Warrior_EA/Expert

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.

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