forked from animatedread/Warrior_EA
| Filename | Latest commit message | Latest commit date |
|---|---|---|
d20058fc1b
Meta_ExportDataset input: with AIType=META the chart writes its complete training set once per attach - every resolved+labeled candidate as [barTime|family|pattern|side|won|NetInputWidth floats] using the SAME window builder, descriptor and label caches pass 2 trains on, so offline examples are byte-equivalent to the EA's own. Sidecar .meta.csv carries layout + the geometry/BE the labels were computed at. Files land in Common\Files\Warrior_EA\MetaExport\<sym>_<period>.f32. This is the pooling architecture decision: multi-symbol training INSIDE the per-chart God-class would be the riskiest surgery this codebase has seen; instead each chart exports, the pooled head trains offline (small dense+BN net, minutes on this box), is validated per-symbol under the same chronological splits and coverage x (p - BE) gate, and only a WINNER gets written back into a .nnw for the EA to load natively (format fully mapped). Also turns every future meta experiment from a 20-minute tester cycle into minutes of offline iteration. Cost-model note for the record (user challenge, verified): spread is 0.099 ATR = ~2% of the 4.74 ATR trade width - tiny per bar, but expressed in win-rate points it is 0.099/4.74 = 2.1pp, which is the measured base-vs-BE gap and the size of the entire observed skill lift. Zero-spread relabeling would put base == BE by construction. Multi-day holds additionally pay swap, which the label does NOT charge - the true bar is higher, not lower. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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| .. | ||
| ConfidenceBridge.mqh | ||
| IndicatorResources.mqh | ||
| IndicatorTuneRanges.mqh | ||
| Inputs.mqh | ||
| README.md | ||
| RiskBudget.mqh | ||
| Variables.mqh | ||
Variables Subsystem (Variables/)
Overview
The Variables/ directory contains global input parameters and runtime variables for the Warrior EA. These files centralize configuration, feature toggles, and runtime state, supporting both user customization and internal logic.
Key Components
Inputs.mqh
- Purpose: Defines all user-configurable input parameters for the EA.
- Contents:
- General EA settings (magic number, training mode, logging, etc.)
- Money management strategy selection and parameters
- Entry strategy and thresholds
- Trailing stop strategy selection
- Neural network/AI configuration (algorithm, layers, training years, etc.)
- Indicator and feature toggles (enable/disable specific indicators and features)
- Time/session filter settings
- Integration: Used for both manual and programmatic configuration of the EA. Enables dynamic feature selection and AI/ML pipeline configuration.
Variables.mqh
- Purpose: Stores global runtime variables and constants.
- Contents:
- EA name and database schema
- Backtesting and feature enablement flags
- AI/ML signal toggles (EnablePAI, EnableCONV, EnableLSTM)
- Integration: Used throughout the EA for runtime logic, feature gating, and database operations.
Integration Notes
- Centralized configuration and variable management improves maintainability and supports advanced, AI/ML-driven workflows.
- Feature toggles allow for rapid experimentation and safe deployment of new logic.
Documented April 2026. For further details, see the main project documentation.