forked from animatedread/Warrior_EA
AI Expert Advisor
- MQL5 79%
- Python 8.9%
- HTML 5.5%
- C 3.3%
- C++ 3.1%
- Other 0.2%
| Filename | Latest commit message | Latest commit date |
|---|---|---|
788115970c
Two defects behind "arrows drawn while members are still mid-era". 1. THE DRAW. The filtered overlay armed on the FIRST member to finish pass 3 and leaned on a 60 s rate limit to "collapse the burst", assuming members finish seconds apart. They do not - on USDJPY one member was at sample 10496 of pass 2 while another was at 2304, minutes apart. A member with no era-end snapshot returns false from SnapshotVoteAt, and the sweep's `if(!hasData) continue;` skips it BEFORE `den += ModuleWeight()`, so the one finished model's tier weight became the entire vote and was drawn as a consensus arrow. An abstention is a member that looked at the bar and said nothing; a missing snapshot is a member that has not looked. The first must dilute the vote, the second must suppress the draw. The arm is now a readiness MASK - one bit per m_ensembleIndex, set at that member's pass-3 completion, cleared when a sweep arms - and a sweep waits for every enrolled member. Bounded at 10 minutes so a member that stops cannot freeze the chart, and the partial draw PRINTS which members were missing: the |
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| .claude | ||
| .clinerules | ||
| AI | ||
| Database | ||
| DirectML | ||
| docs | ||
| Enumerations | ||
| Expert | ||
| Market Descriptions | ||
| Marketing/Logo | ||
| Money | ||
| Panel | ||
| references | ||
| research | ||
| Scripts | ||
| Signals | ||
| Structures | ||
| System | ||
| Trailing | ||
| Variables | ||
| .gitignore | ||
| AI_NETWORK.md | ||
| cpu_directml.log | ||
| DATABASE.md | ||
| EXPERIMENTS.md | ||
| Meta_Labeling_Design.md | ||
| opencl.log | ||
| profiling.csv | ||
| README.md | ||
| REFACTOR_NOTES.md | ||
| SIGNALS.md | ||
| Warrior_EA.md | ||
| Warrior_EA.mq5 | ||
| Warrior_EA.mqproj | ||
| Warrior_EA_System_Overview.md | ||
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
- Configure your desired strategies and features in
Variables/Inputs.mqh. - Compile
Warrior_EA.mq5in MetaEditor. - Attach to a chart and enable Algo Trading.
- 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.