MQL4 and MQL5 Programming Articles

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Study the MQL5 language for programming trading strategies in numerous published articles mostly written by you - the community members. The articles are grouped into categories to help you quicker find answers to any questions related to programming: Integration, Tester, Trading Strategies, etc.

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MQL5 Wizard Techniques you should know (Part 67): Using Patterns of TRIX and the Williams Percent Range

MQL5 Wizard Techniques you should know (Part 67): Using Patterns of TRIX and the Williams Percent Range

The Triple Exponential Moving Average Oscillator (TRIX) and the Williams Percentage Range Oscillator are another pair of indicators that could be used in conjunction within an MQL5 Expert Advisor. This indicator pair, like those we’ve covered recently, is also complementary given that TRIX defines the trend while Williams Percent Range affirms support and Resistance levels. As always, we use the MQL5 wizard to prototype any potential these two may have.
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Neural Networks in Trading: Superpoint Transformer (SPFormer)

Neural Networks in Trading: Superpoint Transformer (SPFormer)

In this article, we introduce a method for segmenting 3D objects based on Superpoint Transformer (SPFormer), which eliminates the need for intermediate data aggregation. This speeds up the segmentation process and improves the performance of the model.
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Larry Williams Market Secrets (Part 1): Building a Swing Structure Indicator in MQL5

Larry Williams Market Secrets (Part 1): Building a Swing Structure Indicator in MQL5

A practical guide to building a Larry Williams–style market structure indicator in MQL5, covering buffer setup, swing-point detection, plot configuration, and how traders can apply the indicator in technical market analysis.
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Generative Adversarial Networks (GANs) for Synthetic Data in Financial Modeling (Part 1): Introduction to GANs and Synthetic Data in Financial Modeling

Generative Adversarial Networks (GANs) for Synthetic Data in Financial Modeling (Part 1): Introduction to GANs and Synthetic Data in Financial Modeling

This article introduces traders to Generative Adversarial Networks (GANs) for generating Synthetic Financial data, addressing data limitations in model training. It covers GAN basics, python and MQL5 code implementations, and practical applications in finance, empowering traders to enhance model accuracy and robustness through synthetic data.
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From Novice to Expert: Animated News Headline Using MQL5 (VIII) — Quick Trade Buttons for News Trading

From Novice to Expert: Animated News Headline Using MQL5 (VIII) — Quick Trade Buttons for News Trading

While algorithmic trading systems manage automated operations, many news traders and scalpers prefer active control during high-impact news events and fast-paced market conditions, requiring rapid order execution and management. This underscores the need for intuitive front-end tools that integrate real-time news feeds, economic calendar data, indicator insights, AI-driven analytics, and responsive trading controls.
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Neural networks made easy (Part 41): Hierarchical models

Neural networks made easy (Part 41): Hierarchical models

The article describes hierarchical training models that offer an effective approach to solving complex machine learning problems. Hierarchical models consist of several levels, each of which is responsible for different aspects of the task.
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Implementing Practical Modules from Other Languages in MQL5 (Part 02): Building the REQUESTS Library, Inspired by Python

Implementing Practical Modules from Other Languages in MQL5 (Part 02): Building the REQUESTS Library, Inspired by Python

In this article, we implement a module similar to requests offered in Python to make it easier to send and receive web requests in MetaTrader 5 using MQL5.
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Neural Networks Made Easy (Part 91): Frequency Domain Forecasting (FreDF)

Neural Networks Made Easy (Part 91): Frequency Domain Forecasting (FreDF)

We continue to explore the analysis and forecasting of time series in the frequency domain. In this article, we will get acquainted with a new method to forecast data in the frequency domain, which can be added to many of the algorithms we have studied previously.
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Developing a multi-currency Expert Advisor (Part 18): Automating group selection considering forward period

Developing a multi-currency Expert Advisor (Part 18): Automating group selection considering forward period

Let's continue to automate the steps we previously performed manually. This time we will return to the automation of the second stage, that is, the selection of the optimal group of single instances of trading strategies, supplementing it with the ability to take into account the results of instances in the forward period.
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Introduction to MQL5 (Part 22): Building an Expert Advisor for the 5-0 Harmonic Pattern

Introduction to MQL5 (Part 22): Building an Expert Advisor for the 5-0 Harmonic Pattern

This article explains how to detect and trade the 5-0 harmonic pattern in MQL5, validate it using Fibonacci levels, and display it on the chart.
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Developing a quality factor for Expert Advisors

Developing a quality factor for Expert Advisors

In this article, we will see how to develop a quality score that your Expert Advisor can display in the strategy tester. We will look at two well-known calculation methods – Van Tharp and Sunny Harris.
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MQL5 Wizard Techniques you should know (Part 49): Reinforcement Learning with Proximal Policy Optimization

MQL5 Wizard Techniques you should know (Part 49): Reinforcement Learning with Proximal Policy Optimization

Proximal Policy Optimization is another algorithm in reinforcement learning that updates the policy, often in network form, in very small incremental steps to ensure the model stability. We examine how this could be of use, as we have with previous articles, in a wizard assembled Expert Advisor.
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DoEasy. Service functions (Part 2): Inside Bar pattern

DoEasy. Service functions (Part 2): Inside Bar pattern

In this article, we will continue to look at price patterns in the DoEasy library. We will also create the Inside Bar pattern class of the Price Action formations.
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From Novice to Expert: Mastering Detailed Trading Reports with Reporting EA

From Novice to Expert: Mastering Detailed Trading Reports with Reporting EA

In this article, we delve into enhancing the details of trading reports and delivering the final document via email in PDF format. This marks a progression from our previous work, as we continue exploring how to harness the power of MQL5 and Python to generate and schedule trading reports in the most convenient and professional formats. Join us in this discussion to learn more about optimizing trading report generation within the MQL5 ecosystem.
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Building A Candlestick Trend Constraint Model (Part 6): All in one integration

Building A Candlestick Trend Constraint Model (Part 6): All in one integration

One major challenge is managing multiple chart windows of the same pair running the same program with different features. Let's discuss how to consolidate several integrations into one main program. Additionally, we will share insights on configuring the program to print to a journal and commenting on the successful signal broadcast on the chart interface. Find more information in this article as we progress the article series.
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Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis (2)

Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis (2)

Join us for our follow-up discussion, where we will merge our first two trading strategies into an ensemble trading strategy. We shall demonstrate the different schemes possible for combining multiple strategies and also how to exercise control over the parameter space, to ensure that effective optimization remains possible even as our parameter size grows.
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Overcoming The Limitation of Machine Learning (Part 6): Effective Memory Cross Validation

Overcoming The Limitation of Machine Learning (Part 6): Effective Memory Cross Validation

In this discussion, we contrast the classical approach to time series cross-validation with modern alternatives that challenge its core assumptions. We expose key blind spots in the traditional method—especially its failure to account for evolving market conditions. To address these gaps, we introduce Effective Memory Cross-Validation (EMCV), a domain-aware approach that questions the long-held belief that more historical data always improves performance.
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Developing a Replay System (Part 77): New Chart Trade (IV)

Developing a Replay System (Part 77): New Chart Trade (IV)

In this article, we will cover some of the measures and precautions to consider when creating a communication protocol. These are pretty simple and straightforward things, so we won't go into too much detail in this article. But to understand what will happen, you need to understand the content of the article.
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MQL5 Wizard Techniques you should know (Part 31): Selecting the Loss Function

MQL5 Wizard Techniques you should know (Part 31): Selecting the Loss Function

Loss Function is the key metric of machine learning algorithms that provides feedback to the training process by quantifying how well a given set of parameters are performing when compared to their intended target. We explore the various formats of this function in an MQL5 custom wizard class.
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Neural Networks in Trading: Hierarchical Feature Learning for Point Clouds

Neural Networks in Trading: Hierarchical Feature Learning for Point Clouds

We continue to study algorithms for extracting features from a point cloud. In this article, we will get acquainted with the mechanisms for increasing the efficiency of the PointNet method.
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From Novice to Expert: Animated News Headline Using MQL5 (IV) — Locally hosted AI model market insights

From Novice to Expert: Animated News Headline Using MQL5 (IV) — Locally hosted AI model market insights

In today's discussion, we explore how to self-host open-source AI models and use them to generate market insights. This forms part of our ongoing effort to expand the News Headline EA, introducing an AI Insights Lane that transforms it into a multi-integration assistive tool. The upgraded EA aims to keep traders informed through calendar events, financial breaking news, technical indicators, and now AI-generated market perspectives—offering timely, diverse, and intelligent support to trading decisions. Join the conversation as we explore practical integration strategies and how MQL5 can collaborate with external resources to build a powerful and intelligent trading work terminal.
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From Novice to Expert: Backend Operations Monitor using MQL5

From Novice to Expert: Backend Operations Monitor using MQL5

Using a ready-made solution in trading without concerning yourself with the internal workings of the system may sound comforting, but this is not always the case for developers. Eventually, an upgrade, misperformance, or unexpected error will arise, and it becomes essential to trace exactly where the issue originates to diagnose and resolve it quickly. Today’s discussion focuses on uncovering what normally happens behind the scenes of a trading Expert Advisor, and on developing a custom dedicated class for displaying and logging backend processes using MQL5. This gives both developers and traders the ability to quickly locate errors, monitor behavior, and access diagnostic information specific to each EA.
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Developing a Replay System (Part 36): Making Adjustments (II)

Developing a Replay System (Part 36): Making Adjustments (II)

One of the things that can make our lives as programmers difficult is assumptions. In this article, I will show you how dangerous it is to make assumptions: both in MQL5 programming, where you assume that the type will have a certain value, and in MetaTrader 5, where you assume that different servers work the same.
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Creating a Trading Administrator Panel in MQL5 (Part VI): Multiple Functions Interface (I)

Creating a Trading Administrator Panel in MQL5 (Part VI): Multiple Functions Interface (I)

The Trading Administrator's role goes beyond just Telegram communications; they can also engage in various control activities, including order management, position tracking, and interface customization. In this article, we’ll share practical insights on expanding our program to support multiple functionalities in MQL5. This update aims to overcome the current Admin Panel's limitation of focusing primarily on communication, enabling it to handle a broader range of tasks.
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Developing a multi-currency Expert Advisor (Part 11): Automating the optimization (first steps)

Developing a multi-currency Expert Advisor (Part 11): Automating the optimization (first steps)

To get a good EA, we need to select multiple good sets of parameters of trading strategy instances for it. This can be done manually by running optimization on different symbols and then selecting the best results. But it is better to delegate this work to the program and engage in more productive activities.
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From Novice to Expert: Animated News Headline Using MQL5 (X)—Multiple Symbol Chart View for News Trading

From Novice to Expert: Animated News Headline Using MQL5 (X)—Multiple Symbol Chart View for News Trading

Today we will develop a multi-chart view system using chart objects. The goal is to enhance news trading by applying MQL5 algorithms that help reduce trader reaction time during periods of high volatility, such as major news releases. In this case, we provide traders with an integrated way to monitor multiple major symbols within a single all-in-one news trading tool. Our work is continuously advancing with the News Headline EA, which now features a growing set of functions that add real value both for traders using fully automated systems and for those who prefer manual trading assisted by algorithms. Explore more knowledge, insights, and practical ideas by clicking through and joining this discussion.
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Neural Networks in Trading: Market Analysis Using a Pattern Transformer

Neural Networks in Trading: Market Analysis Using a Pattern Transformer

When we use models to analyze the market situation, we mainly focus on the candlestick. However, it has long been known that candlestick patterns can help in predicting future price movements. In this article, we will get acquainted with a method that allows us to integrate both of these approaches.
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Neural Networks in Trading: Hierarchical Vector Transformer (Final Part)

Neural Networks in Trading: Hierarchical Vector Transformer (Final Part)

We continue studying the Hierarchical Vector Transformer method. In this article, we will complete the construction of the model. We will also train and test it on real historical data.
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Neural Networks Made Easy (Part 97): Training Models With MSFformer

Neural Networks Made Easy (Part 97): Training Models With MSFformer

When exploring various model architecture designs, we often devote insufficient attention to the process of model training. In this article, I aim to address this gap.
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Population optimization algorithms: Intelligent Water Drops (IWD) algorithm

Population optimization algorithms: Intelligent Water Drops (IWD) algorithm

The article considers an interesting algorithm derived from inanimate nature - intelligent water drops (IWD) simulating the process of river bed formation. The ideas of this algorithm made it possible to significantly improve the previous leader of the rating - SDS. As usual, the new leader (modified SDSm) can be found in the attachment.
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Developing an MQTT client for Metatrader 5: a TDD approach — Part 4

Developing an MQTT client for Metatrader 5: a TDD approach — Part 4

This article is the fourth part of a series describing our development steps of a native MQL5 client for the MQTT protocol. In this part, we describe what MQTT v5.0 Properties are, their semantics, how we are reading some of them, and provide a brief example of how Properties can be used to extend the protocol.
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Propensity score in causal inference

Propensity score in causal inference

The article examines the topic of matching in causal inference. Matching is used to compare similar observations in a data set. This is necessary to correctly determine causal effects and get rid of bias. The author explains how this helps in building trading systems based on machine learning, which become more stable on new data they were not trained on. The propensity score plays a central role and is widely used in causal inference.
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Developing an MQTT client for MetaTrader 5: a TDD approach — Final

Developing an MQTT client for MetaTrader 5: a TDD approach — Final

This article is the last part of a series describing our development steps of a native MQL5 client for the MQTT 5.0 protocol. Although the library is not production-ready yet, in this part, we will use our client to update a custom symbol with ticks (or rates) sourced from another broker. Please, see the bottom of this article for more information about the library's current status, what is missing for it to be fully compliant with the MQTT 5.0 protocol, a possible roadmap, and how to follow and contribute to its development.
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Neural networks made easy (Part 89): Frequency Enhanced Decomposition Transformer (FEDformer)

Neural networks made easy (Part 89): Frequency Enhanced Decomposition Transformer (FEDformer)

All the models we have considered so far analyze the state of the environment as a time sequence. However, the time series can also be represented in the form of frequency features. In this article, I introduce you to an algorithm that uses frequency components of a time sequence to predict future states.
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Neural Networks in Trading: Contrastive Pattern Transformer (Final Part)

Neural Networks in Trading: Contrastive Pattern Transformer (Final Part)

In the previous last article within this series, we looked at the Atom-Motif Contrastive Transformer (AMCT) framework, which uses contrastive learning to discover key patterns at all levels, from basic elements to complex structures. In this article, we continue implementing AMCT approaches using MQL5.
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MQL5 Wizard Techniques you should know (Part 50): Awesome Oscillator

MQL5 Wizard Techniques you should know (Part 50): Awesome Oscillator

The Awesome Oscillator is another Bill Williams Indicator that is used to measure momentum. It can generate multiple signals, and therefore we review these on a pattern basis, as in prior articles, by capitalizing on the MQL5 wizard classes and assembly.
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From Basic to Intermediate: BREAK and CONTINUE Statements

From Basic to Intermediate: BREAK and CONTINUE Statements

In this article, we will look at how to use the RETURN, BREAK, and CONTINUE statements in a loop. Understanding what each of these statements does in the loop execution flow is very important for working with more complex applications. 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.
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Connexus Helper (Part 5): HTTP Methods and Status Codes

Connexus Helper (Part 5): HTTP Methods and Status Codes

In this article, we will understand HTTP methods and status codes, two very important pieces of communication between client and server on the web. Understanding what each method does gives you the control to make requests more precisely, informing the server what action you want to perform and making it more efficient.
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The base class of population algorithms as the backbone of efficient optimization

The base class of population algorithms as the backbone of efficient optimization

The article represents a unique research attempt to combine a variety of population algorithms into a single class to simplify the application of optimization methods. This approach not only opens up opportunities for the development of new algorithms, including hybrid variants, but also creates a universal basic test stand. This stand becomes a key tool for choosing the optimal algorithm depending on a specific task.
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Dialectic Search (DA)

Dialectic Search (DA)

The article introduces the dialectical algorithm (DA), a new global optimization method inspired by the philosophical concept of dialectics. The algorithm exploits a unique division of the population into speculative and practical thinkers. Testing shows impressive performance of up to 98% on low-dimensional problems and overall efficiency of 57.95%. The article explains these metrics and presents a detailed description of the algorithm and the results of experiments on different types of functions.