AiDataGenByLeo/README.md

<p align="center">
  <img src="./Images/AiDataGenByLeo_banner.png" alt="AiDataGenByLeo Logo" width="1150" height="175"/>
</p>


<p align="center">
Build AI-powered trading systems with automatic feature generation, DSL-based configuration, and 85+ built-in features.
</p>

<p align="center">
  <img src="https://img.shields.io/badge/Language-MQL5-1B6CA8?style=flat-square"/>
  <img src="https://img.shields.io/badge/Platform-MetaTrader%205-0D1B2A?style=flat-square"/>
  <img src="https://img.shields.io/badge/Author-nique__372%20And%20Leo-C9D6DF?style=flat-square&logoColor=white"/>
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<a href="./LICENSE">
  <img src="https://img.shields.io/badge/License-Nique%26Leo%20NL--ND-red.svg"/>
</a>
</p>

 
---

## Main Features

### DSL-Based Feature Definition
Define features declaratively without writing repetitive code:
```dsl
@ New vector
{
  [Rsi_Valor][](Period=14|Applied=0)
  [Vidya_Tendencia][](CmoPeriod=9|EmaPeriod=12)
  [News_HasCpiUsaToday][]()
}
```

### Automatic Data Generation
```mql5
// Initialize feature generator
m_generator.Init("MyStrategy", features_dsl);

// Generate feature vector on each bar
m_generator.ObtenerDataEnVector(vector, curr_time);
```

### Python Training Pipeline
```python
# Load generated data
df = pd.read_csv('data.csv')

# Train classifier
model = train_classifier(df, selected_features)

# Export to ONNX
export_model(model, 'model.onnx')
```

### Real-Time AI Predictions
```mql5
// In your EA
if(m_predictor.CheckOpenOrder(trade_config)) {
   // AI approved - execute trade
   OpenPosition(type, entry, sl, tp);
}
```

### EasySb - ICT EA with AI
- **Production-Ready EA:** Complete trading system with AI integration, ONNX model included, and SetFile: https://forge.mql5.io/nique_372/EasySbAi

---

## Repository Structure
```
AiDataGenByLeo/
├── Extra/         # Library to measure correlations (with integrated plot)
├── GenericData/   # Classifier, DSL Parser, Feature Factory
├── Py/            # Python script for model training
└── Images/        # Repo banner
```

---

## License

**[Read Full License](./LICENSE)** 

By downloading or using this repository, you accept the license.

---

## Requirements
- For python see the `requirements.txt` 
- For project see the `dependencies.json` 

---

## Installation
- Clone the Git repository into shared projects via CMD
- 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
- Or fork the repository


---

## Quick Start

```bash
# 1. Generate training data
# Run EA in training mode
# Data will be saved to: TerminalPath/Files/Common/EasySbAi/data.csv

# 2. Train model
# Execute the Python script
# 3 files will be generated in TerminalPath/Files/Common/EasySbAi/
# Copy the ONNX model and scaler file to:
# AiDataGenByLeo/Examples/EAs/TTrades/EasySb/res/

# 3. Run the EA
# Execute Ea.mq5 with the training parameter set to false
```

---

## Disclaimer

**Trading involves substantial risk of loss.**

- This software is a technical tool, not financial advice
- Past performance does not guarantee future results
- You are solely responsible for your trading decisions
- Always test thoroughly before deploying with real capital
- Use appropriate risk management in all operations

The authors assume no liability for trading losses, system failures, or any damages arising from the use of this software.

---

## Contact and Support

- **Platform:** [MQL5 Community](https://www.mql5.com/es/users/nique_372)
- **Profile:** https://www.mql5.com/es/users/nique_372/news
- **Issues:** For bug reports or questions

---

**Copyright © 2026 Nique-Leo. All rights reserved.**