External libraries and frameworks that can be used to train neural networks and create experts for trading – Neural Networks – 18 January 2023

TensorFlow is an open-source machine learning library that can be used to train and deploy neural networks. It provides a wide range of tools and functions for building and training neural networks, as well as for deploying them in production environments. TensorFlow is widely used in industry and academia and is supported by a large community of developers and users.

Keras is a high-level neural networks API that can be run on top of TensorFlow. It is designed to make building and training neural networks easier by providing a simple and intuitive interface for defining and training neural networks. Keras is a popular choice for researchers and developers who are new to neural networks and want a simple and easy-to-use tool for building and training models.

PyTorch is another open-source machine learning library that is similar to TensorFlow, but is designed to be more flexible and user-friendly. It uses a dynamic computational graph, which allows for more flexibility and ease of use compared to TensorFlow’s static computational graph. PyTorch is gaining popularity in research and industry, especially in the computer vision and natural language processing field.

scikit-learn is a widely used machine learning library for Python that includes a variety of tools for training and evaluating models, including neural networks. It provides a consistent interface for a wide range of machine learning algorithms, including neural networks, making it a popular choice for data scientists and researchers.

LightGBM and XGBoost are gradient boosting frameworks that use tree-based learning and are designed to be efficient and scalable. They can be used for both classification and regression tasks. LightGBM and XGBoost are popular in Kaggle competitions and are known for their ability to handle large datasets and for their good performance. Both of them can also be used for training and building models for trading.

In summary, all of the above libraries and frameworks can be used to train neural networks and create experts for trading, but it is important to choose the one that best fits your use case and has the best performance for your specific problem.

How to train an Expert Advisor for Metatrader with a neural network, by example of the TensorFlow plugin. Step by step guide.

Training an expert advisor (EA) for MetaTrader using TensorFlow would involve several steps. Here’s a general overview of the process:

1. Collect and preprocess data: The first step is to collect historical data for the financial instrument you wish to trade. This data should include historical prices, as well as other relevant information such as indicators or economic data. Once you have collected the data, you will need to preprocess it, which may involve cleaning, normalizing, and transforming the data to make it suitable for training.
2. Define the model: Next, you will need to define the neural network model that you want to use for your EA. This can be done using the TensorFlow library, which provides a wide range of tools for building and training neural networks. You will need to choose the appropriate architecture and parameters for your model based on your specific use case and the data you have collected.
3. Train the model: After defining the model, you will need to train it using the preprocessed data. This will involve using TensorFlow’s training functions to iteratively update the model’s weights and biases based on the input data and the desired output. You may need to experiment with different training parameters and techniques to find the best results.
4. Test the model: Once the model is trained, you will need to test it using out-of-sample data to evaluate its performance. You can use TensorFlow’s evaluation functions to calculate metrics such as accuracy, precision, and recall.
5. Integrate the model into MetaTrader: After testing the model and obtaining satisfactory results, you will need to integrate it into the MetaTrader platform. This can be done by creating an EA script that uses the trained model to make trading decisions. You will also need to set up the EA to run on a schedule or to trigger when specific conditions are met.
6. Backtest and optimize the EA: Finally, you will need to backtest the EA using historical data to evaluate its performance and optimize its parameters. This will involve running the EA on the historical data and comparing the results to the actual market. You can use the results of the backtest to fine-tune the parameters of the EA and make it more effective.