MQL4 and MQL5 Programming Articles

In this article we will discuss in detail how to write a risk manager class for manual trading from scratch. This class can also be used as a base class for inheritance by algorithmic traders who use automated programs.

Explore the world of MQL5 arrays in Part 5, designed for absolute beginners. Simplifying complex coding concepts, this article focuses on clarity and inclusivity. Join our community of learners, where questions are embraced, and knowledge is shared!

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.

We continue to implement the approaches proposed by the authors of the FinCon framework. FinCon is a multi-agent system based on Large Language Models (LLMs). Today, we will implement the necessary modules and conduct comprehensive testing of the model on real historical data.

Detecting patterns in financial markets is challenging because it involves seeing what's on the chart, something that's difficult to undertake in MQL5 due to image limitations. In this article, we are going to discuss a decent model made in Python that helps us detect patterns present on the chart with minimal effort.

Dynamic multi pair Expert Advisor leverages both on correlation and inverse correlation strategies to optimize trading performance. By analyzing real-time market data, it identifies and exploits the relationship between currency pairs.

In this article we demonstrate how Hidden Markov Models trained using Python can be integrated into MetaTrader 5 applications. Hidden Markov Models are a powerful statistical tool used for modeling time series data, where the system being modeled is characterized by unobservable (hidden) states. A fundamental premise of HMMs is that the probability of being in a given state at a particular time depends on the process's state at the previous time slot.

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.

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.

Revolutionize your financial market analysis with Principal Component Analysis (PCA)! Discover how this powerful technique can unlock hidden patterns in your data, uncover latent market trends, and optimize your investment strategies. In this article, we explore how PCA can provide a new lens for analyzing complex financial data, revealing insights that would be missed by traditional approaches. Find out how applying PCA to financial market data can give you a competitive edge and help you stay ahead of the curve

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.

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

This article continues the series on category theory implementation in MQL5. Here we continue monoid-actions as a means of transforming monoids, covered in the previous article, leading to increased applications.

The article applies the A* heuristic to market structure by modeling validated swing highs and lows as graph nodes and weighting edges with ATR‑normalized distance, spread, and noise penalties. The engine searches the most efficient route to infer trade direction and targets, then filters signals by directional ratio, total path cost, and opposing swings. It anchors TP to the final node and SL to prior structure, with on‑chart visualization and configurable inputs.

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.

K-Means clustering takes the approach to grouping data points as a process that’s initially focused on the macro view of a data set that uses random generated cluster centroids before zooming in and adjusting these centroids to accurately represent the data set. We will look at this and exploit a few of its use cases.

Gary Anderson developed a method of market analysis based on a theory he dubbed the Janus Factor. The theory describes a set of indicators that can be used to reveal trends and assess market risk. In this article we will implement these tools in mql5.

GSA is a population optimization algorithm inspired by inanimate nature. Thanks to Newton's law of gravity implemented in the algorithm, the high reliability of modeling the interaction of physical bodies allows us to observe the enchanting dance of planetary systems and galactic clusters. In this article, I will consider one of the most interesting and original optimization algorithms. The simulator of the space objects movement is provided as well.

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.

We continue our discussion of reinforcement learning algorithms for solving continuous action space problems. In this article, I will present the Soft Actor-Critic (SAC) algorithm. The main advantage of SAC is the ability to find optimal policies that not only maximize the expected reward, but also have maximum entropy (diversity) of actions.

This article discusses the creation of a Messaging Interface for MetaTrader 5, aimed at System Administrators, to facilitate communication with other traders directly within the platform. Recent integrations of social platforms with MQL5 allow for quick signal broadcasting across different channels. Imagine being able to validate sent signals with just a click—either "YES" or "NO." Read on to learn more.

In this article, we continue mastering API and WebRequest in MQL5 by retrieving candlestick data from an external source. We focus on splitting the server response, cleaning the data, and extracting essential elements such as opening time and OHLC values for multiple daily candles, preparing the data for further analysis.

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 article we will create a system of cross orders. There is one type of assets that makes traders' life very difficult for traders — futures contracts. But why do they make life difficult?