Articles with MQL5 programming examples
Access a huge collection of articles with code examples showing how to create indicators and trading robots for the MetaTrader platform in the MQL5 language. Source codes are attached to the articles, so you can open them in MetaEditor and run them to see how the applications work.
These articles will be useful both for those who have just started exploring automated trading and for professional traders with programming experience. They feature not only examples, but also contain new ideas.
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Requesting in Connexus (Part 6): Creating an HTTP Request and Response
In this sixth article of the Connexus library series, we will focus on a complete HTTP request, covering each component that makes up a request. We will create a class that represents the request as a whole, which will help us bring together the previously created classes.
From Basic to Intermediate: Arrays and Strings (II)
In this article I will show that although we are still at a very basic stage of programming, we can already implement some interesting applications. In this case, we will create a fairly simple password generator. This way we will be able to apply some of the concepts that have been explained so far. In addition, we will look at how solutions can be developed for some specific problems.
Mastering Log Records (Part 6): Saving logs to database
This article explores the use of databases to store logs in a structured and scalable way. It covers fundamental concepts, essential operations, configuration and implementation of a database handler in MQL5. Finally, it validates the results and highlights the benefits of this approach for optimization and efficient monitoring.
Most notable Artificial Cooperative Search algorithm modifications (ACSm)
Here we will consider the evolution of the ACS algorithm: three modifications aimed at improving the convergence characteristics and the algorithm efficiency. Transformation of one of the leading optimization algorithms. From matrix modifications to revolutionary approaches regarding population formation.
From Basic to Intermediate: Template and Typename (III)
In this article, we will discuss the first part of the topic, which is not so easy for beginners to understand. In order not to get even more confused and to explain this topic correctly, we will divide the explanation into stages. We will devote this article to the first stage. However, although at the end of the article it may seem that we have reached the deadlock, in fact we will take a step towards another situation, which will be better understood in the next article.
Developing a Replay System (Part 33): Order System (II)
Today we will continue to develop the order system. As you will see, we will be massively reusing what has already been shown in other articles. Nevertheless, you will receive a small reward in this article. First, we will develop a system that can be used with a real trading server, both from a demo account or from a real one. We will make extensive use of the MetaTrader 5 platform, which will provide us with all the necessary support from the beginning.
Developing a Replay System (Part 69): Getting the Time Right (II)
Today we will look at why we need the iSpread feature. At the same time, we will understand how the system informs us about the remaining time of the bar when there is not a single tick available for it. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
Reimagining Classic Strategies (Part 14): High Probability Setups
High probability Setups are well known in our trading community, but regrettably they are not well-defined. In this article, we will aim to find an empirical and algorithmic way of defining exactly what is a high probability setup, identifying and exploiting them. By using Gradient Boosting Trees, we demonstrated how the reader can improve the performance of an arbitrary trading strategy and better communicate the exact job to be done to our computer in a more meaningful and explicit manner.
Anarchic Society Optimization (ASO) algorithm
In this article, we will get acquainted with the Anarchic Society Optimization (ASO) algorithm and discuss how an algorithm based on the irrational and adventurous behavior of participants in an anarchic society (an anomalous system of social interaction free from centralized power and various kinds of hierarchies) is able to explore the solution space and avoid the traps of local optimum. The article presents a unified ASO structure applicable to both continuous and discrete problems.
Integrating MQL5 with data processing packages (Part 3): Enhanced Data Visualization
In this article, we will perform Enhanced Data Visualization by going beyond basic charts by incorporating features like interactivity, layered data, and dynamic elements, enabling traders to explore trends, patterns, and correlations more effectively.
Integrating MQL5 with Data Processing Packages (Part 7): Building Multi-Agent Environments for Cross-Symbol Collaboration
The article presents a complete Python–MQL5 integration for multi‑agent trading: MT5 data ingestion, indicator computation, per‑agent decisions, and a weighted consensus that outputs a single action. Signals are stored to JSON, served by Flask, and consumed by an MQL5 Expert Advisor for execution with position sizing and ATR‑derived SL/TP. Flask routes provide safe lifecycle control and status monitoring.
Self Optimizing Expert Advisor With MQL5 And Python (Part VI): Taking Advantage of Deep Double Descent
Traditional machine learning teaches practitioners to be vigilant not to overfit their models. However, this ideology is being challenged by new insights published by diligent researches from Harvard, who have discovered that what appears to be overfitting may in some circumstances be the results of terminating your training procedures prematurely. We will demonstrate how we can use the ideas published in the research paper, to improve our use of AI in forecasting market returns.
Risk Management (Part 4): Completing the Key Class Methods
This is Part 4 of our series on risk management in MQL5, where we continue exploring advanced methods for protecting and optimizing trading strategies. Having laid important foundations in earlier articles, we will now focus on completing all remaining methods postponed in Part 3, including functions for checking whether specific profit or loss levels have been reached. In addition, we will introduce new key events that enable more accurate and flexible risk management.
An introduction to Receiver Operating Characteristic curves
ROC curves are graphical representations used to evaluate the performance of classifiers. Despite ROC graphs being relatively straightforward, there exist common misconceptions and pitfalls when using them in practice. This article aims to provide an introduction to ROC graphs as a tool for practitioners seeking to understand classifier performance evaluation.
Ordinal Encoding for Nominal Variables
In this article, we discuss and demonstrate how to convert nominal predictors into numerical formats that are suitable for machine learning algorithms, using both Python and MQL5.
The MQL5 Standard Library Explorer (Part 2): Connecting Library Components
Today, we take an important step toward helping every developer understand how to read class structures and quickly build Expert Advisors using the MQL5 Standard Library. The library is rich and expandable, yet it can feel like being handed a complex toolkit without a manual. Here we share and discuss an alternative integration routine—a concise, repeatable workflow that shows how to connect classes reliably in real projects.
Overcoming The Limitation of Machine Learning (Part 7): Automatic Strategy Selection
This article demonstrates how to automatically identify potentially profitable trading strategies using MetaTrader 5. White-box solutions, powered by unsupervised matrix factorization, are faster to configure, more interpretable, and provide clear guidance on which strategies to retain. Black-box solutions, while more time-consuming, are better suited for complex market conditions that white-box approaches may not capture. Join us as we discuss how our trading strategies can help us carefully identify profitable strategies under any circumstance.
Hybridization of population algorithms. Sequential and parallel structures
Here we will dive into the world of hybridization of optimization algorithms by looking at three key types: strategy mixing, sequential and parallel hybridization. We will conduct a series of experiments combining and testing relevant optimization algorithms.
How to implement AutoARIMA forecasting in MQL5
This article presents an MQL5 implementation of AutoARIMA that builds ARIMA models without manual tuning. It estimates d via a variance-based heuristic, fits ARMA(p,q) by gradient optimization with Adam, and selects p and q using AICc. The code returns a one-step-ahead price forecast by differencing, model estimation, and integration back to price level, ready to call on a Close series.
Overcoming The Limitation of Machine Learning (Part 2): Lack of Reproducibility
The article explores why trading results can differ significantly between brokers, even when using the same strategy and financial symbol, due to decentralized pricing and data discrepancies. The piece helps MQL5 developers understand why their products may receive mixed reviews on the MQL5 Marketplace, and urges developers to tailor their approaches to specific brokers to ensure transparent and reproducible outcomes. This could grow to become an important domain-bound best practice that will serve our community well if the practice were to be widely adopted.
Developing a Replay System (Part 68): Getting the Time Right (I)
Today we will continue working on getting the mouse pointer to tell us how much time is left on a bar during periods of low liquidity. Although at first glance it seems simple, in reality this task is much more difficult. This involves some obstacles that we will have to overcome. Therefore, it is important that you have a good understanding of the material in this first part of this subseries in order to understand the following parts.
Market Simulation (Part 10): Sockets (IV)
In this article, we'll look at what you need to do to start using Excel to manage MetaTrader 5, but in a very interesting way. To do this, we will use an Excel add-in to avoid using built-in VBA. If you don't know what add-in is meant, read this article and learn how to program in Python directly in Excel.
Brain Storm Optimization algorithm (Part I): Clustering
In this article, we will look at an innovative optimization method called BSO (Brain Storm Optimization) inspired by a natural phenomenon called "brainstorming". We will also discuss a new approach to solving multimodal optimization problems the BSO method applies. It allows finding multiple optimal solutions without the need to pre-determine the number of subpopulations. We will also consider the K-Means and K-Means++ clustering methods.
Population optimization algorithms: Whale Optimization Algorithm (WOA)
Whale Optimization Algorithm (WOA) is a metaheuristic algorithm inspired by the behavior and hunting strategies of humpback whales. The main idea of WOA is to mimic the so-called "bubble-net" feeding method, in which whales create bubbles around prey and then attack it in a spiral motion.
From Basic to Intermediate: Variables (II)
Today we will look at how to work with static variables. This question often confuses many programmers, both beginners and those with some experience, because there are several recommendations that must be followed when using this mechanism. The materials presented here are intended for didactic purposes only. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
Applying Localized Feature Selection in Python and MQL5
This article explores a feature selection algorithm introduced in the paper 'Local Feature Selection for Data Classification' by Narges Armanfard et al. The algorithm is implemented in Python to build binary classifier models that can be integrated with MetaTrader 5 applications for inference.
From Basic to Intermediate: Arrays and Strings (III)
This article considers two aspects. First, how the standard library can convert binary values to other representations such as octal, decimal, and hexadecimal. Second, we will talk about how we can determine the width of our password based on the secret phrase, using the knowledge we have already acquired.
From Basic to Intermediate: Overload
Perhaps this article will be the most confusing for novice programmers. As a matter of fact, here I will show that it is not always that all functions and procedures have unique names in the same code. Yes, we can easily use functions and procedures with the same name — and this is called overload.
From Basic to Intermediate: Operator Precedence
This is definitely the most difficult question to be explained purely theoretically. That is why you need to practice everything that we're going to discuss here. While this may seem simple at first, the topic of operators can only be understood in practice combined with constant education.
Market Simulation (Part 09): Sockets (III)
Today's article is a continuation of the previous one. We will look at the implementation of an Expert Advisor, focusing mainly on how the server code is executed. The code given in the previous article is not enough to make everything work as expected, so we need to dig a little deeper into it. Therefore, it is necessary to read both articles to better understand what will happen.
Artificial Tribe Algorithm (ATA)
The article provides a detailed discussion of the key components and innovations of the ATA optimization algorithm, which is an evolutionary method with a unique dual behavior system that adapts depending on the situation. ATA combines individual and social learning while using crossover for explorations and migration to find solutions when stuck in local optima.
Building Volatility models in MQL5 (Part I): The Initial Implementation
In this article, we present an MQL5 library for modeling volatility, designed to function similarly to Python's arch package. The library currently supports the specification of common conditional mean (HAR, AR, Constant Mean, Zero Mean) and conditional volatility (Constant Variance, ARCH, GARCH) models.
DoEasy. Controls (Part 33): Vertical ScrollBar
In this article, we will continue the development of graphical elements of the DoEasy library and add vertical scrolling of form object controls, as well as some useful functions and methods that will be required in the future.
Market Simulation (Part 08): Sockets (II)
How about creating something practical using sockets? In today's article, we'll start creating a mini-chat. Let's look together at how this is done - it will be very interesting. Please note that the code provided here is for educational purposes only. It should not be used for commercial purposes or in ready-made applications, as it does not provide data transfer security and the content transmitted over the socket can be accessed.
Population optimization algorithms: Evolution of Social Groups (ESG)
We will consider the principle of constructing multi-population algorithms. As an example of this type of algorithm, we will have a look at the new custom algorithm - Evolution of Social Groups (ESG). We will analyze the basic concepts, population interaction mechanisms and advantages of this algorithm, as well as examine its performance in optimization problems.
Bivariate Copulae in MQL5 (Part 1): Implementing Gaussian and Student's t-Copulae for Dependency Modeling
This is the first part of an article series presenting the implementation of bivariate copulae in MQL5. This article presents code implementing Gaussian and Student's t-copulae. It also delves into the fundamentals of statistical copulae and related topics. The code is based on the Arbitragelab Python package by Hudson and Thames.
From Basic to Intermediate: Arrays and Strings (I)
In today's article, we'll start exploring some special data types. To begin, we'll define what a string is and explain how to use some basic procedures. This will allow us to work with this type of data, which can be interesting, although sometimes a little confusing for beginners. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
Market Simulation (Part 07): Sockets (I)
Sockets. Do you know what they are for or how to use them in MetaTrader 5? If the answer is no, let's start by studying them. In today's article, we'll cover the basics. Since there are several ways to do the same thing, and we are always interested in the result, I want to show that there is indeed a simple way to transfer data from MetaTrader 5 to other programs, such as Excel. However, the main idea is not to transfer data from MetaTrader 5 to Excel, but the opposite, that is, to transfer data from Excel or any other program to MetaTrader 5.
Account Audit System in MQL5 (Part 1): Designing the User Interface
This article builds the user interface layer of an Account Audit System in MQL5 using CChartObject classes. We construct an on-chart dashboard that displays key metrics such as start/end balance, net profit, total trades, wins/losses, win rate, withdrawals, and a star-based performance rating. A menu button lets you show or hide the panel and restores one-click trading, delivering a clean, usable foundation for the broader audit pipeline.
Overcoming The Limitation of Machine Learning (Part 4): Overcoming Irreducible Error Using Multiple Forecast Horizons
Machine learning is often viewed through statistical or linear algebraic lenses, but this article emphasizes a geometric perspective of model predictions. It demonstrates that models do not truly approximate the target but rather map it onto a new coordinate system, creating an inherent misalignment that results in irreducible error. The article proposes that multi-step predictions, comparing the model’s forecasts across different horizons, offer a more effective approach than direct comparisons with the target. By applying this method to a trading model, the article demonstrates significant improvements in profitability and accuracy without changing the underlying model.