Warrior_EA Project Overview

Description

Warrior_EA is a modular, AI/ML-ready MetaTrader 5 Expert Advisor designed for robust, production-grade trading. It integrates traditional and AI-driven signals, advanced money management, trailing stops, and a database/statistics subsystem for adaptive optimization.

Key Features

• AI/ML Integration: LSTM, PAI, and CONV neural network signals, with configurable feature pipelines and training options.
• Traditional Signals: Modular support for classic indicators (MA, MACD, RSI, etc.) and price action patterns.
• Money Management: Fixed lot, fixed risk, and intelligent/adaptive strategies.
• Trailing Stops: ATR-based, MA-based, Parabolic SAR, and more.
• Database/Statistics: Tracks trades, signals, and performance for optimization and research.
• Configurable Inputs: All major features and strategies are user-configurable via Inputs.mqh.
• Robust Initialization: Retry logic and error handling for all critical subsystems.
• Production-Ready: Designed for institutional and advanced retail use, with a focus on maintainability and extensibility.

Directory Structure

• AI/: Neural network and ML logic
• Database/: Database and statistics management
• Enumerations/: Enum and type definitions
• Expert/: Main EA orchestration and custom logic
• Money/: Money management strategies
• Signals/: Signal generation (AI and traditional)
• Structures/: Data structures for signals and trades
• System/: Utility and infrastructure modules
• Trailing/: Trailing stop strategies
• Variables/: Global input parameters and runtime variables

Getting Started

1. Configure your desired strategies and features in Variables/Inputs.mqh.
2. Compile Warrior_EA.mq5 in MetaEditor.
3. Attach to a chart and enable Algo Trading.
4. Monitor logs and database/statistics for performance and optimization.

Modernization & AI/ML Roadmap

• Migrate all hard-coded signals to a configurable, feature-driven pipeline.
• Expand AI/ML subsystem with new models and training options.
• Enhance database/statistics for deeper analytics and automated optimization.
• Introduce unit and integration tests for all modules.

---

Documented April 2026. For subsystem details, see each directory's README.md and AI_NETWORK.md.