MQL5 Programming Articles

In today's article, we will look at how to control some object properties in a simple way using code. We will also see how a custom application can place more than one object on the same chart. In addition, we will begin to understand the importance of assigning a short name to any indicator we plan to implement.

In this article, we will look at how to fix two errors in the code. However, I will try to explain them in a way that will help you, beginner programmers, understand that things don't always go as you expect. Anyway, this is an opportunity to learn. The content presented here is intended solely for educational purposes. In no way should this application be considered as a final document with any purpose other than to explore the concepts presented.

Your Expert Advisor runs clean on your demo, then throws errors on a client's broker and quietly stops trading - and the code never changed. What changed is the broker's rulebook. This first article of the Broker Reality Check series builds a diagnostic EA that reads every relevant symbol trading condition - filling policy, stops and freeze levels, volume step, trade mode, swap and the triple-swap day - and flags the ones that silently break EAs, in plain language. It shows a green/amber/red panel, prints a report and dumps every Market Watch symbol to CSV, so you see why an OrderSend fails (10030, invalid stops, invalid volume) before it costs you a trade.

Our next focus in these series on ideas that can be rapidly prototyped with the MQL5 Wizard, is a Custom Trailing class that uses the Blooming Filter. Trailing Stop systems are an optional but very resourceful part to any trading system that we want to explore more in these series besides the traditional Entry Signals.

This is the second part of the article devoted to the implementation of the table model in MQL5 using the MVC (Model-View-Controller) architectural paradigm. The article discusses the development of table classes and the table header based on a previously created table model. The developed classes will form the basis for further implementation of View and Controller components, which will be discussed in the following articles.

The Actor–Director–Critic framework is an evolution of the classic agent learning architecture. The article presents practical experience of its implementation and adaptation to financial market conditions.

The article provides a detailed guide to creating the innovative 3DBarCustomSymbol.mq5 indicator, which generates custom symbols in MetaTrader 5 that combine price, time, volume, and volatility into a single three-dimensional representation. The mathematical foundations, system architecture, practical aspects of implementation and application in trading strategies are considered.

Today we will learn a technique that can help us a lot in different stages of our professional life as a programmer. Often it is not the platform itself that is limited, but the knowledge of the person who talks about the limitations. This article will tell you that with common sense and creativity you can make the MetaTrader 5 platform much more interesting and versatile without resorting to creating crazy programs or anything like that, and create simple yet safe and reliable code. We will use our creativity to modify existing code without deleting or adding a single line to the source code.

We add a descriptor-driven property stack and a floating ribbon that binds to the current selection on the drawing layer. The article covers the descriptor list for each tool, the engine get/set API with snapshot-and-restore live preview, and widget renderers for color, opacity, line width, line style, fonts, and level visibility. You get in-place, real-time editing of object appearance via a compact, draggable panel.

Raw feature correlations contain estimation noise and a shared market-mode component that distort clustering. We fit the Marcenko–Pastur noise ceiling (with an effective sample size correction), apply constant-residual denoising and market detonation, and run the Optimal Number of Clusters routine. The result is a cleaned correlation matrix and stable cluster labels that avoid substitution effects and feed clustered MDI/MDA in the next article.

Secrets of effective optimization of trading strategies in metaheuristic approaches. Community of Scientists Optimization is a new population-based algorithm inspired by the mechanisms of the scientific community. Unlike traditional nature-inspired metaphors, CoSO models unique aspects of human scientific activity: publishing results in journals, competing for grants, and forming research teams.

This article introduces file handling in MQL5 using a practical, project-based workflow. You will use FileSelectDialog to choose or create a CSV file, open it with FileOpen, and write structured account headers such as account name, balance, login, date range, and last update. The result is a clear foundation for a reusable trading journal and safe file operations in MetaTrader 5.

In this series of articles, we revisit well-known trading strategies to see if we can improve them using AI. In today's discussion, join us as we test whether there is a reliable relationship between precious metals and currencies.

In this discussion, we’ll explore additional advancements as we integrate refined event‑alerting logic for the economic calendar events displayed by the News Headline EA. This enhancement is critical—it ensures users receive timely notifications a short time before key upcoming events. Join this discussion to discover more.

This article implements a real-time monitoring dashboard for a self-healing MetaTrader 5 Expert Advisor. The dashboard displays the current EA state, virtual stop-loss and take-profit levels, breakeven and trailing status, recovery state, synchronization status, and heartbeat information directly on the chart. By exposing the internal recovery state visually, the Expert Advisor becomes easier to monitor, verify, and troubleshoot while managing active trades.

We add a tabbed settings window opened from the ribbon and bound to the selected object. The tabs — Style, Text, Coordinates, and Visibility — are built from the same descriptor system, with scrolling, per-level rows, and shared color/width/style popovers. The article covers layout, rendering, interaction, and inline price/time and numeric editing. You get one place to edit every property with live preview and commit-or-discard on close.

Backtests often understate spread, commission, and slippage. This MQL5 analyzer loads closing deals and simulates rising execution costs to measure robustness. It computes the breakeven cost per deal, the cushion over an assumed cost, the net profit and profit factor at that cost, and how many winners turn into losers, then summarizes the result with an A+ to F grade and targeted guidance.

We implement a Future Swing Projection indicator in MQL5 that analyzes historical swing structure and estimates the next move from recent price behavior. It locates six alternating swing points, measures five completed legs, and uses their average distance to project a target five bars ahead. The indicator draws swing legs, a projection line, ATR‑based support and resistance zones, and a label with the projected price to keep the process rule‑based and reproducible.

Despite what was shown in the previous article, all of this may seem simple at first. In reality, several problems remain, along with many tasks that still need to be completed. You, dear reader, may imagine that everything is easy and straightforward. Out of inexperience, you may simply accept whatever is presented to you. And that is a mistake you should try to avoid. Even worse is trying to use something without truly understanding what exactly you are using. Beginners often pass through a copy-and-paste stage. If you do not want to remain stuck at that stage forever, you should learn how to use certain tools. One of the tools most often used by programmers is documentation. The second is testing, supported by log files. Here we will see how to do this.

We are adding to our web application the ability to retrieve and display information about the terminal instances’ trading accounts, including balance, profit, connection status, and other important details. We will also implement a flexible configuration system that lets you manage application settings via an external JSON file, and improve the user interface of the main page.

An SLSQP optimizer is implemented in MQL5 to resolve parameter discrepancies between a volatility library and Python's ARCH module. The article details constraint handling, gradient options, configuration, and convergence controls and shows how to integrate the solver into existing code. Practical examples and comparisons demonstrate matched log‑likelihoods and parameters on shared datasets.

We take a closer look at the Color Harmony Algorithm (CHA) — a meta-heuristic optimization method based on Munsell's theory of color harmony. We explore the structure of the hue circle, harmony templates, the alternation between the concentration and dispersion phases, and the role of solution memory. From a theoretical framework to a working implementation in MQL5 and unbiased testing on a standard benchmark.

The article describes an approach to trade labeling using oscillators for machine learning models. This eliminates look-ahead bias. It has been shown that this type of labeling does not lead to model overfitting, and the strategies continue to perform well over the long term.

In this article, we will get acquainted with an algorithm that uses closed-form policy improvement operators to optimize Agent actions in offline mode.

In this article, we explore the butterfly curve, a parametric mathematical equation, and render it visually on a MQL5 canvas. We build an interactive display with a draggable, resizable canvas window, supersampled curve rendering, gradient backgrounds, and a color-segmented legend. By the end, we have a fully functional visual tool that plots the butterfly curve directly on the MetaTrader 5 chart.

The article describes an interesting approach to algorithmic trading based on symbolic mathematical equations instead of traditional machine learning "black boxes". The author demonstrates how to transform opaque neural networks into readable mathematical equations using the SymPy library and polynomial regression, allowing for a full understanding of the logic behind trading decisions. The approach combines the computational power of ML with the transparency of classical methods, giving traders the ability to analyze, adjust, and adapt models in real time.

In this article, we took a detailed look at the integration of the SSAM module into the SEW-ResNeXt block, demonstrating how the S3CE-Net framework enables the effective combination of spiking attention with residual blocks. This architecture ensures accurate processing of temporal and spatial data streams and high training stability. The modularity and flexibility of the components make it easier to extend the model and reuse proven methods.