forked from animatedread/Warrior_EA
AI Expert Advisor
- MQL5 79.4%
- HTML 10.9%
- C++ 6.9%
- C 2.2%
- Batchfile 0.2%
- Other 0.4%
| Filename | Latest commit message | Latest commit date |
|---|---|---|
213b3aac15
cooldown-recon put the chart-wide cooldown at the end of the overlay sweep. It executed ZERO times. This store's own header already said why: the sweep 're-arms only when an era ends. A DEPLOYED ensemble runs no further eras'. Five of six charts were deployed, so there were zero 'Filtered view: swept' lines in the entire session while the saved files still held 148 same-side pairs under 30 bars on XTIUSD. Moved to the completion of the progressive vote-arrow restore, which runs on every chart including deployed ones. The restore thinning alone was never going to be enough either: MT5 persists chart objects in profiles\Charts\*\chart*.chr, so arrows drawn under an older window are ALREADY on the chart when the process starts, and a freshly-thinned restore just adds to them. Two correctly-thinned sets still union into clusters. The chart is the only authority. Same construction as before: OBJ_TREND only (the line is the canonical half of a mark, matching Snapshot()), sorted by time first because object order is not time order, and the gap>0 guard so a mis-ordered set fails visibly by keeping rather than silently by deleting. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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| .claude | ||
| .clinerules | ||
| AI | ||
| Database | ||
| DirectML | ||
| docs | ||
| Enumerations | ||
| Expert | ||
| Market Descriptions | ||
| Marketing/Logo | ||
| Money | ||
| Panel | ||
| Scripts | ||
| Signals | ||
| Structures | ||
| System | ||
| Tests | ||
| Trailing | ||
| Variables | ||
| .gitignore | ||
| AI_NETWORK.md | ||
| cpu_directml.log | ||
| DATABASE.md | ||
| EXPERIMENTS.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.