MQL5 Programming Articles

This article implements an MQL5 Expert Advisor that connects to a weekend gap indicator via iCustom and CopyBuffer, reading six buffers for buy/sell signals and SL/TP. It validates broker stop-distance rules, handles closed-bar confirmation and duplicate-signal control, and executes orders with a configurable magic number. The EA also includes midpoint stop-loss management and a backtesting procedure so you can verify behavior and adapt parameters to your setup.

Implement the Malaysian Engulfing concept in MQL5 with two coordinated indicators. One applies strict, body‑based engulfing rules for precise pattern detection; the other uses a state-driven model to monitor what follows—pullbacks and timed retests—directly on the chart. The result is a repeatable, rule-based workflow that replaces visual guesswork with programmable logic.

A structured MQL5 implementation of a multi‑symbol trading panel with clear separation of concerns: symbol handling, trading logic, and GUI. Integrated into an Expert Advisor, it validates symbols, exposes centralized controls for opening and positions managing across symbols, and applies SL/TP changes. Real‑time account and portfolio metrics help streamline routine operations from a single chart.

Fish School Search (FSS) is a new optimization algorithm inspired by the behavior of fish in a school, most of which (up to 80%) swim in an organized community of relatives. It has been proven that fish aggregations play an important role in the efficiency of foraging and protection from predators.

We build a calendar‑driven news filter entirely in MQL5, avoiding web requests and external DLLs. Part 1 covers loading and caching events, mapping them to symbols by currency, filtering by impact level, defining pre/post windows, and blocking new trades during active news, with optional pre‑news position closure. The result is a configurable, prop‑firm‑friendly control that reduces false pauses and protects entries during volatility.

In this article, we implement automated trade entry using the MQL5 Economic Calendar by applying user-defined filters and time offsets to identify qualifying news events. We compare forecast and previous values to determine whether to open a BUY or SELL trade. Dynamic countdown timers display the remaining time until news release and reset automatically after a trade.

Candlestick patterns offer valuable insights into potential market moves. Some single candles signal continuation of the current trend, while others foreshadow reversals, depending on their position within the price action. This article introduces an EA that automatically identifies four key candlestick formations. Explore the following sections to learn how this tool can enhance your price-action analysis.

In strategy development, there are many intricate details to consider, many of which are not highlighted for beginner traders. As a result, many traders, myself included, have had to learn these lessons the hard way. This article is based on my observations of common pitfalls that most beginner traders encounter when developing strategies on MQL5. It will offer a range of tips, tricks, and examples to help identify the disqualification of an EA and test the robustness of our own EAs in an easy-to-implement way. The goal is to educate readers, helping them avoid future scams when purchasing EAs as well as preventing mistakes in their own strategy development.

Points of support and resistance are critical levels that signal potential trend reversals and continuations. Although identifying these levels can be challenging, once you pinpoint them, you’re well-prepared to navigate the market. For further assistance, check out the Quarters Drawer tool featured in this article, it will help you identify both primary and minor support and resistance levels.

Discover practical techniques to optimize memory usage in MQL5 trading systems. Learn to build efficient, stable, and fast-performing Expert Advisors and indicators. We’ll explore how memory really works in MQL5, the common traps that slow your systems down or cause them to fail, and — most importantly — how to fix them.

This article explores the development of an Expert Advisor (EA) for automated trading that combines technical analysis with deep learning predictions.

In this article, I will check and optimize binary options strategies in MetaTrader 5.

Join us today as we challenge ourselves to build a trading strategy around the USDJPY pair. We will trade candlestick patterns that are formed on the daily time frame because they potentially have more strength behind them. Our initial strategy was profitable, which encouraged us to continue refining the strategy and adding extra layers of safety, to protect the capital gained.

This part focuses on building a flexible, adaptive trading model trained on historical XAUUSD data, preparing it for ONNX export and potential integration into live trading systems.

In our previous article, we introduced a simple script called "The Quarters Drawer." Building on that foundation, we are now taking the next step by creating a monitor Expert Advisor (EA) to track these quarters and provide oversight regarding potential market reactions at these levels. Join us as we explore the process of developing a zone detection tool in this article.

Today we will add some more resources to our EA. This interesting article can provide some new ideas and methods of presenting information. At the same time, it can assist in fixing minor flaws in your projects.

Here we will describe only one of the aspects of machine learning - activation functions. In artificial neural networks, a neuron activation function calculates an output signal value based on the values of an input signal or a set of input signals. We will delve into the inner workings of the process.

Variables and data types are very important topics not only in MQL5 programming but also in any programming language. MQL5 variables and data types can be categorized as simple and advanced ones. In this article, we will identify and learn about advanced ones because we already mentioned simple ones in a previous article.

Depth of Market is undoubtedly a very important element for executing fast trades, especially in High Frequency Trading (HFT) algorithms. In this series of articles, we will look at this type of trading events that can be obtained through a broker on many tradable symbols. We will start with an indicator, where you can customize the color palette, position and size of the histogram displayed directly on the chart. We will also look at how to generate BookEvent events to test the indicator under certain conditions. Other possible topics for future articles include how to store price distribution data and how to use it in a strategy tester.

In this discussion, we will set the foundation for using powerful linear, algebra tools that are implemented in the MQL5 matrix and vector API. For us to make proficient use of this API, we need to have a firm understanding of the principles in linear algebra that govern intelligent use of these methods. This article aims to get the reader an intuitive level of understanding of some of the most important rules of linear algebra that we, as algorithmic traders in MQL5 need,to get started, taking advantage of this powerful library.

This article guides you through building a custom Heikin Ashi indicator from scratch and demonstrates how to integrate custom indicators into an EA. It covers indicator calculations, trade execution logic, and risk management techniques to enhance automated trading strategies.

In this third part, we revisit the Simple Hedge and Simple Grid Expert Advisors (EAs) developed earlier. Our focus shifts to refining the Simple Hedge EA through mathematical analysis and a brute force approach, aiming for optimal strategy usage. This article delves deep into the mathematical optimization of the strategy, setting the stage for future exploration of coding-based optimization in later installments.

With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.

We will continue to build a system for working in the FOREX market. In order to solve this problem, we must first declare the loading of ticks before loading the previous bars. This solves the problem, but at the same time forces the user to follow some structure in the configuration file, which, personally, does not make much sense to me. The reason is that by designing a program that is responsible for analyzing and executing what is in the configuration file, we can allow the user to declare the elements he needs in any order.

If we are going to automate periodic optimization, we need to think about auto updates of the settings of the EAs already running on the trading account. This should also allow us to run the EA in the strategy tester and change its settings within a single run.

For decades, traders have been using the Kelly Criterion formula to determine the optimal proportion of capital to allocate to an investment or bet to maximize long-term growth while minimizing the risk of ruin. However, blindly following Kelly Criterion using the result of a single backtest is often dangerous for individual traders, as in live trading, trading edge diminishes over time, and past performance is no predictor of future result. In this article, I will present a realistic approach to applying the Kelly Criterion for one or more EA's risk allocation in MetaTrader 5, incorporating Monte Carlo simulation results from Python.

Data mining is crucial to a data scientist and a trader because very often, the data isn't as straightforward as we think it is. The human eye can not understand the minor underlying pattern and relationships in the dataset, maybe the K-means algorithm can help us with that. Let's find out...

Monitoring manually drawn trendlines requires constant chart observation, which can cause important price interactions to be missed. This article develops a trendline monitoring Expert Advisor that synchronizes manually drawn trendlines with automated monitoring logic in MQL5, generating alerts when price approaches, touches, or breaks a monitored line.

In this article, we will begin to stabilize the entire system, without which we might not be able to proceed to the next steps.

Discover the indispensable role of Support Vector Machines (SVM) in shaping the future of trading. This comprehensive guide explores how SVM can elevate your trading strategies, enhance decision-making, and unlock new opportunities in the financial markets. Dive into the world of SVM with real-world applications, step-by-step tutorials, and expert insights. Equip yourself with the essential tool that can help you navigate the complexities of modern trading. Elevate your trading game with SVM—a must-have for every trader's toolbox.