forked from nique_372/AiDataGenByLeo
145 lines
No EOL
3.7 KiB
Markdown
145 lines
No EOL
3.7 KiB
Markdown
<p align="center">
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<img src="./Images/AiDataGenByLeo_banner.png" alt="AiDataGenByLeo Logo" width="1150" height="175"/>
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</p>
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<p align="center">
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Build AI-powered trading systems with automatic feature generation, DSL-based configuration, and 85+ built-in features.
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</p>
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<p align="center">
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<img src="https://img.shields.io/badge/Language-MQL5-1B6CA8?style=flat-square"/>
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<img src="https://img.shields.io/badge/Platform-MetaTrader%205-0D1B2A?style=flat-square"/>
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<img src="https://img.shields.io/badge/Author-nique__372%20And%20Leo-C9D6DF?style=flat-square&logoColor=white"/>
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<img src="https://img.shields.io/badge/Articles-mql5.com-1B6CA8?style=flat-square"/>
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<a href="./LICENSE">
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<img src="https://img.shields.io/badge/License-Nique%26Leo%20NL--ND-red.svg"/>
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</a>
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</p>
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---
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## Main Features
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### DSL-Based Feature Definition
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Define features declaratively without writing repetitive code:
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```dsl
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@ New vector
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{
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[Rsi_Valor][](Period=14|Applied=0)
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[Vidya_Tendencia][](CmoPeriod=9|EmaPeriod=12)
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[News_HasCpiUsaToday][]()
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}
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```
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### Automatic Data Generation
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```mql5
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// Initialize feature generator
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m_generator.Init("MyStrategy", features_dsl);
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// Generate feature vector on each bar
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m_generator.ObtenerDataEnVector(vector, curr_time);
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```
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### Python Training Pipeline
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```python
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# Load generated data
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df = pd.read_csv('data.csv')
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# Train classifier
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model = train_classifier(df, selected_features)
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# Export to ONNX
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export_model(model, 'model.onnx')
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```
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### Real-Time AI Predictions
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```mql5
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// In your EA
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if(m_predictor.CheckOpenOrder(trade_config)) {
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// AI approved - execute trade
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OpenPosition(type, entry, sl, tp);
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}
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```
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### EasySb - ICT EA with AI
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- **Production-Ready EA:** Complete trading system with AI integration, ONNX model included, and SetFile: https://forge.mql5.io/nique_372/EasySbAi
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---
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## Repository Structure
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```
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AiDataGenByLeo/
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├── Extra/ # Library to measure correlations (with integrated plot)
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├── GenericData/ # Classifier, DSL Parser, Feature Factory
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├── Py/ # Python script for model training
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└── Images/ # Repo banner
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```
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---
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## License
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**[Read Full License](./LICENSE)**
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By downloading or using this repository, you accept the license.
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---
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## Requirements
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- For python see the `requirements.txt`
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- For project see the `dependencies.json`
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---
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## Installation
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- Clone the Git repository into shared projects via CMD
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- Contact me on MQL5 (user: nique_372) to be added as a collaborator with your MQL5 nickname (read-only access), which will make the repository automatically appear in your Shared Projects folder
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- Or fork the repository
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---
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## Quick Start
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```bash
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# 1. Generate training data
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# Run EA in training mode
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# Data will be saved to: TerminalPath/Files/Common/EasySbAi/data.csv
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# 2. Train model
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# Execute the Python script
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# 3 files will be generated in TerminalPath/Files/Common/EasySbAi/
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# Copy the ONNX model and scaler file to:
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# AiDataGenByLeo/Examples/EAs/TTrades/EasySb/res/
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# 3. Run the EA
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# Execute Ea.mq5 with the training parameter set to false
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```
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---
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## Disclaimer
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**Trading involves substantial risk of loss.**
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- This software is a technical tool, not financial advice
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- Past performance does not guarantee future results
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- You are solely responsible for your trading decisions
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- Always test thoroughly before deploying with real capital
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- Use appropriate risk management in all operations
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The authors assume no liability for trading losses, system failures, or any damages arising from the use of this software.
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---
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## Contact and Support
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- **Platform:** [MQL5 Community](https://www.mql5.com/es/users/nique_372)
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- **Profile:** https://www.mql5.com/es/users/nique_372/news
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- **Issues:** For bug reports or questions
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---
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**Copyright © 2026 Nique-Leo. All rights reserved.** |