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Warrior_EA/Expert/README.md
AnimateDread 8157c42314 feat: Enhance README and documentation for Warrior_EA project 
- Updated README.md with project overview, key features, directory structure, getting started guide, and modernization roadmap.
- Added AI_NETWORK.md detailing the neural network and AI/ML infrastructure, including architecture, components, usage patterns, and next steps.
- Introduced DATABASE.md for the Database module, outlining key components, design highlights, usage patterns, and future enhancements.
- Created README.md files for Enumerations, Expert, Money, Signals, Structures, System, Trailing, Variables directories, detailing their purpose, key components, and integration notes.
- Documented the Signals subsystem, emphasizing modularity, extensibility, and AI/ML readiness.
- Added comprehensive descriptions for individual signal modules in Signals/ directory.
- Established clear integration notes and recommendations for future improvements across all modules.
2026-04-20 19:28:34 -04:00

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Expert/ Directory Documentation

ExpertCustom.mqh

  • Purpose: Defines the CExpertCustom class, a base class for custom expert advisors in the EA framework.
  • Inheritance: Inherits from CExpert (not shown here, likely a core MQL5/MetaQuotes class or defined elsewhere in the project).
  • Key Features:
    • Provides virtual event handlers for MQL5 events: OnTick, OnTimer, OnChartEvent, etc., allowing derived classes to implement custom trading logic.
    • Contains methods for initializing trading objects, handling series data, and managing trading events.
    • Designed for extensibility: users can derive their own expert logic by subclassing and overriding the provided virtual methods.
  • Modernization Note:
    • This class is a good candidate for further abstraction to support AI/ML-driven strategies. Consider introducing interfaces or abstract base classes for signal generation, money management, and risk control, allowing plug-and-play of traditional and AI/ML modules.
    • Ensure all event handlers are unit-testable and decoupled from hard-coded logic.

ExpertMoneyCustom.mqh

  • Purpose: Implements a custom money management class (CExpertMoneyCustom) for the EA, extending CExpertMoney.
  • Key Features:
    • CheckAndCorrectVolumeValue: Ensures trade volume is within broker constraints (min/max/step), adjusting and describing corrections.
    • CheckAndAdjustMoneyForTrade: Dynamically adjusts lot size to fit available margin, reducing lots if margin is insufficient, and handles errors gracefully.
  • Modernization Note:
    • This class is a good candidate for AI/ML-driven position sizing. Consider abstracting the logic to allow for ML-based risk and lot size optimization.
    • Ensure all adjustments are logged for transparency and auditability.

ExpertSignalAIBase.mqh

  • Purpose: Provides a base class (CExpertSignalAIBase) for AI/ML-driven signal generation, extending CExpertSignalCustom.
  • Key Features:
    • Integrates with the neural network/AI subsystem (Network.mqh).
    • Manages a wide range of indicator objects (Open, Close, High, Low, Volumes, AD, ADX, MACD, etc.) for feature extraction.
    • Supports dynamic configuration of neural network topology, training, and feature selection.
    • Implements methods for indicator initialization, data buffering, training, and topology persistence.
  • Modernization Note:
    • This is the main integration point for advanced ML/AI logic. Ensure all feature selection and training parameters are externally configurable.
    • Refactor to support plug-and-play feature pipelines and automated hyperparameter optimization.

ExpertSignalCustom.mqh

  • Purpose: Implements a custom signal logic class (CExpertSignalCustom) for the EA, extending CExpertSignal.
  • Key Features:
    • Adds database-driven signal tracking, pattern recognition, and trade record management.
    • Supports dynamic filter addition, ATR-based entry/exit logic, and advanced signal buffering.
    • Implements robust duplicate detection, trade status management, and event-driven signal processing.
  • Modernization Note:
    • This class is central to integrating traditional and AI/ML signals. Refactor to decouple hard-coded logic and support dynamic, testable signal pipelines.
    • Ensure all database operations are abstracted for testability and future migration to more advanced data stores.

(Other files in Expert/ will be documented as they are processed.)