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, via a declarative YAML config schema, and 85+ built-in features.
</p>

<p align="center">
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<a href="./LICENSE">
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</a>
</p>

 
---

## Main Features

### YAML Schema (FGBLC) configuration

AiDataGenByLeo uses a declarative YAML schema (**FGBLC** — *Feature Generator By Leo Config*) to define, at a single glance, which features are calculated,
in what order, and how they map onto the output vector or matrix — all without touching a single line of code.

#### YAML Example

```yaml
name: Generador de features

config:
  cols: 2

output:
  type: AIDATALEO_GEN_VECTOR
  contexts:
    - mode: custom 
      idx: [1, 2]  
      data:
        - class: News_GetValue
          prefix: ~
          params: { event_code: "core-cpi-mm",  country_code: US}
        - class: News_HasEventToday
          prefix: ~
          params: { event_code: "core-cpi-mm",  country_code: US}  
```
> A Schema JSON file has been provided so you can validate your own YAML, or you can insert it into your code editor:
> [Json Schema](./GenericData/ShemaJson/Schema.json)

### Automatic Data Generation
```mql5
// Parse yaml from TSN Ecositem YamlParserByLeo
TSN::CYamlParser yml;
yml.Assing(my_yaml_src);
yml.CorrectPadding();
yml.Parse();

// Initialize feature generator
m_generator.Init(yml.GetRoot());

// 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/
├── 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` 


---
## Docs
- YAML Schema Estructure: [Parser README](https://forge.mql5.io/nique_372/AiDataGenByLeo/src/branch/main/GenericData/ShemaJson/README.md).
- Features docs: [AiDataGenByLeoFeaturesDocs](https://forge.mql5.io/nique_372/AiDataGenByLeoFeaturesDocs).

---

## Installation

```bash
cd "C:\Users\YOUR_USER\AppData\Roaming\MetaQuotes\Terminal\YOUR_ID\MQL5\Shared Projects"
tsndep install "https://forge.mql5.io/nique_372/AiDataGenByLeo.git"
```

- Requires the `tsndep` package — available on [PyPI](https://pypi.org/project/tsndep). It automatically downloads and installs all declared dependencies.
- If any dependency is private or paid, the install will fail for that package — check [dependencies.json](./dependencies.json) and contact me for access.

---

## 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.**