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fix: session filter, short weight cap, futures multiplier in cash checks, CAGR overflow #129

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stefan-jansen merged 13 commits into from
Oct 15, 2026
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fix: session filter, short weight cap, futures multiplier in cash checks, CAGR overflow #129

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3f6eac0
Fix overnight session filter dropping bars before the session date
cursoragent Oct 7, 2026
abc0c4c
Cap short rebalance targets at max_single_weight
cursoragent Oct 7, 2026
e78288d
Include contract multiplier in next-bar simple cash checks
cursoragent Oct 7, 2026
cd49c85
Include contract multiplier in buying-power reservation
cursoragent Oct 7, 2026
c9397df
Return infinite CAGR when annualization overflows
cursoragent Oct 7, 2026
2852d25
Merge remote-tracking branch 'origin/main' into codex/complete-pr129
stefan-jansen Oct 14, 2026
30c946a
test: cover futures cash limits through Engine
stefan-jansen Oct 13, 2026
6ccebce
validation: record combined issue 128 correctness matrix
stefan-jansen Oct 13, 2026
8a4ed2c
validation: rederive real-strategy correctness for issue 128 repairs
stefan-jansen Oct 13, 2026
07362ec
fix(validation): fall back to architecture for blank processor identity
stefan-jansen Oct 13, 2026
de39018
validation: retain fresh qualified real-strategy timings and generate…
stefan-jansen Oct 14, 2026
742132a
Merge remote-tracking branch 'origin/main' into codex/complete-pr129
stefan-jansen Oct 15, 2026
574f34b
Merge remote-tracking branch 'origin/main' into codex/complete-pr129
stefan-jansen Oct 15, 2026
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38 changes: 19 additions & 19 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -340,25 +340,25 @@ The table reports engine-call wall time for all 17 correctness-passing pairs. Th

| Real strategy | Pinned framework | Framework median (95% CI), s | ML4T median (95% CI), s | Framework / ML4T median |
|---|---|---:|---:|---:|
| ETF allocation | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.293 (0.290-0.298) | 0.424 (0.421-0.434) | 0.691x |
| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.192 (0.176-0.493) | 0.414 (0.413-0.417) | 0.462x |
| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.631 (9.428-9.710) | 0.439 (0.434-0.444) | 21.954x |
| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.888 (3.878-3.959) | 0.621 (0.618-0.626) | 6.256x |
| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.638 (2.516-2.707) | 0.750 (0.743-0.757) | 3.520x |
| CME futures | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.290 (0.289-0.294) | 0.410 (0.406-0.958) | 0.708x |
| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 6.639 (5.831-8.332) | 0.428 (0.422-1.235) | 15.504x |
| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.828 (2.737-2.952) | 1.327 (0.657-2.183) | 2.131x |
| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.288 (0.286-0.294) | 0.148 (0.147-0.149) | 1.947x |
| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.144 (0.142-0.146) | 0.145 (0.145-0.146) | 0.988x |
| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.434 (0.430-0.440) | 0.147 (0.146-0.149) | 2.947x |
| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.953 (0.936-1.164) | 0.158 (0.155-0.164) | 6.033x |
| US equity panel | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 3.806 (3.336-4.341) | 23.306 (21.906-25.833) | 0.163x |
| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 17.116 (17.069-17.633) | 25.738 (24.983-27.163) | 0.665x |
| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 668.611 (642.993-718.202) | 26.887 (26.406-29.406) | 24.868x |
| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 117.445 (116.308-118.391) | 26.821 (25.473-27.644) | 4.379x |
| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 48.436 (48.221-48.986) | 27.688 (27.575-27.796) | 1.749x |

Measured 2026-09-24 on `Linux-6.8.0-139-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
| ETF allocation | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.317 (0.297-0.330) | 0.420 (0.418-0.422) | 0.754x |
| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.173 (0.170-0.175) | 0.416 (0.414-0.417) | 0.415x |
| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.408 (9.378-9.537) | 0.437 (0.435-0.438) | 21.549x |
| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.846 (3.841-3.867) | 0.615 (0.613-0.622) | 6.254x |
| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.571 (2.515-2.635) | 0.749 (0.743-0.754) | 3.433x |
| CME futures | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.286 (0.283-0.291) | 0.410 (0.408-0.412) | 0.697x |
| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.475 (2.465-2.535) | 0.422 (0.419-0.425) | 5.864x |
| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.793 (2.751-2.871) | 0.653 (0.649-0.658) | 4.280x |
| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.291 (0.283-0.294) | 0.149 (0.147-0.163) | 1.951x |
| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.146 (0.138-0.152) | 0.147 (0.145-0.149) | 0.988x |
| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.439 (0.434-0.447) | 0.152 (0.149-0.154) | 2.891x |
| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.961 (0.935-0.979) | 0.159 (0.158-0.161) | 6.055x |
| US equity panel | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.865 (0.851-1.004) | 22.991 (22.877-23.162) | 0.038x |
| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 17.246 (17.166-17.345) | 21.418 (21.157-24.293) | 0.805x |
| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 505.641 (499.073-515.494) | 23.585 (21.878-26.507) | 21.439x |
| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 110.874 (105.635-113.330) | 22.831 (22.502-31.470) | 4.856x |
| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 49.967 (48.757-50.759) | 40.260 (34.451-40.927) | 1.241x |

Measured 2026-10-13 on `Linux-7.0.0-38-generic-x86_64-with-glibc2.43` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).

### Synthetic diagnostic scenarios

Expand Down
38 changes: 19 additions & 19 deletions docs/index.md
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Original file line number Diff line number Diff line change
Expand Up @@ -159,25 +159,25 @@ The table reports engine-call wall time for all 17 correctness-passing pairs. Th

| Real strategy | Pinned framework | Framework median (95% CI), s | ML4T median (95% CI), s | Framework / ML4T median |
|---|---|---:|---:|---:|
| ETF allocation | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.293 (0.290-0.298) | 0.424 (0.421-0.434) | 0.691x |
| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.192 (0.176-0.493) | 0.414 (0.413-0.417) | 0.462x |
| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.631 (9.428-9.710) | 0.439 (0.434-0.444) | 21.954x |
| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.888 (3.878-3.959) | 0.621 (0.618-0.626) | 6.256x |
| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.638 (2.516-2.707) | 0.750 (0.743-0.757) | 3.520x |
| CME futures | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.290 (0.289-0.294) | 0.410 (0.406-0.958) | 0.708x |
| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 6.639 (5.831-8.332) | 0.428 (0.422-1.235) | 15.504x |
| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.828 (2.737-2.952) | 1.327 (0.657-2.183) | 2.131x |
| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.288 (0.286-0.294) | 0.148 (0.147-0.149) | 1.947x |
| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.144 (0.142-0.146) | 0.145 (0.145-0.146) | 0.988x |
| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.434 (0.430-0.440) | 0.147 (0.146-0.149) | 2.947x |
| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.953 (0.936-1.164) | 0.158 (0.155-0.164) | 6.033x |
| US equity panel | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 3.806 (3.336-4.341) | 23.306 (21.906-25.833) | 0.163x |
| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 17.116 (17.069-17.633) | 25.738 (24.983-27.163) | 0.665x |
| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 668.611 (642.993-718.202) | 26.887 (26.406-29.406) | 24.868x |
| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 117.445 (116.308-118.391) | 26.821 (25.473-27.644) | 4.379x |
| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 48.436 (48.221-48.986) | 27.688 (27.575-27.796) | 1.749x |

Measured 2026-09-24 on `Linux-6.8.0-139-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
| ETF allocation | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.317 (0.297-0.330) | 0.420 (0.418-0.422) | 0.754x |
| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.173 (0.170-0.175) | 0.416 (0.414-0.417) | 0.415x |
| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.408 (9.378-9.537) | 0.437 (0.435-0.438) | 21.549x |
| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.846 (3.841-3.867) | 0.615 (0.613-0.622) | 6.254x |
| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.571 (2.515-2.635) | 0.749 (0.743-0.754) | 3.433x |
| CME futures | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.286 (0.283-0.291) | 0.410 (0.408-0.412) | 0.697x |
| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.475 (2.465-2.535) | 0.422 (0.419-0.425) | 5.864x |
| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.793 (2.751-2.871) | 0.653 (0.649-0.658) | 4.280x |
| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.291 (0.283-0.294) | 0.149 (0.147-0.163) | 1.951x |
| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.146 (0.138-0.152) | 0.147 (0.145-0.149) | 0.988x |
| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.439 (0.434-0.447) | 0.152 (0.149-0.154) | 2.891x |
| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.961 (0.935-0.979) | 0.159 (0.158-0.161) | 6.055x |
| US equity panel | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.865 (0.851-1.004) | 22.991 (22.877-23.162) | 0.038x |
| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 17.246 (17.166-17.345) | 21.418 (21.157-24.293) | 0.805x |
| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 505.641 (499.073-515.494) | 23.585 (21.878-26.507) | 21.439x |
| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 110.874 (105.635-113.330) | 22.831 (22.502-31.470) | 4.856x |
| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 49.967 (48.757-50.759) | 40.260 (34.451-40.927) | 1.241x |

Measured 2026-10-13 on `Linux-7.0.0-38-generic-x86_64-with-glibc2.43` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).

### Synthetic diagnostic scenarios

Expand Down
38 changes: 19 additions & 19 deletions docs/user-guide/profiles.md
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Expand Up @@ -198,25 +198,25 @@ The table reports engine-call wall time for all 17 correctness-passing pairs. Th

| Real strategy | Pinned framework | Framework median (95% CI), s | ML4T median (95% CI), s | Framework / ML4T median |
|---|---|---:|---:|---:|
| ETF allocation | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.293 (0.290-0.298) | 0.424 (0.421-0.434) | 0.691x |
| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.192 (0.176-0.493) | 0.414 (0.413-0.417) | 0.462x |
| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.631 (9.428-9.710) | 0.439 (0.434-0.444) | 21.954x |
| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.888 (3.878-3.959) | 0.621 (0.618-0.626) | 6.256x |
| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.638 (2.516-2.707) | 0.750 (0.743-0.757) | 3.520x |
| CME futures | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.290 (0.289-0.294) | 0.410 (0.406-0.958) | 0.708x |
| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 6.639 (5.831-8.332) | 0.428 (0.422-1.235) | 15.504x |
| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.828 (2.737-2.952) | 1.327 (0.657-2.183) | 2.131x |
| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.288 (0.286-0.294) | 0.148 (0.147-0.149) | 1.947x |
| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.144 (0.142-0.146) | 0.145 (0.145-0.146) | 0.988x |
| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.434 (0.430-0.440) | 0.147 (0.146-0.149) | 2.947x |
| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.953 (0.936-1.164) | 0.158 (0.155-0.164) | 6.033x |
| US equity panel | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 3.806 (3.336-4.341) | 23.306 (21.906-25.833) | 0.163x |
| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 17.116 (17.069-17.633) | 25.738 (24.983-27.163) | 0.665x |
| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 668.611 (642.993-718.202) | 26.887 (26.406-29.406) | 24.868x |
| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 117.445 (116.308-118.391) | 26.821 (25.473-27.644) | 4.379x |
| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 48.436 (48.221-48.986) | 27.688 (27.575-27.796) | 1.749x |

Measured 2026-09-24 on `Linux-6.8.0-139-generic-x86_64-with-glibc2.39` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).
| ETF allocation | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.317 (0.297-0.330) | 0.420 (0.418-0.422) | 0.754x |
| ETF allocation | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.173 (0.170-0.175) | 0.416 (0.414-0.417) | 0.415x |
| ETF allocation | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 9.408 (9.378-9.537) | 0.437 (0.435-0.438) | 21.549x |
| ETF allocation | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 3.846 (3.841-3.867) | 0.615 (0.613-0.622) | 6.254x |
| ETF allocation | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.571 (2.515-2.635) | 0.749 (0.743-0.754) | 3.433x |
| CME futures | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.286 (0.283-0.291) | 0.410 (0.408-0.412) | 0.697x |
| CME futures | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 2.475 (2.465-2.535) | 0.422 (0.419-0.425) | 5.864x |
| Crypto perpetual funding | [LEAN 18001](https://github.com/QuantConnect/Lean) | 2.793 (2.751-2.871) | 0.653 (0.649-0.658) | 4.280x |
| FX allocation (USD-quoted pairs) | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.291 (0.283-0.294) | 0.149 (0.147-0.163) | 1.951x |
| FX allocation (USD-quoted pairs) | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 0.146 (0.138-0.152) | 0.147 (0.145-0.149) | 0.988x |
| FX allocation (USD-quoted pairs) | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 0.439 (0.434-0.447) | 0.152 (0.149-0.154) | 2.891x |
| FX allocation (USD-quoted pairs) | [LEAN 18001](https://github.com/QuantConnect/Lean) | 0.961 (0.935-0.979) | 0.159 (0.158-0.161) | 6.055x |
| US equity panel | [VectorBT Pro 2026.6.27](https://vectorbt.pro/) | 0.865 (0.851-1.004) | 22.991 (22.877-23.162) | 0.038x |
| US equity panel | [VectorBT OSS 1.1.0](https://pypi.org/project/vectorbt/1.1.0/) | 17.246 (17.166-17.345) | 21.418 (21.157-24.293) | 0.805x |
| US equity panel | [Backtrader 1.9.78.123](https://pypi.org/project/backtrader/1.9.78.123/) | 505.641 (499.073-515.494) | 23.585 (21.878-26.507) | 21.439x |
| US equity panel | [Zipline Reloaded 3.1.1](https://pypi.org/project/zipline-reloaded/3.1.1/) | 110.874 (105.635-113.330) | 22.831 (22.502-31.470) | 4.856x |
| US equity panel | [LEAN 18001](https://github.com/QuantConnect/Lean) | 49.967 (48.757-50.759) | 40.260 (34.451-40.927) | 1.241x |

Measured 2026-10-13 on `Linux-7.0.0-38-generic-x86_64-with-glibc2.43` with 24 logical CPUs. Each side used one isolated warm-up process and ten isolated measured processes. The timer includes only the engine call; it excludes input loading, model inference, target construction, adapter preparation, result extraction, serialization, reporting. These measurements apply only to the named strategy, framework version, frozen input bundle, and machine. Raw samples and bootstrap intervals are retained in [real-strategy performance evidence](https://github.com/ml4t/backtest/blob/main/validation/REAL_STRATEGY_PERFORMANCE.json).

### Synthetic diagnostic scenarios

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6 changes: 5 additions & 1 deletion src/ml4t/backtest/analytics/metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -162,7 +162,11 @@ def cagr(
if final_value <= 0:
return -1.0 # Total loss

return (final_value / initial_value) ** (1 / years) - 1
try:
return (final_value / initial_value) ** (1 / years) - 1
except OverflowError:
# Sub-day windows make the annualized power exceed the float range.
return float("inf")


def calmar_ratio(cagr_value: float, max_dd: float) -> float:
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