Articles on MetaTrader 5 integration using MQL5

The article examines the impact of changing the shape of probability distributions on the performance of optimization algorithms. We will conduct experiments using the Smart Cephalopod (SC) test algorithm to evaluate the efficiency of various probability distributions in the context of optimization problems.

The article proposes the method of creating bots using machine learning.

This article is the sixth part of a series describing our development steps of a native MQL5 client for the MQTT 5.0 protocol. In this part we comment on the main changes in our first refactoring, how we arrived at a viable blueprint for our packet-building classes, how we are building PUBLISH and PUBACK packets, and the semantics behind the PUBACK Reason Codes.

In this third part, we revisit the Simple Hedge and Simple Grid Expert Advisors (EAs) developed earlier. Our focus shifts to refining the Simple Hedge EA through mathematical analysis and a brute force approach, aiming for optimal strategy usage. This article delves deep into the mathematical optimization of the strategy, setting the stage for future exploration of coding-based optimization in later installments.

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 adopted in modern models.

DRAKON is a visual programming language designed to simplify interaction between specialists from different fields (biologists, physicists, engineers...) with programmers in Russian space projects (for example, in the Buran reusable spacecraft project). In this article, I will talk about how DRAKON makes the creation of algorithms accessible and intuitive, even if you have never encountered code, and also how it is easier for customers to explain their thoughts when ordering trading robots, and for programmers to make fewer mistakes in complex functions.

Embark on a compelling exploration where financial analysis meets algorithmic trading as we unravel the art of seamlessly uniting R and MetaTrader 5. This article is your guide to bridging the realms of analytical finesse in R with the formidable trading capabilities of MetaTrader 5.

In this article, we will analyse the impact of dividend announcements on stock market returns and see how investors can earn more returns than those offered by the market when they expect a company to announce dividends. In doing so, we will also check the validity of the Efficient Market Hypothesis in the context of the Indian Stock Market.

XLV is SPDR healthcare ETF and in an age where it is common to be bombarded by a wide array of traditional news items plus social media feeds, it can be pressing to select a data set for use with a model. We try to tackle this problem for this ETF by sizing up some of its critical data sets in MQL5.

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 and double precision. Furthermore, we will examine the ONNX representation of regression models, aiming to provide a better understanding of their internal structure and operational principles.

Dive into the world of ONNX, the powerful open-standard format for exchanging machine learning models. Discover how leveraging ONNX can revolutionize algorithmic trading in MQL5, allowing traders to seamlessly integrate cutting-edge AI models and elevate their strategies to new heights. Uncover the secrets to cross-platform compatibility and learn how to unlock the full potential of ONNX in your MQL5 trading endeavors. Elevate your trading game with this comprehensive guide to Mastering ONNX

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 will compare the accuracy of the original models with their ONNX versions on the full Iris dataset.

This article is the third part of a series describing our development steps of a native MQL5 client for the MQTT protocol. In this part, we describe in detail how we are using Test-Driven Development to implement the Operational Behavior part of the CONNECT/CONNACK packet exchange. At the end of this step, our client MUST be able to behave appropriately when dealing with any of the possible server outcomes from a connection attempt.

This time, let's try a different approach to achieve the 1 minute goal. However, this task is not as simple as one might think.

How about creating a system that would allow us to study the market when it is closed or even to simulate market situations? Here we are going to start a new series of articles in which we will deal with this topic.

Unlock the potential of dynamic data representation in your trading strategies and utilities with our in-depth guide to creating movable GUIs in MQL5. Delve into the fundamental principles of object-oriented programming and discover how to design and implement single or multiple movable GUIs on the same chart with ease and efficiency.

This article continues the series on category theory implementation in MQL5. Here we look at monoid-groups as a means normalising monoid sets making them more comparable across a wider span of monoid sets and data types..

Unleash the power of dynamic data representation in your trading strategies or utilities with our comprehensive guide on creating movable GUI in MQL5. Dive into the core concept of chart events and learn how to design and implement simple and multiple movable GUI on the same chart. This article also explores the process of adding elements to your GUI, enhancing their functionality and aesthetic appeal.

Category Theory is a diverse and expanding branch of Mathematics which is only recently getting some coverage in the MQL5 community. These series of articles look to explore and examine some of its concepts & axioms with the overall goal of establishing an open library that provides insight while also hopefully furthering the use of this remarkable field in Traders' strategy development.

This article describes four methods for connecting MQL5 code to a Postgres database and provides a step-by-step tutorial for setting up a development environment for one of them, a REST API, using the Windows Subsystem For Linux (WSL). A demo app for the API is provided along with the corresponding MQL5 code to insert data and query the respective tables, as well as a demo Expert Advisor to consume this data.

In this article, I will consider the Monkey Algorithm (MA) optimization algorithm. The ability of these animals to overcome difficult obstacles and get to the most inaccessible tree tops formed the basis of the idea of the MA algorithm.

GSA is a population optimization algorithm inspired by inanimate nature. Thanks to Newton's law of gravity implemented in the algorithm, the high reliability of modeling the interaction of physical bodies allows us to observe the enchanting dance of planetary systems and galactic clusters. In this article, I will consider one of the most interesting and original optimization algorithms. The simulator of the space objects movement is provided as well.

Category Theory is a diverse and expanding branch of Mathematics which is only recently getting some coverage in the MQL5 community. These series of articles look to explore and examine some of its concepts & axioms with the overall goal of establishing an open library that provides insight while also hopefully furthering the use of this remarkable field in Traders' strategy development.

The article discusses the possibilities of handling databases based on the SQLite engine. The CDatabase class has been formed for convenience and efficient use of OOP principles. It is subsequently involved in the creation and management of the database of macroeconomic events. The article provides the examples of using multiple methods of the CDatabase class.