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Filename Latest commit message Latest commit date
AnimateDread 9a0d063da4 fix(inputs): the Neural Networks group header was singular 
Typo, and more wrong than it was: the group now holds four independent NN toggles rather
than one architecture selector.

NOT COMPILED - user compiles in MetaEditor.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-19 14:23:37 -04:00
..
ConfidenceBridge.mqh feat(ensemble): per-NN inputs replace the preset selector - the meta head becomes the vote's gate 2026-08-19 13:01:02 -04:00
IndicatorResources.mqh refactor(indicator-resources): centralize indicator embedding in main EA file 2026-07-23 15:28:04 -04:00
IndicatorTuneRanges.mqh feat: extend ADWyckoffEventStream with new range-lifecycle parameters and update related features 2026-08-02 17:08:48 -04:00
Inputs.mqh fix(inputs): the Neural Networks group header was singular 2026-08-19 14:23:37 -04:00
README.md feat: Enhance README and documentation for Warrior_EA project 2026-04-20 19:28:34 -04:00
RiskBudget.mqh feat: expectancy stop - halt when the measured result says the strategy loses 2026-08-07 14:20:00 -04:00
TunedPeriods.mqh fix(altdata): robustness pass for arbitrary symbol/timeframe - validation + error handling 2026-08-16 17:59:10 -04:00
Variables.mqh feat(ensemble): per-NN inputs replace the preset selector - the meta head becomes the vote's gate 2026-08-19 13:01:02 -04:00

README.md

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.