Neural Networks for Algorithmic Trading with MQL5

In the era of digital technology and artificial intelligence, algorithmic trading is transforming financial markets, offering innovative strategies. The book "Neural Networks for Algorithmic Trading with MQL5" serves as a unique guide that combines advanced technological knowledge with practical guidance on creating trading algorithms. This book is tailored for traders, developers, and financial analysts who wish to understand the principles of neural networks and their application in algorithmic trading on the MetaTrader 5 platform.

The book has 7 chapters that cover everything you need to know to get started with neural networks and integrate them into your trading robots in MQL5. Beginning with basic principles of neural networks and advancing to more complex architectural solutions and attention mechanisms, this book provides all the necessary information for the successful implementation of machine learning in your algorithmic trading solutions.

You will discover how to use different types of neural networks, including convolutional and recurrent models, and how to integrate them into the MQL5 environment. Additionally, the book explores architectural solutions to improve model convergence, such as Batch Normalization and Dropout.

Furthermore, the author provides practical guidance on how to train neural networks and embed them into your trading strategies. You will learn how to create trading Expert Advisors to test the performance of trained models on new data, enabling you to evaluate their potential in real-world financial markets.

• Chapter 1 introduces you to the world of artificial intelligence, laying the foundation with essential neural network building blocks, such as activation functions and weight initialization methods.
• Chapter 2 explores MetaTrader 5 capabilities in detail, describing how to utilize the platform tools to create powerful algorithmic trading strategies.
• Chapter 3 guides you through the step-by-step development of your first neural network model in MQL5, covering everything from data preparation to model implementation and testing.
• Chapter 4 delves deep into understanding fundamental neural layer types, including convolutional and recurrent neural networks, their practical implementation, and comprehensive testing.
• Chapter 5 introduces attention mechanisms like Self-Attention and Multi-Head Self-Attention, presenting advanced data analysis methodologies.
• Chapter 6 explains architectural solutions to improve model convergence, such as Batch Normalization and Dropout.
• Chapter 7 concludes the book and offers methods for testing trading strategies using the developed neural network models under real trading conditions through MetaTrader 5.

With "Neural Networks for Algorithmic Trading with MQL5", you will gain comprehensive knowledge and practical skills for creating your own trading robots capable of analyzing markets and making decisions using advanced machine learning technologies. This book will be an invaluable resource for anyone who wants to use artificial intelligence in algorithmic trading and explore new horizons in financial analytics and trading.