Articles on the MQL5 programming and use of trading robots

The majority of students in my courses felt that MQL5 was really difficult to understand. In addition to this, they were searching for a straightforward method to automate a few processes. Find out how to begin working with MQL5 right now by reading the information contained in this article. Even if you have never done any form of programming before. And even in the event that you are unable to comprehend the previous illustrations that you have observed.

The article shows the workability of graphics in creation of a convenient interface to manage trading.

This article discusses an implementation of stop levels in an expert advisor in order to make it compatible with the two platforms MetaTrader 4 and MetaTrader 5.

Let's now shape the PHP-based Twitter idea which was introduced in the first part of this article. We are assembling the different parts of the SDSS. Regarding the client side of the system architecture, we are relying on the new MQL5 WebRequest() function for sending trading signals via HTTP.

The article deals with the problem of how to arrange pauses between trade operations when a number of experts work on one МТ 4 Client Terminal. It is intended for users who have basic skills in both working with the terminal and programming in MQL 4.

Developers of trading robots no longer need to market their services to traders that require Expert Advisors - as now they will find you. Already, thousands of traders place orders to MQL5 freelance developers, and pay for work in on MQL5.com. For 4 years, this service facilitated three thousand traders to pay for more than 10 000 jobs performed. And the activity of traders and developers is constantly growing!

This article summarizes and systematizes the principles of creating algorithms and elements of trading systems. The article considers designing of expert algorithm. As an example the CExpertAdvisor class is considered, which can be used for quick and easy development of trading systems.

This article introduces the Pattern Density Heatmap, a price‑action mapping tool that transforms repeated candlestick pattern detections into statistically significant support and resistance zones. Rather than treating each signal in isolation, the EA aggregates detections into fixed price bins, scores their density with optional recency weighting, and confirms levels against higher‑timeframe data. The resulting heatmap reveals where the market has historically reacted—levels that can be used proactively for trade timing, risk management, and strategy confidence across any trading style.

In this article, we build an MQL5 EA that detects regular RSI divergences using swing points with strength, bar limits, and tolerance checks. It executes trades on bullish or bearish signals with fixed lots, SL/TP in pips, and optional trailing stops. Visuals include colored lines on charts and labeled swings for better strategy insights.

Mean reversion is a type of contrarian trading where the trader expects the price to return to some form of equilibrium which is generally measured by a mean or another central tendency statistic.

The article considers applying the separate optimization method during various market conditions. Separate optimization means defining trading system's optimal parameters by optimizing for an uptrend and downtrend separately. To reduce the effect of false signals and improve profitability, the systems are made flexible, meaning they have some specific set of settings or input data, which is justified because the market behavior is constantly changing.

The article deals with convenient and efficient methods of information storage and viewing. Alternatives to the terminal standard log file and the Comment() function are considered here.

As a continuation of the neural network topic, I propose considering convolutional neural networks. This type of neural network are usually applied to analyzing visual imagery. In this article, we will consider the application of these networks in the financial markets.

In this article, we develop a Change of Character (CHoCH) detection system in MQL5 that identifies swing highs and lows over a user-defined bar length, labels them as HH/LH for highs or LL/HL for lows to determine trend direction, and triggers trades on breaks of these swing points, indicating a potential reversal, and trades the breaks when the structure changes.

In this article, I will create the signal collection class of the MQL5.com Signals service with the functions for managing signals. Besides, I will improve the Depth of Market snapshot object class for displaying the total DOM buy and sell volumes.