FaisalPratama/Warrior_EA
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Filename Latest commit message Latest commit date
AnimateDread 10253b581b feat(inputs): private-build defaults = the drop-on-chart meta-pooling workflow 
User request: attaching a chart must need zero Inputs-tab edits. Private
(non-Market) build now defaults to AIType=META, all four classic families
ON (they are the sweep's candidate sources), Meta_ExportDataset=true.
Market-build defaults unchanged (AI_NONE, MA/RSI only, no export);
UseDatabaseRanking=true applies to both per the earlier request.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-13 16:31:15 -04:00
..
ConfidenceBridge.mqh fix: live trades now use the geometry the gate certifies; perf: BN kernels 2026-08-09 17:51:40 -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 feat(inputs): private-build defaults = the drop-on-chart meta-pooling workflow 2026-08-13 16:31:15 -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
Variables.mqh feat: S2 meta-labeling head - binary trade-quality model over the classic-candidate corpus 2026-08-13 06:52:31 -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.