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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
..
README.md feat: Enhance README and documentation for Warrior_EA project 2026-04-20 19:28:34 -04:00
WarriorEnums.mqh Add Feature Scaling and Regime Math Classes; Implement Mind Trading Logic 2026-09-30 18:36:33 -04:00

Enumerations Documentation

GlobalEnums.mqh

Defines the ENUM_SIGNAL enumeration for trading signal states:

  • Buy: Indicates a buy signal.
  • Sell: Indicates a sell signal.
  • Neutral: Indicates a neutral/no-action signal.
  • Undefine: Indicates an undefined or uninitialized state.

InputEnums.mqh

Defines a large set of enumerations for configuration and input parameters used throughout the EA. These include:

  • Custom menu and property enums for UI/configuration.
  • Period presets (e.g., 5, 10, 14, 20, 30, 50, 100, 200) for indicator calculations.
  • Training years presets for ML/AI training window selection.
  • ATR multipliers for volatility-based calculations.
  • Threshold presets for signal/trigger sensitivity.
  • Risk/reward ratio presets for money management.
  • Bars expiration settings for trade/session logic.
  • Entry multipliers for order sizing.
  • Trailing strategy types (none, ATR-based, SAR, MA, etc.).
  • Money management strategies (fixed risk, intelligent, fixed lot, etc.).
  • Day-of-week and session enums for time-based logic.
  • ITF (Intraday Time Filter) settings.
  • Hourly session presets (H1-H23) for time filtering.

Purpose: These enumerations provide a strongly-typed, maintainable way to configure and control the EA's behavior, supporting both traditional and AI/ML-driven logic. They enable dynamic feature selection, risk management, and strategy configuration, and are essential for modular, testable code.

Modernization Note:

  • Enumerations should be referenced in configuration UIs and parameter files to enable dynamic, user-driven feature pipelines.
  • Consider extending enums to support new AI/ML features and dynamic input selection as the EA evolves.