Neural Networks for Algorithmic Trading with MQL5

✍ Scribed by Dmitriy Gizlyk

• Publisher: Metaquotes
• Year: 2024
• Tongue: English
• Leaves: 752
• Category: Library

✦ Synopsis

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.

Examples from the book "Neural networks for algorithmic trading with MQL5"
https://www.mql5.com/en/code/48097

✦ Table of Contents

Neural Networks for Algorithmic Trading with MQL5
Introduction
1. Basic principles of artificial intelligence construction
1.1 Neuron and principles of building neural networks
1.2 Activation functions
1.3 Weight initialization methods in neural networks
1.4 Neural network training
1.4.1 Loss functions
1.4.2 Error gradient backpropagation method
1.4.3 Methods for optimizing neural networks
1.5 Techniques for improving the convergence of neural networks
1.5.1 Regularization
1.5.2 Dropout
1.5.3 Normalization
1.6 Artificial intelligence in trading
2. MetaTrader 5 features for algorithmic trading
2.1 Program types and their construction features
2.2 Statistical analysis and fuzzy logic tools
2.3 OpenCL: Parallel computations in MQL5
2.4 Integration with Python
3. Building the first neural network model in MQL5
3.1 Problem statement
3.2 File arrangement structure
3.3 Choosing the input data
3.4 Creating the framework for the future MQL5 program
3.4.1 Defining constants
3.4.2 Mechanism for describing the structure of the future neural network
3.4.3 Neural network base class and organization of forward and backward pass processes
3.4.4 Dynamic storage array of neural layers
3.5 Description of a Python script structure
3.6 Fully connected neural layer
3.6.1 Architecture and implementation principles
3.6.2 Creating a neural layer using MQL5 tools
3.6.3 Activation function class
3.7 Organizing parallel computing using OpenCL
3.7.1 Creating an OpenCL program
3.7.2 Implementing functionality on the main program side
3.8 Implementing the perceptron model in Python
3.9 Creating training and testing samples
3.10 Gradient distribution verification
3.11 Comparative testing of implementations
4. Basic types of neural layers
4.1 Convolutional neural networks
4.1.1 Description of architecture and implementation principles
4.1.2 Construction using MQL5
4.1.3 Organizing parallel computing in convolutional networks using OpenCL
4.1.4 Implementing a convolutional model in Python
4.1.5 Practical testing of convolutional models
4.2 Recurrent neural networks
4.2.1 Description of architecture and implementation principles
4.2.2. Building an LSTM block in MQL5
4.2.2.1 Feed-forward method
4.2.2.2 Backpropagation methods
4.2.2.3 Saving and restoring the LSTM block
4.2.3 Organizing parallel computing in the LSTM block
4.2.4 Implementing recurrent models in Python
4.2.4.1 Building a test recurrent model in Python
4.2.5 Comparative testing of recurrent models
5. Attention mechanisms
5.1 Self-Attention
5.1.1 Description of architecture and implementation principles
5.1.2 Building Self-Attention with MQL5 tools
5.1.2.1 Self-Attention feed-forward method
5.1.2.2 Self-Attention backpropagation methods
5.1.2.3 File operations
5.1.3 Organizing parallel computing in the attention block
5.1.4 Testing the attention mechanism
5.2 Multi-Head attention
5.2.1 Description of the Multi-Head Self-Attention architecture
5.2.2 Building Multi-Head Self-Attention in MQL5
5.2.2.1 Multi-Head Self-Attention feed-forward method
5.2.2.2 Multi-Head Self-Attention backpropagation methods
5.2.2.3 File operations
5.2.3 Organizing parallel computing for Multi-Head Self-Attention
5.2.4 Building Multi-Head Self-Attention in Python
5.2.4.1 Creating a new neural layer class
5.2.4.2 Creating a script to test Multi-Head Self-Attention
5.2.5 Comparative testing of Attention models
5.3 GPT architecture
5.3.1 Description of the architecture
5.3.2 Building a GPT model in MQL5
5.3.2.1 GPT feed-forward method
5.3.2.2 GPT backpropagation methods
5.3.2.3 File operations
5.3.3 Organizing parallel computing in the GPT model
5.3.4 Comparative testing of implementations
6. Architectural solutions for improving model convergence
6.1 Batch normalization
6.1.1 Principles of batch normalization implementation
6.1.2 Building a batch normalization class in MQL5
6.1.2.1 Batch normalization feed-forward methods
6.1.2.2 Batch normalization backpropagation methods
6.1.2.3 File operations
6.1.3 Organizing multi-threaded computations in the batch normalization class
6.1.4 Implementing batch normalization in Python
6.1.4.1 Creating a script to test batch normalization
6.1.5 Comparative testing of models using batch normalization
6.2 Dropout
6.2.1 Building Dropout in MQL5
6.2.1.1 Feed-forward method
6.2.1.2 Backpropagation methods for Dropout
6.2.1.3 File operations
6.2.2 Organizing multi-threaded operations in Dropout
6.2.3 Implementing Dropout in Python
6.2.4 Comparative testing of models with Dropout
7. Testing trading capabilities of the model
7.1 Introduction to MetaTrader 5 Strategy Tester
7.2 Developing an Expert Advisor template using MQL5
7.3 Creating a model for testing
7.4 Determining Expert Advisor parameters
7.5 Testing the model on new data
Conclusion

✦ Subjects

metatrader,ea,expert advisor,mql5