scenario_matrix": false,
"source": "https://github.com/QuantConnect/Lean",
"source_commit": "278fcb3d1b815b63ccadba68d7ae54422e34b792",
"unsupported_scenarios": 0,
"version": "18001"
},
"vectorbt_oss": {
"access": "public",
"artifact": "vectorbt-1.1.0.tar.gz",
"display_name": "VectorBT OSS",
"environment": ".venv-vectorbt-oss",
"immutable_id": "sha256:67a3b41466234485af70c18d201da105f1ebb2c1d1fac079db20059e45ddc73b",
"license": "Apache-2.0 WITH Commons-Clause",
"package": "vectorbt",
"profile": "vectorbt_oss_strict",
"python": ">=3.11,<3.15",
"python_env_var": "ML4T_VECTORBT_OSS_PYTHON",
"required_scenarios": 16,
"scenario_matrix": true,
"source": "https://pypi.org/project/vectorbt/1.1.0/",
"source_commit": "259d2d89fe2e7638baf3ca76c394937cd32b656d",
"unsupported_scenarios": 1,
"version": "1.1.0"
},
"vectorbt_pro": {
"access": "licensed",
"display_name": "VectorBT Pro",
"environment": ".venv-vectorbt-pro",
"immutable_id": "git:6e18cf0aa37849cfc20848f40f1d26ecfdc771b4",
"license": "Proprietary",
"package": "vectorbtpro",
"profile": "vectorbt_strict",
"python": ">=3.11",
"python_env_var": "ML4T_VECTORBT_PRO_PYTHON",
"required_scenarios": 17,
"scenario_matrix": true,
"source": "https://github.com/polakowo/vectorbt.pro",
"source_commit": "6e18cf0aa37849cfc20848f40f1d26ecfdc771b4",
"unsupported_scenarios": 0,
"version": "2026.6.27"
},
"zipline": {
"access": "public",
"artifact": "zipline_reloaded-3.1.1.tar.gz",
"display_name": "Zipline Reloaded",
"environment": ".venv-zipline",
"immutable_id": "sha256:4a305524616f7aad836f929e5a2ba5afc7db0e238757f47eb49487d9e2457a6f",
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"required_scenarios": 16,
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"source": "https://pypi.org/project/zipline-reloaded/3.1.1/",
"source_commit": "09885a2ebc7567d40942c891b3879dc03c745070",
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}
},
"library_commit": "0c3de46b1881ce6cb9fd72b672a1dba38ebc383b",
"performance_policy": {
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"excluded": [
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"model inference",
"target construction",
"adapter preparation",
"result extraction",
"serialization",
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],
"interval": "10,000-draw percentile bootstrap of the sample median",
"measured_processes": 10,
"process_isolation": true,
"publication_scope": "only correctness-passing case-study/framework pairs",
"warmup_processes": 1
},
"performance_records": [
{
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],
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{
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],
"protocol_scope": {
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],
"shared_inputs": "frozen model-derived targets and historical market data",
"tested": [
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]
},
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},
{
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},
{
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},
{
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],
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"scope": {
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},
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],
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},
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},
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},
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},
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]
}
```Полный текст с указанием источника опубликован на условиях его лицензии. Лицензия: MIT
Это краткое изложение подготовлено исследовательским агентом Stratmill по оригиналу и не является его копией.