MetaQuotes adds support for ONNX models in MQL5
Fintech company MetaQuotes has added support for ONNX models in MQL5. By using the new capabilities, developers can train models in their preferred environment and then run them in trading with minimal effort.
ONNX (Open Neural Network Exchange) is an open-source format for exchanging machine learning models between various frameworks. Developed by Microsoft, Facebook and Amazon Web Services (AWS), it aims at facilitating the development and deployment of ML models.
Key benefits of ONNX:
- Interoperability: The standard allows the exchange of models between different frameworks such as TensorFlow, PyTorch, Caffe2, MXNet and others. This facilitates the model development and deployment processes.
- Optimization: The technology provides optimized operations and computational graphs to improve model performance and reduce computational costs.
- Standardization: ONNX offers a single format to serialize and store ML models. This simplifies model exchange between developers and organizations.
- Ecosystem: ONNX is supported by a plethora of libraries, tools and hardware, which helps spread and accelerate ML innovations.
- Open Standard: The project is an open format with an active community and we believe that it will continue growing. We encourage you to contribute to the project development.
To use ONNX, developers can export their models from various frameworks such as TensorFlow or PyTorch to the ONNX format. Further, the models can be included in MQL5 applications and run in the MetaTrader 5 trading terminal.
One of the most popular tools for converting models to the ONNX format is Microsoft’s ONNXMLTools.
To run a trained model, you should use ONNX Runtime. ONNX Runtime is a high-performance cross-platform engine designed to run ML models exported in the ONNX format. With ONNX, developers can build models in one framework and then easily deploy them in other environments. This provides flexibility while simplifying the development process.
ONNX is a powerful tool for ML developers and researchers. Use these capabilities to efficiently develop and implement models in trading using MQL5.