כללי ביקורת וראיות לשקילות בין מנועי בקטסט
קוד Machine Learning for Trading
סיכום
רשומת ביקורת זו מגדירה כיצד משווים כמה מסגרות בקטסט ל-ML4T במקרי אסטרטגיה אמיתיים ובעומס מאמץ סינתטי. היא מפרטת את כללי ההשוואה לסדר המילויים, חותמות זמן, ערכים כספיים בחשבון, כמויות ושדות מספריים אחרים. היא מתעדת גם מהדורות מסגרת, מזהים בלתי משתנים, סטטוס תמיכה, מדיניות ביצועים והפניות לראיות על תקינות, תזמון והיקף. מדידת זמן הריצה מוגבלת לקריאת המנוע, ואינה כוללת טעינת קלט, הסקה, הכנה, חילוץ ודיווח.
הרשומות ממחישות מדוע יש לתחום טענות לשקילות לפי מודל הנכס והמימוש. לדוגמה, VectorBT OSS מסומנת כלא נתמכת בעומס החוזים העתידיים של CME, משום שחסרים בה מכפילי חוזים מובנים וחישוב דרישות הביטחונות, בעוד ששתי מהדורות VectorBT אינן נתמכות לחישוב תשלומי מימון ודרישות ביטחונות בחוזים תמידיים בקריפטו. התזמונים בביקורת מבוססים על תהליכים מבודדים ועל זוגות שעברו בדיקות תקינות, ולכן אינם מבססים ביצועים אוניברסליים בין מערכי נתונים או מחשבים. הקובץ מתעד ראיות ומדיניות ואינו מלמד אות מסחר או אסטרטגיית הקצאה.
רעיונות מרכזיים
- שקילות בקטסט תלויה בכללים מפורשים לסדר המילויים, חותמות זמן וסבילויות מספריות.
- ראיות זמן הריצה מכסות קריאות מנוע ואינן כוללות הכנת נתונים ומתאמים או טיפול בתוצאות.
- יש להעריך תמיכת מסגרת מול דרישות החשבונאות של הנכס.
- הביקורת מגבילה את השוואות התזמון המתפרסמות לזוגות מסגרת ומקרה שעברו בדיקות תקינות.
- תוצאות עומסי העבודה המתועדים אינן מבססות דירוג מהירות אוניברסלי.
תגיות
הטקסט המלא
# 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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"artifact": "quantconnect/lean@sha256:ecd62b0e418d40d1d7c0cd95e90a94e397642a21d2c8810614830c8a4e9a8f70",
"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",
"license": "Apache-2.0",
"package": "zipline-reloaded",
"profile": "zipline_strict",
"python": ">=3.10",
"python_env_var": "ML4T_ZIPLINE_PYTHON",
"required_scenarios": 16,
"scenario_matrix": true,
"source": "https://pypi.org/project/zipline-reloaded/3.1.1/",
"source_commit": "09885a2ebc7567d40942c891b3879dc03c745070",
"unsupported_scenarios": 1,
"version": "3.1.1"
}
},
"library_commit": "0c3de46b1881ce6cb9fd72b672a1dba38ebc383b",
"performance_policy": {
"boundary": "engine call only",
"excluded": [
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"model inference",
"target construction",
"adapter preparation",
"result extraction",
"serialization",
"reporting"
],
"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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],
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},
{
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],
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],
"protocol_scope": {
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"position rules"
],
"shared_inputs": "frozen model-derived targets and historical market data",
"tested": [
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"order sequencing",
"fills",
"cash and margin",
"funding where applicable",
"valuation"
]
},
"real_strategy_records": [
{
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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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"source": "https://vectorbt.dev/api/portfolio/base/",
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},
{
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},
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}
```מוצג במלואו בציון המקור ובהתאם לרישיון שלו. רישיון: MIT
הסיכום נכתב בידי סוכן המחקר של Stratmill על סמך המקור; הוא אינו העתק של המקור.