Articles with MQL5 programming examples

In this article we will look at what a union is. Here, through experiments, we will analyze the first constructions in which union can be used. However, what will be shown here is only a core part of a set of concepts and information that will be covered in subsequent articles. 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.

Before we proceed to the second stage of development, we need to revise some ideas. Do you know how to make MQL5 do what you need? Have you ever tried to go beyond what is contained in the documentation? If not, then get ready. Because we will be doing something that most people don't normally do.

This article shows how to build rounded speech bubbles in MQL5 by combining a rounded rectangle with a pointer triangle and controlling orientation (up, down, left, right). It details geometry precomputation, supersampled filling, rounded apex arcs, and segmented borders with an extension ratio for seamless joins. Readers get configurable code for size, radii, colors, opacity, and thickness, ready for alerts or tooltips in trading interfaces.

In this article, we will complete the first phase of construction. Although this part is fairly quick to complete, I will cover details that were not discussed previously. I will explain some points that many do not understand. Do you know why you have to press the Shift or Ctrl key?

In this article, we look at the process of developing a table model in MQL5 using the MVC (Model-View-Controller) architectural pattern to separate data logic, presentation, and control, enabling structured, flexible, and scalable code. We consider implementation of classes for building a table model, including the use of linked lists for storing data.

We continue developing the Connexus library. In this chapter, we explore the concept of headers in the HTTP protocol, explaining what they are, what they are for, and how to use them in requests. We cover the main headers used in communications with APIs, and show practical examples of how to configure them in the library.

Learn how to complete the creation of the final module in the History Manager EX5 library, focusing on the functions responsible for handling the most recently canceled pending order. This will provide you with the tools to efficiently retrieve and store key details related to canceled pending orders with MQL5.

In this eighth installment of the Mastering Log Records series, we explore the implementation of multilingual error messages in Logify, a powerful logging library for MQL5. You’ll learn how to structure errors with context, translate messages into multiple languages, and dynamically format logs by severity level. All of this with a clean, extensible, and production-ready design.

This article is a brief introduction to the concept of floating-point numbers. Since this text is very complex please, read it attentively and carefully. Do not expect to quickly master the floating-point system. It only becomes clear over time, as you gain experience using it. But this article will help you understand why your application sometimes produces results different from what you expect.

We present a complete workflow for adaptive filtering in MQL5 using the CNlEq Levenberg–Marquardt–like solver. The EA fits a VAMAC model—two EWMAs with an ATR‑based scaling—by supplying residuals and a Jacobian through CNlEq's reverse‑communication loop, with optional numerical or analytical derivatives. Code, setup instructions, and GBPUSD H1 tests show how to replace static thresholds with on‑bar re‑estimation.

This article advances the stateful supply and demand zone framework for MetaTrader 5 by replacing polling with an event-driven model based on OnChartEvent(). We split synchronization into dedicated handlers for creation, modification, and deletion, and separate market logic in OnTick() from user interactions in OnChartEvent(). A persistent, append-only CSV logger records all lifecycle events, improving responsiveness, state consistency, and recoverable history for downstream analysis.

Although the modules already interact with each other properly, an error occurs when trying to use the mouse pointer in the replay service. We need to fix this before moving on to the next step. Additionally, we will fix an issue in the mouse indicator code. So this version will be finally stable and properly polished.

This article improves the logging library by adding formatters in handlers, the CIntervalWatcher class to manage execution cycles, optimization with caching and file rotation, performance tests and practical examples. With these improvements, we ensure an efficient, scalable and adaptable logging system to different development scenarios.

Many MetaTrader 5 setups run several EAs on one account, so risk gets fragmented and correlated exposure slips through. The article introduces RiskGate, a centralized Service that evaluates EA intents account‑wide: EAs send a JSON signal, the Service returns approved, lot and reason. You will see the client/server wiring, example rules (daily loss, exposure and correlation caps), unit‑tested handler design, and an EA example. The result is consistent portfolio‑level risk with simpler EAs.

Learn a practical way to execute MetaTrader 5 trades from Telegram voice notes using a Python middleware and an MQL5 EA acting as an HTTP client. The article covers architecture, WebRequest polling, in-memory queuing, JSON parsing with null-terminator stripping, and a constrained command grammar with a 0.001-lot default. You will configure the environment and validate round‑trip latency suitable for mobile data connections.

This article explores an accessibility-focused enhancement that goes beyond default terminal alerts by leveraging MQL5 resource management to deliver contextual voice feedback. Instead of generic tones, the indicator communicates what has occurred and why, allowing traders to understand market events without relying solely on visual observation. This approach is especially valuable for visually impaired traders, but it also benefits busy or multitasking users who prefer hands-free interaction.

In this article, we will finally solve the problems with the simulation of ticks on a one-minute bar so that they can coexist with real ticks. This will help us avoid problems in the future. The material presented here is for educational purposes only. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.

RSI accumulates losses in trending conditions by firing at every threshold crossing regardless of market regime. A Random Forest secondary classifier trained on 12 contextual features — RSI momentum slope, EMA50 trend velocity, ATR-normalised trend stretch, and nine others — filters raw signals and scales position size by classifier confidence on EURUSD H1. Results compare plain RSI, meta-filtered RSI, and bet-sized RSI across a 16-month out-of-sample period with per-trade metrics and drawdown diagnostics.

When running multiple strategies in parallel, you may want to periodically close all open positions and start the strategies over again. The existing code only allows this behavior to be implemented through manual intervention. Let's try to automate this part.

After a break in development and improvement of the service used for replay/simulator, we are resuming work on it. Now that we've abandoned the use of resources like terminal globals, we'll have to completely restructure some parts of it. Don't worry, this process will be explained in detail so that everyone can follow the development of our service.

In this article, we will discuss creating a "Container" control that supports scrolling its contents. Within the process, the already implemented classes of graphics library controls will be improved.

We are beginning to implement the connection between Excel and MetaTrader 5, but first we need to understand some key points. This way, you won't have to rack your brains trying to figure out why something works or doesn't. And before you frown at the prospect of integrating Python and Excel, let's see how we can (to some extent) control MetaTrader 5 through Excel using xlwings. What we demonstrate here will primarily focus on educational objectives. However, don't think that we can only do what will be covered here.

In this article, we will continue our awareness of #define directive, but this time we will focus on its second form of use, that is, creating macros. Since this subject can be a bit complicated, we decided to use an application that we have been studying for some time. I hope you enjoy today's article.

In this article, we will begin looking at how to work with objects directly on the chart. This is done using code specially developed for demonstration purposes. Working with objects is very interesting and can be a lot of fun. Since this will be our first contact with the topic, we will start with something very simple.

The article presents an MQL5 Expert Advisor that adapts the Ford–Fulkerson max-flow method into a liquidity-capacity filter. Market structures—Swing Highs/Lows, Fair Value Gaps, Order Blocks, and Liquidity Pools—form a directed graph with edge capacities from volume, price reaction, distance, and structure quality. Maximum flow qualifies ICT setups, filters weak paths, and drives dynamic position sizing for a consistent, two-stage decision process.

This work presents an end-to-end pipeline: collect MetaTrader 5 data, engineer entropy/volatility/trend features, train a PyTorch classifier, and expose predictions through a Flask API. An MQL5 EA posts rolling prices each tick, receives probability and regime, and applies adaptive position sizing and stop distances. The result is a clear recipe for integrating ML inference with MetaTrader 5.