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Warrior_EA/mql5
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Filename Latest commit message Latest commit date
AnimateDread e9c562b39f Add Feature Scaling and Regime Math Classes; Implement Mind Trading Logic 
- Introduced `FeatureScale.mqh` with `FeatSquash` function for stateless feature scaling.
- Added `RegimeMath.mqh` class for regime arithmetic, including efficiency and variance calculations.
- Documented the Mind trading logic in `MIND.md`, detailing the trading process and modes.
- Created `VOLNORM_PLAN.md` and `VOLNORM_RESULTS.md` for tick-volume normalization testing.
- Implemented `read_book.py` for analyzing trade book data and correlations.
- Developed `volnorm.py` for testing tick-volume normalization with new and old methods.
2026-09-30 18:36:33 -04:00
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ExportBars.mq5 feat(dipz): WarriorDipZ - one chart per symbol, account-level guards 2026-09-23 13:24:57 -04:00
make_inis.py Add Feature Scaling and Regime Math Classes; Implement Mind Trading Logic 2026-09-30 18:36:33 -04:00
run_tests.ps1 feat(warrior): the vol-gated dip-buy book, ported into Warrior_EA 2026-09-23 21:14:53 -04:00
WarriorDipZ.mq5 feat(dipz): WarriorDipZ - one chart per symbol, account-level guards 2026-09-23 13:24:57 -04:00
WarriorGapFade.mq5 research(fx): forex and metals - thirteen registered families, nothing passed 2026-09-23 13:24:57 -04:00