MQL4 and MQL5 Programming Articles

The best practices, defining how to safely us an indicator, are not always easy to follow. Quiet market conditions may surprisingly produce readings on the indicator that do not qualify as a trading signal, leading to missed opportunities for algorithmic traders. This article will suggest a potential solution to this problem, as we discuss how to build trading applications capable of adapting their trading rules to the available market data.

Trading is, first of all, a calculus of probabilities. The proverb about idleness being an engine for progress reveals us the reason why all those indicators and trading systems have been developed. It comes that the major of newcomers in trading study "ready-made" trading theories. But, as luck would have it, there are some more undiscovered market secrets, and tools used in analyzing of price movements exist, basically, as those unrealized technical indicators or math and stat packages. Thanks awfully to Bill Williams for his contribution to the market movements theory. Though, perhaps, it's too early to rest on oars.

This article is devoted to the topic "Widening possibilities of MQL4 programs by using functions of operating systems and other means of program development". The article describes an example of a program system that implements the task of the synchronization of several terminal copies based on a single source template.

Err is human. Everybody makes mistakes: more or less frequently, through ignorance or inadvertently. You ask and we answer your questions about: terminal time, test results, Print in journal, symbols, history for Tester, history import, leverage, traffic, hints, scaling, wrong calculations, Invalid account, Empty News, Price changed, Not Enough Money, Market Is Closed.

In this article, we develop a ChatGPT-integrated program in MQL5 with a user interface, leveraging the JSON parsing framework from Part 1 to send prompts to OpenAI’s API and display responses on a MetaTrader 5 chart. We implement a dashboard with an input field, submit button, and response display, handling API communication and text wrapping for user interaction.

In a world where speed and precision matter, analysis tools need to be as smart as the markets we trade. This article presents an EA built on button logic—an interactive system that instantly transforms raw price data into meaningful statistical levels. With a single click, it calculates and displays mean, deviation, percentiles, and more, turning advanced analytics into clear on-chart signals. It highlights the zones where price is most likely to bounce, retrace, or break, making analysis both faster and more practical.

The article considers an example of creating multi-symbol multi-period standard indicators using a single indicator buffer for construction and working in the indicator subwindow. I am going to prepare the library classes for working with standard indicators working in the program main window and having more than one buffer for displaying their data.

The article includes an interactive "sandbox" as an Excel file which simulates randomized Expert Advisor backtest data. Readers can use this to help explore and more deeply understand the EA performance metrics offered by default with MetaTrader. The text of the article is designed to guide the user through this experience.

In the article, I will continue my work on the Panel WinForms object and implement its auto resizing to fit the general size of Dock objects located inside the panel. Besides, I will add the new properties to the Symbol library object.

Human traders had long participated in financial markets before the rise of computers, developing rules of thumb that guided their decisions. In this article, we revisit a well-known breakout strategy to test whether such market logic, learned through experience, can hold its own against systematic methods. Our findings show that while the original strategy produced high accuracy, it suffered from instability and poor risk control. By refining the approach, we demonstrate how discretionary insights can be adapted into more robust, algorithmic trading strategies.

Dijkstra's algorithm, a classic shortest-path solution in graph theory, can optimize trading strategies by modeling market networks. Traders can use it to find the most efficient routes in the candlestick chart data.

This is an introductory article on optimization algorithm (OA) classification. The article attempts to create a test stand (a set of functions), which is to be used for comparing OAs and, perhaps, identifying the most universal algorithm out of all widely known ones.

"Be sure to participate in the Automated Trading Championships, where you can get a truly invaluable experience!" - this is the motto of contestant Enbo Lu (luenbo) from China. He appeared in the TOP-10 of Automated Trading Championship 2012 last week and is now consistently trying to reach the podium.

In this article, the author proposes the way of improving trading systems presented in his previous articles. The article is of interest for traders already having experiences in writing Expert Advisors.