8157c42314
- 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.
41 lines
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2.3 KiB
Markdown
41 lines
No EOL
2.3 KiB
Markdown
# Warrior_EA Project Overview
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## Description
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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.
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## Key Features
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- **AI/ML Integration:** LSTM, PAI, and CONV neural network signals, with configurable feature pipelines and training options.
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- **Traditional Signals:** Modular support for classic indicators (MA, MACD, RSI, etc.) and price action patterns.
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- **Money Management:** Fixed lot, fixed risk, and intelligent/adaptive strategies.
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- **Trailing Stops:** ATR-based, MA-based, Parabolic SAR, and more.
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- **Database/Statistics:** Tracks trades, signals, and performance for optimization and research.
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- **Configurable Inputs:** All major features and strategies are user-configurable via Inputs.mqh.
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- **Robust Initialization:** Retry logic and error handling for all critical subsystems.
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- **Production-Ready:** Designed for institutional and advanced retail use, with a focus on maintainability and extensibility.
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## Directory Structure
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- **AI/**: Neural network and ML logic
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- **Database/**: Database and statistics management
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- **Enumerations/**: Enum and type definitions
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- **Expert/**: Main EA orchestration and custom logic
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- **Money/**: Money management strategies
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- **Signals/**: Signal generation (AI and traditional)
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- **Structures/**: Data structures for signals and trades
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- **System/**: Utility and infrastructure modules
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- **Trailing/**: Trailing stop strategies
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- **Variables/**: Global input parameters and runtime variables
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## Getting Started
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1. Configure your desired strategies and features in `Variables/Inputs.mqh`.
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2. Compile `Warrior_EA.mq5` in MetaEditor.
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3. Attach to a chart and enable Algo Trading.
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4. Monitor logs and database/statistics for performance and optimization.
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## Modernization & AI/ML Roadmap
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- Migrate all hard-coded signals to a configurable, feature-driven pipeline.
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- Expand AI/ML subsystem with new models and training options.
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- Enhance database/statistics for deeper analytics and automated optimization.
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- Introduce unit and integration tests for all modules.
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---
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*Documented April 2026. For subsystem details, see each directory's README.md and AI_NETWORK.md.* |