MQL5 Programming Articles
Study the MQL5 language for programming trading strategies in numerous published articles mostly written by you - the community members. The articles are grouped into categories to help you quicker find answers to any questions related to programming: Integration, Tester, Trading Strategies, etc.
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MQL5 Wizard techniques you should know (Part 06): Fourier Transform
The Fourier transform introduced by Joseph Fourier is a means of deconstructing complex data wave points into simple constituent waves. This feature could be resourceful to traders and this article takes a look at that.
MQL5 Cookbook — Services
The article describes the versatile capabilities of services — MQL5 programs that do not require binding graphs. I will also highlight the differences of services from other MQL5 programs and emphasize the nuances of the developer's work with services. As examples, the reader is offered various tasks covering a wide range of functionality that can be implemented as a service.
MQL5 Cloud Network: Are You Still Calculating?
It will soon be a year and a half since the MQL5 Cloud Network has been launched. This leading edge event ushered in a new era of algorithmic trading - now with a couple of clicks, traders can have hundreds and thousands of computing cores at their disposal for the optimization of their trading strategies.
Price Action Analysis Toolkit Development (Part 39): Automating BOS and ChoCH Detection in MQL5
This article presents Fractal Reaction System, a compact MQL5 system that converts fractal pivots into actionable market-structure signals. Using closed-bar logic to avoid repainting, the EA detects Change-of-Character (ChoCH) warnings and confirms Breaks-of-Structure (BOS), draws persistent chart objects, and logs/alerts every confirmed event (desktop, mobile and sound). Read on for the algorithm design, implementation notes, testing results and the full EA code so you can compile, test and deploy the detector yourself.
Building a Custom Market Regime Detection System in MQL5 (Part 1): Indicator
This article details creating an MQL5 Market Regime Detection System using statistical methods like autocorrelation and volatility. It provides code for classes to classify trending, ranging, and volatile conditions and a custom indicator.
Creating an EA that works automatically (Part 03): New functions
Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode. In the previous article, we started to develop an order system that we will use in our automated EA. However, we have created only one of the necessary functions.
Population optimization algorithms: Particle swarm (PSO)
In this article, I will consider the popular Particle Swarm Optimization (PSO) algorithm. Previously, we discussed such important characteristics of optimization algorithms as convergence, convergence rate, stability, scalability, as well as developed a test stand and considered the simplest RNG algorithm.
Understanding functions in MQL5 with applications
Functions are critical things in any programming language, it helps developers apply the concept of (DRY) which means do not repeat yourself, and many other benefits. In this article, you will find much more information about functions and how we can create our own functions in MQL5 with simple applications that can be used or called in any system you have to enrich your trading system without complicating things.
Universal regression model for market price prediction (Part 2): Natural, technological and social transient functions
This article is a logical continuation of the previous one. It highlights the facts that confirm the conclusions made in the first article. These facts were revealed within ten years after its publication. They are centered around three detected dynamic transient functions describing the patterns in market price changes.
How to choose an Expert Advisor: Twenty strong criteria to reject a trading bot
This article tries to answer the question: how can we choose the right expert advisors? Which are the best for our portfolio, and how can we filter the large trading bots list available on the market? This article will present twenty clear and strong criteria to reject an expert advisor. Each criterion will be presented and well explained to help you make a more sustained decision and build a more profitable expert advisor collection for your profits.
Graphics in DoEasy library (Part 80): "Geometric animation frame" object class
In this article, I will optimize the code of classes from the previous articles and create the geometric animation frame object class allowing us to draw regular polygons with a given number of vertices.
Prices in DoEasy library (part 60): Series list of symbol tick data
In this article, I will create the list for storing tick data of a single symbol and check its creation and retrieval of required data in an EA. Tick data lists that are individual for each used symbol will further constitute a collection of tick data.
Better Programmer (Part 04): How to become a faster developer
Every developer wants to be able to write code faster, and being able to code faster and effective is not some kind of special ability that only a few people are born with. It's a skill that can be learned by every coder, regardless of years of experience on the keyboard.
Filtering Signals Based on Statistical Data of Price Correlation
Is there any correlation between the past price behavior and its future trends? Why does the price repeat today the character of its previous day movement? Can the statistics be used to forecast the price dynamics? There is an answer, and it is positive. If you have any doubt, then this article is for you. I'll tell how to create a working filter for a trading system in MQL5, revealing an interesting pattern in price changes.
Automated Parameter Optimization for Trading Strategies Using Python and MQL5
There are several types of algorithms for self-optimization of trading strategies and parameters. These algorithms are used to automatically improve trading strategies based on historical and current market data. In this article we will look at one of them with python and MQL5 examples.
Automating Trading Strategies in MQL5 (Part 18): Envelopes Trend Bounce Scalping - Core Infrastructure and Signal Generation (Part I)
In this article, we build the core infrastructure for the Envelopes Trend Bounce Scalping Expert Advisor in MQL5. We initialize envelopes and other indicators for signal generation. We set up backtesting to prepare for trade execution in the next part.
Building an Interactive Application to Display RSS Feeds in MetaTrader 5
In this article we look at the possibility of creating an application for the display of RSS feeds. The article will show how aspects of the Standard Library can be used to create interactive programs for MetaTrader 5.
Library for easy and quick development of MetaTrader programs (part IX): Compatibility with MQL4 - Preparing data
In the previous articles, we started creating a large cross-platform library simplifying the development of programs for MetaTrader 5 and MetaTrader 4 platforms. In the eighth part, we implemented the class for tracking order and position modification events. Here, we will improve the library by making it fully compatible with MQL4.
Engineering Trading Discipline into Code (Part 2): Building a Daily Trade Limit Enforcer for All Trades in MQL5
We have developed a system that enforces a daily trade limit to keep you aligned with your trading rules. It monitors all executed trades across the account and automatically intervenes once the defined limit is reached, preventing any further activity. By embedding control directly into the platform, the system ensures discipline is maintained even when market pressure rises.
Interview with Juan Pablo Alonso Escobar (ATC 2012)
"Everyone who is struggling with programming and who were not able to participate in this year's competition, know that it becomes a lot easier in time", said Juan Pablo Alonso Escobar (JPAlonso), the hero of today's interview.
Creating volatility forecast indicator using Python
In this article, we will forecast future extreme volatility using binary classification. Besides, we will develop an extreme volatility forecast indicator using machine learning.
Tips for Purchasing a Product on the Market. Step-By-Step Guide
This step-by-step guide provides tips and tricks for better understanding and searching for a required product. The article makes an attempt to puzzle out different methods of searching for an appropriate product, sorting out unwanted products, determining product efficiency and essentiality for you.
Custom Indicator Workshop (Part 2) : Building a Practical Supertrend Expert Advisor in MQL5
Learn how to build a Supertrend-driven Expert Advisor in MQL5 from the ground up. The article covers embedding the indicator as a resource, reading buffer values on closed bars, detecting confirmed flips, aligning and switching positions, and configuring stop-loss modes and position sizing. It concludes with Strategy Tester setup and reproducible tests, leaving you with a configurable EA and a clear framework for further research and extensions.
Neural networks made easy (Part 14): Data clustering
It has been more than a year since I published my last article. This is quite a lot time to revise ideas and to develop new approaches. In the new article, I would like to divert from the previously used supervised learning method. This time we will dip into unsupervised learning algorithms. In particular, we will consider one of the clustering algorithms—k-means.
Prices in DoEasy library (part 62): Updating tick series in real time, preparation for working with Depth of Market
In this article, I will implement updating tick data in real time and prepare the symbol object class for working with Depth of Market (DOM itself is to be implemented in the next article).
From Novice to Expert: Trading the RSI with Market Structure Awareness
In this article, we will explore practical techniques for trading the Relative Strength Index (RSI) oscillator with market structure. Our focus will be on channel price action patterns, how they are typically traded, and how MQL5 can be leveraged to enhance this process. By the end, you will have a rule-based, automated channel-trading system designed to capture trend continuation opportunities with greater precision and consistency.
Learn how to design a trading system by Chaikin Oscillator
Welcome to our new article from our series about learning how to design a trading system by the most popular technical indicator. Through this new article, we will learn how to design a trading system by the Chaikin Oscillator indicator.
How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 4): Triangular moving average — Indicator Signals
The Multi-Currency Expert Advisor in this article is Expert Advisor or trading robot that can trade (open orders, close orders and manage orders for example: Trailing Stop Loss and Trailing Profit) for more than one symbol pair only from one symbol chart. This time we will use only 1 indicator, namely Triangular moving average in multi-timeframes or single timeframe.
Gradient boosting in transductive and active machine learning
In this article, we will consider active machine learning methods utilizing real data, as well discuss their pros and cons. Perhaps you will find these methods useful and will include them in your arsenal of machine learning models. Transduction was introduced by Vladimir Vapnik, who is the co-inventor of the Support-Vector Machine (SVM).
Jeremy Scott - Successful MQL5 Market Seller
Jeremy Scott who is better known under Johnnypasado nickname at MQL5.community became famous offering products in our MQL5 Market service. Jeremy has already made several thousands of dollars in the Market and that is not the limit. We decided to take a closer look at the future millionaire and receive some pieces of advice for MQL5 Market sellers.
Ready-made templates for including indicators to Expert Advisors (Part 3): Trend indicators
In this reference article, we will look at standard indicators from the Trend Indicators category. We will create ready-to-use templates for indicator use in EAs - declaring and setting parameters, indicator initialization and deinitialization, as well as receiving data and signals from indicator buffers in EAs.
Other classes in DoEasy library (Part 67): Chart object class
In this article, I will create the chart object class (of a single trading instrument chart) and improve the collection class of MQL5 signal objects so that each signal object stored in the collection updates all its parameters when updating the list.
Graphical interfaces X: New features for the Rendered table (build 9)
Until today, the CTable was the most advanced type of tables among all presented in the library. This table is assembled from edit boxes of the OBJ_EDIT type, and its further development becomes problematic. Therefore, in terms of maximum capabilities, it is better to develop rendered tables of the CCanvasTable type even at the current development stage of the library. Its current version is completely lifeless, but starting from this article, we will try to fix the situation.
A Library for Constructing a Chart via Google Chart API
The construction of various types of diagrams is an essential part of the analyses of the market situation and the testing of a trading system. Frequently, in order to construct a nice looking diagram, it is necessary to organize the data output into a file, after which it is used in applications such as MS Excel. This is not very convenient and deprives us of the ability to dynamically update the data. Google Charts API provided the means for creating charts in online modes, by sending a special request to the server. In this article we attempt to automate the process of creating such a request and obtaining a chart from the Google server.
Cycle analysis using the Goertzel algorithm
In this article we present code utilities that implement the goertzel algorithm in Mql5 and explore two ways in which the technique can be used in the analysis of price quotes for possible strategy development.
The Most Active MQL5.community Members Have Been Awarded iPhones!
After we decided to reward the most outstanding MQL5.com participants, we have selected the key criteria to determine each participant's contribution to the Community development. As a result, we have the following champions who published the greatest amount of articles on the website - investeo (11 articles) and victorg (10 articles), and who submitted their programs to Code Base – GODZILLA (340 programs), Integer (61 programs) and abolk (21 programs).
How to create and test custom MOEX symbols in MetaTrader 5
The article describes the creation of a custom exchange symbol using the MQL5 language. In particular, it considers the use of exchange quotes from the popular Finam website. Another option considered in this article is the possibility to work with an arbitrary format of text files used in the creation of the custom symbol. This allows working with any financial symbols and data sources. After creating a custom symbol, we can use all the capabilities of the MetaTrader 5 Strategy Tester to test trading algorithms for exchange instruments.
An example of how to ensemble ONNX models in MQL5
ONNX (Open Neural Network eXchange) is an open format built to represent neural networks. In this article, we will show how to use two ONNX models in one Expert Advisor simultaneously.
Graphics in DoEasy library (Part 87): Graphical object collection - managing object property modification on all open charts
In this article, I will continue my work on tracking standard graphical object events and create the functionality allowing users to control changes in the properties of graphical objects placed on any charts opened in the terminal.
Neural networks made easy (Part 36): Relational Reinforcement Learning
In the reinforcement learning models we discussed in previous article, we used various variants of convolutional networks that are able to identify various objects in the original data. The main advantage of convolutional networks is the ability to identify objects regardless of their location. At the same time, convolutional networks do not always perform well when there are various deformations of objects and noise. These are the issues which the relational model can solve.