ответвлён от animatedread/Warrior_EA
AI Expert Advisor
- MQL5 79.7%
- Python 7.4%
- HTML 5.7%
- C 3.4%
- C++ 3.2%
- Разное 0.6%
| Имя файла | Текст последнего коммита | Дата последнего коммита |
|---|---|---|
d20058fc1b
Meta_ExportDataset input: with AIType=META the chart writes its complete training set once per attach - every resolved+labeled candidate as [barTime|family|pattern|side|won|NetInputWidth floats] using the SAME window builder, descriptor and label caches pass 2 trains on, so offline examples are byte-equivalent to the EA's own. Sidecar .meta.csv carries layout + the geometry/BE the labels were computed at. Files land in Common\Files\Warrior_EA\MetaExport\<sym>_<period>.f32. This is the pooling architecture decision: multi-symbol training INSIDE the per-chart God-class would be the riskiest surgery this codebase has seen; instead each chart exports, the pooled head trains offline (small dense+BN net, minutes on this box), is validated per-symbol under the same chronological splits and coverage x (p - BE) gate, and only a WINNER gets written back into a .nnw for the EA to load natively (format fully mapped). Also turns every future meta experiment from a 20-minute tester cycle into minutes of offline iteration. Cost-model note for the record (user challenge, verified): spread is 0.099 ATR = ~2% of the 4.74 ATR trade width - tiny per bar, but expressed in win-rate points it is 0.099/4.74 = 2.1pp, which is the measured base-vs-BE gap and the size of the entire observed skill lift. Zero-spread relabeling would put base == BE by construction. Multi-day holds additionally pay swap, which the label does NOT charge - the true bar is higher, not lower. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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| .claude | ||
| .clinerules | ||
| AI | ||
| Database | ||
| DirectML | ||
| 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.