Codes
Examples from the book "Neural networks for algorithmic trading with MQL5" for MetaTrader 5
The book "Neural networks in algorithmic trading with MQL5" is a comprehensive guide, covering both the theoretical foundations of artificial intelligence and neural networks and practical aspects of their application in financial trading using the MQL5 programming language
MQL5 Programming for Traders – Source Codes from the Book. Part 7 for MetaTrader 5
The final seventh part of the book discusses the advanced capabilities of the MQL5 API, which will be useful when developing programs for MetaTrader 5. These include custom financial symbols, built-in economic calendar events, and general-purpose technologies such as networking, databases, and
MQL5 Programming for Traders – Source Codes from the Book. Part 6 for MetaTrader 5
In Part 6 of the "MQL5 Programming for Traders", we will study a key component of the MQL5 language – trading automation. We will start with a description of the fundamental entities, such as financial instrument specifications and trading account settings. These are prerequisites for creating
MQL5 Programming for Traders – Source Codes from the Book. Part 5 for MetaTrader 5
In Part 5 of the book, we'll delve deeper into the APIs associated with algorithmic trading, including financial data analysis and processing, chart visualization, automation, and user interactions
MQL5 Programming for Traders – Source Codes from the Book. Part 4 for MetaTrader 5
In the fourth part of the book, we will focus on mastering built-in functions (MQL5 API) and will gradually delve into specialized subsystems. Any MQL5 program can utilize a plethora of technologies and functionalities. Therefore, it makes sense to begin with the most simple and useful functions
MQL5 Programming for Traders – Source Codes from the Book. Part 3 for MetaTrader 5
Part 3 "Object Oriented Programming in MQL5" offers an immersion into the world of object-oriented programming (OOP) in the MQL5 language. Software development often involves the complexity related to the management of multiple entities, requiring advanced technology to improve programming
MQL5 Programming for Traders – Source Codes from the Book. Part 2 for MetaTrader 5
Part 2 "MQL5 programming fundamentals" is an introduction to the key concepts of this programming language. This part of the book is devoted to data types, identifiers, variables, expressions, and operators. You will learn how to combine different instructions to form the program logic
MQL5 Programming for Traders – Source Codes from the Book. Part 1 for MetaTrader 5
The first chapter of the book introduces the MQL5 language and development environment. One of the new features introduced in the MQL5 language compared to MQL4 (MetaTrader 4 language) is support for object-oriented programming (OOP), which makes it similar to C++
Zigzag R for MetaTrader 4
An optimized version of the Zigzag indicator, which was included in the MT4 delivery of 2005 (and in MT3.83)
RegularExpressions in MQL4 for working with regular expressions for MetaTrader 4
Regular expressions provide a formal language for quick and flexible processing of texts. Each regular expression is a pattern (mask), for which the regular expression engine tries to find matches in the source text. A pattern consists of one or more character literals, operators, or constructs
Articles
Working with ONNX models in float16 and float8 formats for MetaTrader 5
Data formats used to represent machine learning models play a crucial role in their effectiveness. In recent years, several new types of data have emerged, specifically designed for working with deep learning models. In this article, we will focus on two new data formats that have become widely
Regression models of the Scikit-learn Library and their export to ONNX for MetaTrader 5
In this article, we will explore the application of regression models from the Scikit-learn package, attempt to convert them into ONNX format, and use the resultant models within MQL5 programs. Additionally, we will compare the accuracy of the original models with their ONNX versions for both float
Launching MetaTrader VPS: A step-by-step guide for first-time users for MetaTrader 5
Everyone who uses trading robots or signal subscriptions sooner or later recognizes the need to rent a reliable 24/7 hosting server for their trading platform. We recommend using MetaTrader VPS for several reasons. You can conveniently pay and manage the subscription through your MQL5.community
Classification models in the Scikit-Learn library and their export to ONNX for MetaTrader 5
In this article, we will explore the application of all classification models available in the Scikit-Learn library to solve the classification task of Fisher's Iris dataset. We will attempt to convert these models into ONNX format and utilize the resulting models in MQL5 programs. Additionally, we
ALGLIB numerical analysis library in MQL5 for MetaTrader 5
The article takes a quick look at the ALGLIB 3.19 numerical analysis library, its applications and new algorithms that can improve the efficiency of financial data analysis
Evaluating ONNX models using regression metrics for MetaTrader 5
Regression is a task of predicting a real value from an unlabeled example. The so-called regression metrics are used to assess the accuracy of regression model predictions
Matrices and vectors in MQL5: Activation functions for MetaTrader 5
Here we will describe only one of the aspects of machine learning - activation functions. In artificial neural networks, a neuron activation function calculates an output signal value based on the values of an input signal or a set of input signals. We will delve into the inner workings of the
Wrapping ONNX models in classes for MetaTrader 5
Object-oriented programming enables creation of a more compact code that is easy to read and modify. Here we will have a look at the example for three ONNX models
An example of how to ensemble ONNX models in MQL5 for MetaTrader 5
ONNX (Open Neural Network eXchange) is an open format built to represent neural networks. In this article, we will show how to use two ONNX models in one Expert Advisor simultaneously
How to use ONNX models in MQL5 for MetaTrader 5
ONNX (Open Neural Network Exchange) is an open format built to represent machine learning models. In this article, we will consider how to create a CNN-LSTM model to forecast financial timeseries. We will also show how to use the created ONNX model in an MQL5 Expert Advisor
Forum
Discussing the article: "Building A Candlestick Trend Constraint Model(Part 2): Merging Native Indicators"
Check out the new article: Building A Candlestick Trend Constraint Model(Part 2): Merging Native Indicators . This article focuses on taking advantage of in-built meta trader 5 indicators to screen out off-trend signals. Advancing from the previous article we will explore how to do it using MQL5
Discussing the article: "Introduction to MQL5 (Part 7): Beginner's Guide to Building Expert Advisors and Utilizing AI-Generated Code in MQL5"
Check out the new article: Introduction to MQL5 (Part 7): Beginner's Guide to Building Expert Advisors and Utilizing AI-Generated Code in MQL5 . Discover the ultimate beginner's guide to building Expert Advisors (EAs) with MQL5 in our comprehensive article. Learn step-by-step how to construct EAs
Discussing the article: "MQL5 Wizard Techniques you should know (Part 18): Neural Architecture Search with Eigen Vectors"
Check out the new article: MQL5 Wizard Techniques you should know (Part 18): Neural Architecture Search with Eigen Vectors . Neural Architecture Search, an automated approach at determining the ideal neural network settings can be a plus when facing many options and large test data sets. We examine
Introducing the book "Neural Networks for algorithmic trading in MQL5"
We are happy to announce the release of a new book entitled Neural Networks for algorithmic trading in MQL5 . From this book, you will learn how to use artificial intelligence in trading robots for the MetaTrader 5 platform. The author, Dmitry Gizlyk , is a hands-on neural network professional; he
Discussing the article: "Data Science and ML (Part 22): Leveraging Autoencoders Neural Networks for Smarter Trades by Moving from Noise to Signal"
Check out the new article: Data Science and ML (Part 22): Leveraging Autoencoders Neural Networks for Smarter Trades by Moving from Noise to Signal . In the fast-paced world of financial markets, separating meaningful signals from the noise is crucial for successful trading. By employing
Discussing the article: "Developing a Replay System (Part 38): Paving the Path (II)"
Check out the new article: Developing a Replay System (Part 38): Paving the Path (II) . Many people who consider themselves MQL5 programmers do not have the basic knowledge that I will outline in this article. Many people consider MQL5 to be a limited tool, but the actual reason is that they do not
Discussing the article: "Custom Indicators (Part 1): A Step-by-Step Introductory Guide to Developing Simple Custom Indicators in MQL5"
Check out the new article: Custom Indicators (Part 1): A Step-by-Step Introductory Guide to Developing Simple Custom Indicators in MQL5 . Learn how to create custom indicators using MQL5. This introductory article will guide you through the fundamentals of building simple custom indicators and
Discussing the article: "Developing an MQL5 RL agent with RestAPI integration (Part 2): MQL5 functions for HTTP interaction with the tic-tac-toe game REST API"
Check out the new article: Developing an MQL5 RL agent with RestAPI integration (Part 2): MQL5 functions for HTTP interaction with the tic-tac-toe game REST API . In this article we will talk about how MQL5 can interact with Python and FastAPI, using HTTP calls in MQL5 to interact with the
Discussing the article: "MQL5 Wizard Techniques you should know (Part 17): Multicurrency Trading"
Check out the new article: MQL5 Wizard Techniques you should know (Part 17): Multicurrency Trading . Trading across multiple currencies is not available by default when an expert advisor is assembled via the wizard. We examine 2 possible hacks traders can make when looking to test their ideas off
Discussing the article: "The Group Method of Data Handling: Implementing the Combinatorial Algorithm in MQL5"
Check out the new article: The Group Method of Data Handling: Implementing the Combinatorial Algorithm in MQL5 . In this article we continue our exploration of the Group Method of Data Handling family of algorithms, with the implementation of the Combinatorial Algorithm along with its refined