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Add single-command GTL tuning matrix

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2026-07-13 13:13:18 +02:00
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@@ -804,6 +804,47 @@ statistics, and portfolio results are unchanged. This closes the local
backtest gate for the dual-purpose implementation. It does not itself authorize
or perform a production deployment.
#### GTL tuning matrix
Exact parity establishes a safe, explicit control, but it does not prove that
the inherited GTL constants are optimal. The `gtl_tuning` backtest arm exposes
only those constants to an offline configuration; the live scanner continues
to call the frozen default helper and cannot read this research configuration.
The single-command matrix contains 20 full-period arms. Each non-control arm
changes exactly one input:
| Knob | Frozen control | Alternatives | Question isolated |
|---|---:|---|---|
| History | All available bars | 252 / 504 / 756 bars | Is recent or long-cycle range geometry useful? |
| Candidate cap | 5 | 8 / unlimited | Does early pruning discard the useful headline? |
| Max target distance | Existing volatility-dependent rule | 5.5 / 8 ATR universally | Is the medium-volatility unlimited branch the hidden edge? |
| Traffic touch padding | 0.5% | 0 / 0.25% | Does padded price traffic carry information? |
| Proposal merge | 0.5% | 0.25% / 1% | Is proposal density or consolidation important? |
| Target zones | 2% | 1% / 3% | Does the reachable near edge manufacture the gate geometry? |
| Range centers | 20 | 12 / 32 | Is coarse ladder density the useful feature? |
| Pivots | Five-bar swings | None / eleven-bar swings | Do pivots add anything beyond the range ladder? |
| Traffic strength scale | 500 | 250 / 1000 | Does strength saturation affect probability/selection? |
```bash
.venv/bin/python scripts/run_gtl_tuning_matrix.py \
backtest_snapshots/prod.sqlite --workers 14
```
Arms execute sequentially so multiprocessing pools never compete. Each arm
produces full-period production metrics, train/test books split at 2024-07-01,
robust expectancy after removing the top 5% of setups, and retained/added/
removed cohorts against control. The consolidated JSON and Markdown table are
checkpointed after every arm; successful runs delete temporary per-arm reports
unless `--keep-arm-reports` is set.
The pre-registered screen requires all of the following versus control:
full/train/test Sharpe not worse, full-period drawdown not worse, at least 80%
of production trades retained, and positive qualified expectancy after removing
the top 5%. Passing identifies a candidate for forward paper validation, not an
automatic deployment. The post-2024 interval has already influenced this
research, so the split is a robustness check rather than a pristine holdout.
The post-2024 window has been opened and is now analysis data, not a valid final
promotion holdout. These arms can isolate mechanism, but neither may ship without
new future data or a separately pre-registered walk-forward protocol.