MQL4 and MQL5 Programming Articles

In this article, we develop an MQL5 First Run User Setup Wizard for Expert Advisors, featuring a scrollable guide with an interactive dashboard, dynamic text formatting, and visual controls like buttons and a checkbox allowing users to navigate instructions and configure trading parameters efficiently. Users of the program get to have insight of what the program is all about and what to do on the first run, more like an orientation model.

Learn how to display logs directly on the MetaTrader chart in an organized way, with frames, titles and automatic scrolling. In this article, we show you how to create a visual log system using MQL5, ideal for monitoring what your robot is doing in real time.

In this article, we develop a canvas-based price dashboard in MQL5 using the CCanvas class to create interactive panels for visualizing recent price graphs and account statistics, with support for background images, fog effects, and gradient fills. The system includes draggable and resizable features via mouse event handling, theme toggling between dark and light modes with dynamic color adjustments, and minimize/maximize controls for efficient chart space management.

In previous works, we always assessed the current state of the environment. At the same time, the dynamics of changes in indicators always remained "behind the scenes". In this article I want to introduce you to an algorithm that allows you to evaluate the direct change in data between 2 successive environmental states.

There are many posts in the MQL5 Forum asking for help calculating the slope of price changes. This article will demonstrate one possible way of calculating the angle formed by the changes in price in any market you wish to trade. Additionally, we will answer if engineering this new feature is worth the extra effort and time invested. We will explore if the slope of the price can improve any of our AI model's accuracy when forecasting the USDZAR pair on the M1.

In the last piece, we concluded our look at the pairing of the gator oscillator and the accumulation/distribution oscillator when used in their typical setting of the raw signals they generate. These two indicators are complimentary as trend and volume indicators, respectively. We now follow up that piece, by examining the effect that supervised learning can have on enhancing some of the feature patterns we had reviewed. Our supervised learning approach is a CNN that engages with kernel regression and dot product similarity to size its kernels and channels. As always, we do this in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.

This article explores a new dimension of analysis using external libraries specifically designed for advanced analytics. These libraries, like pandas, provide powerful tools for processing and interpreting complex data, enabling traders to gain more profound insights into market dynamics. By integrating such technologies, we can bridge the gap between raw data and actionable strategies. Join us as we lay the foundation for this innovative approach and unlock the potential of combining technology with trading expertise.

In the previous article I explained how you can manipulate template data for use in OBJ_CHART. In that article, I only outlined the topic without going into details, since in that version the work was done in a very simplified way. This was done to make it easier to explain the content, because despite the apparent simplicity of many things, some of them were not so obvious, and without understanding the simplest and most basic part, you would not be able to truly understand the entire picture.

In this article, we prepare our MQL5 trading system for strategy testing by embedding economic calendar data as a resource for non-live analysis. We implement event loading and filtering for time, currency, and impact, then validate it in the Strategy Tester. This enables effective backtesting of news-driven strategies.

Learn how to implement a powerful custom logging framework for MQL5 that goes beyond simple Print() statements by supporting severity levels, multiple output handlers, and automated file rotation—all configurable on‐the‐fly. Integrate the singleton CLogger with ConsoleLogHandler and FileLogHandler to capture contextual, timestamped logs in both the Experts tab and persistent files. Streamline debugging and performance tracing in your Expert Advisors with clear, customizable log formats and centralized control.

This article will allow a beginner to write an implementation of a script from scratch for balancing risks when trading multiple instruments simultaneously. Besides, it may give experienced users new ideas for implementing their solutions in relation to the options proposed in this article.

In this article, we prepare our MQL5 trading system for strategy testing by embedding economic calendar data as a resource for non-live analysis. We implement event loading and filtering for time, currency, and impact, then validate it in the Strategy Tester. This enables effective backtesting of news-driven strategies.

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.

In this article, we will implement the first solution that will allow us to determine when a new bar may appear on the chart. This solution is applicable in a wide variety of situations. Understanding its development will help you grasp several important aspects. 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.

How does the market observe Fibonacci-based relationships? This sequence, where each subsequent number is equal to the sum of the two previous ones (1, 1, 2, 3, 5, 8, 13, 21...), not only describes the growth of the rabbit population. We will consider the Pythagorean hypothesis that everything in the world is subject to certain relationships of numbers...

How does the market observe Fibonacci-based relationships? This sequence, where each subsequent number is equal to the sum of the two previous ones (1, 1, 2, 3, 5, 8, 13, 21...), not only describes the growth of the rabbit population. We will consider the Pythagorean hypothesis that everything in the world is subject to certain relationships of numbers...

In this article, we'll complicate things a little. Using what was shown in the previous articles, we will start to open up the template file so that the user can use their own template. However, I will be making changes gradually, as I will also be refining the indicator to reduce the load on MetaTrader 5.

The article considers a metaheuristic Artificial Ecosystem-based Optimization (AEO) algorithm, which simulates interactions between ecosystem components by creating an initial population of solutions and applying adaptive update strategies, and describes in detail the stages of AEO operation, including the consumption and decomposition phases, as well as different agent behavior strategies. The article introduces the features and advantages of this algorithm.

We continue our last article on DDPG with MA and stochastic indicators by examining other key Reinforcement Learning classes crucial for implementing DDPG. Though we are mostly coding in python, the final product, of a trained network will be exported to as an ONNX to MQL5 where we integrate it as a resource in a wizard assembled Expert Advisor.

In this article, we enhance the gauge-based indicator in MQL5 to support multiple oscillators, allowing user selection through an enumeration for single or combined displays. We introduce sector and round gauge styles via derived classes from a base gauge framework, improving case rendering with arcs, lines, and polygons for a more refined visual appearance.

Artificial Cooperative Search (ACS) is an innovative method using a binary matrix and multiple dynamic populations based on mutualistic relationships and cooperation to find optimal solutions quickly and accurately. ACS unique approach to predators and prey enables it to achieve excellent results in numerical optimization problems.

The risk of whipsaw is extremely high during the first minute following a high-impact economic news release. In that brief window, price movements can be erratic and volatile, often triggering both sides of pending orders. Shortly after the release—typically within a minute—the market tends to stabilize, resuming or correcting the prevailing trend with more typical volatility. In this section, we’ll explore an alternative approach to news trading, aiming to assess its effectiveness as a valuable addition to a trader’s toolkit. Continue reading for more insights and details in this discussion.

This article describes a simple but comprehensive statistical arbitrage pipeline for trading a basket of cointegrated stocks. It includes a fully functional Python script for data download and storage; correlation, cointegration, and stationarity tests, along with a sample Metatrader 5 Service implementation for database updating, and the respective Expert Advisor. Some design choices are documented here for reference and for helping in the experiment replication.

The article presents a new metaheuristic algorithm, Chaos Game Optimization (CGO), which demonstrates a unique ability to maintain high efficiency when dealing with high-dimensional problems. Unlike most optimization algorithms, CGO not only does not lose, but sometimes even increases performance when scaling a problem, which is its key feature.

Today, we uncover the often overlooked statistical foundation behind supply and demand trading strategies. By combining MQL5 with Python through a Jupyter Notebook workflow, we conduct a structured, data-driven investigation aimed at transforming visual market assumptions into measurable insights. This article covers the complete research process, including data collection, Python-based statistical analysis, algorithm design, testing, and final conclusions. To explore the methodology and findings in detail, read the full article.

In this article, we revisit a classic crude oil trading strategy with the aim of enhancing it by leveraging supervised machine learning algorithms. We will construct a least-squares model to predict future Brent crude oil prices based on the spread between Brent and WTI crude oil prices. Our goal is to identify a leading indicator of future changes in Brent prices.

In the previous article, we made some fixes and added tests to our replication system to ensure the best possible stability. We also started creating and using a configuration file for this system.

Data classification for purposes of analysis and forecasting is a very diverse arena within machine learning and it features a large number of approaches and methods. This piece looks at one such approach, namely Agglomerative Hierarchical Classification.

This innovative trading bot integrates MetaTrader 5 with Python to leverage real-time social media sentiment analysis for automated trading decisions. By analyzing Twitter sentiment related to specific financial instruments, the bot translates social media trends into actionable trading signals. It utilizes a client-server architecture with socket communication, enabling seamless interaction between MT5's trading capabilities and Python's data processing power. The system demonstrates the potential of combining quantitative finance with natural language processing, offering a cutting-edge approach to algorithmic trading that capitalizes on alternative data sources.