# 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.

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