MQL4 and MQL5 Programming Articles

We will create an indicator based on the Gann's Square of 9, built by squaring time and price. We will prepare the code and test the indicator in the platform on different time intervals.

In this article, we will place Chart Trade in a floating window. In the previous part, we created a basic system which enables the use of templates within a floating window.

This article continues the topic of predicting the upcoming price movement. I invite you to get acquainted with the Multi-future Transformer architecture. Its main idea is to decompose the multimodal distribution of the future into several unimodal distributions, which allows you to effectively simulate various models of interaction between agents on the scene.

This article continues the series for beginners. Here we will discuss the basic principles of developing Expert Advisors (EAs). We will create two EAs: the first one will trade without indicators, using pending orders, and the second one will be based on the standard MA indicator, opening deals at the current price. Here I assume that you are no longer a complete beginner and have a relatively good command of the material from the previous articles.

The article discusses the possibilities of handling databases based on the SQLite engine. The CDatabase class has been formed for convenience and efficient use of OOP principles. It is subsequently involved in the creation and management of the database of macroeconomic events. The article provides the examples of using multiple methods of the CDatabase class.

In the previous work, we discussed the theoretical aspects of the PSformer framework, which includes two major innovations in the classical Transformer architecture: the Parameter Shared (PS) mechanism and attention to spatio-temporal segments (SegAtt). In this article, we continue the work we started on implementing the proposed approaches using MQL5.

In this article, we take the first step in MQL5 programming, even for complete beginners. We'll show you how to transform familiar candlestick patterns into a fully functional custom indicator. Candlestick patterns are valuable as they reflect real price action and signal market shifts. Instead of manually scanning charts—an approach prone to errors and inefficiencies—we'll discuss how to automate the process with an indicator that identifies and labels patterns for you. Along the way, we’ll explore key concepts like indexing, time series, Average True Range (for accuracy in varying market volatility), and the development of a custom reusable Candlestick Pattern library for use in future projects.

Today we will construct the second part of the Times & Trade system for market analysis. In the previous article "Times & Trade (I)" we discussed an alternative chart organization system, which would allow having an indicator for the quickest possible interpretation of deals executed in the market.

News trading can be complicated and overwhelming, in this article we will go through steps to obtain news data. Additionally we will learn about the MQL5 Economic Calendar and what it has to offer.

In this article, we will provide another volatility-based indicator named Chaikin Volatility. We will understand how to build a custom indicator after identifying how it can be used and constructed. We will share some simple strategies that can be used and then test them to understand which one can be better.

This article outlines a simple strategy for normalizing the market data using the daily range and training a neural network to enhance market predictions. The developed models may be used in conjunction with an existing technical analysis frameworks or on a standalone basis to assist in predicting the overall market direction. The framework outlined in this article may be further refined by any technical analyst to develop models suitable for both manual and automated trading strategies.

The article describes the properties of the non-linear model of conditional heteroscedasticity (GARCH). The iGARCH indicator has been built on its basis for predicting volatility one step ahead. The ALGLIB numerical analysis library is used to estimate the model parameters.

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.

Fibonacci tools are among the most popular instruments used by technical analysts. In this article, we’ll build an Interactive Fibonacci EA that draws retracement and extension levels that react dynamically to price movement, delivering real‑time alerts, stylish lines, and a scrolling news‑style headline. Another key advantage of this EA is flexibility; you can manually type the high (A) and low (B) swing values directly on the chart, giving you exact control over the market range you want to analyze.

DRAKON is a visual programming language designed to simplify interaction between specialists from different fields (biologists, physicists, engineers...) with programmers in Russian space projects (for example, in the Buran reusable spacecraft project). In this article, I will talk about how DRAKON makes the creation of algorithms accessible and intuitive, even if you have never encountered code, and also how it is easier for customers to explain their thoughts when ordering trading robots, and for programmers to make fewer mistakes in complex functions.

In this article, we will explore the third part of our journey in formulating a Dynamic Multi-Pair Expert Advisor (EA), focusing specifically on integrating Mean Reversion and Momentum trading strategies. We will break down how to detect and act on price deviations from the mean (Z-score), and how to measure momentum across multiple forex pairs to determine trade direction.

Layered memory approaches that mimic human cognitive processes enable the processing of complex financial data and adaptation to new signals, thereby improving the effectiveness of investment decisions in dynamic markets.

Join us in our discussion today as we look for an algorithmic procedure to minimize the total number of times we get stopped out of winning trades. The problem we faced is significantly challenging, and most solutions given in community discussions lack set and fixed rules. Our algorithmic approach to solving the problem increased the profitability of our trades and reduced our average loss per trade. However, there are further advancements to be made to completely filter out all trades that will be stopped out, our solution is a good first step for anyone to try.

In the previous article, we started exploring non-gradient optimization methods. We got acquainted with the genetic algorithm. Today, we will continue this topic and will consider another class of evolutionary algorithms.

Cycles are of great importance in our lives. Day and night, seasons, days of the week and many other cycles of different nature are present in the life of any person. In this article, we will consider cycles in financial markets.

Graphical price analysis systems are deservedly popular among traders. In this article, I am going to describe the complete Murray system, including its famous levels, as well as some other useful techniques for assessing the current price position and making a trading decision.

In this article, we will create an arbitration system that remains legal in the eyes of brokers, creates thousands of synthetic prices on the Forex market, analyzes them, and successfully trades for profit.

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

In this article, we develop a JSON parsing framework in MQL5 to handle data exchange for AI API integration, focusing on a JSON class for processing JSON structures. We implement methods to serialize and deserialize JSON data, supporting various data types like strings, numbers, and objects, essential for communicating with AI services like ChatGPT, enabling future AI-driven trading systems by ensuring accurate data handling and manipulation.

In this article, we develop a dynamic multi-symbol, multi-period RSI indicator dashboard in MQL5, providing traders real-time RSI values across various symbols and timeframes. The dashboard features interactive buttons, real-time updates, and color-coded indicators to help traders make informed decisions.

We have built a robust feature engineering pipeline using proper tick-based bars to eliminate data leakage and solved the critical problem of labeling with meta-labeled triple-barrier signals. This installment covers the advanced labeling technique, trend-scanning, for adaptive horizons. After covering the theory, an example shows how trend-scanning labels can be used with meta-labeling to improve on the classic moving average crossover strategy.