forked from animatedread/Warrior_EA
Introduce m_useSwingContext flag and FindConfirmedZigZagPivot method to compute normalized swing direction/magnitude/age features from the existing ADZigZag indicator. Only pivots that are at least m_swingConfirmationBars old are trusted, preventing lookahead bias. The SWING_SCAN_CAP_BARS macro limits backward scan depth. Default is off. |
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|---|---|---|
| .. | ||
| ConfidenceBridge.mqh | ||
| IndicatorTuneRanges.mqh | ||
| Inputs.mqh | ||
| README.md | ||
| 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.