Articles with MQL5 programming examples

Access a huge collection of articles with code examples showing how to create indicators and trading robots for the MetaTrader platform in the MQL5 language. Source codes are attached to the articles, so you can open them in MetaEditor and run them to see how the applications work.

These articles will be useful both for those who have just started exploring automated trading and for professional traders with programming experience. They feature not only examples, but also contain new ideas.

Add a new article

latest | best

1 2 3 4 5 6 7 8 9 ...15

Understanding Programming Paradigms (Part 1): A Procedural Approach to Developing a Price Action Expert Advisor

Learn about programming paradigms and their application in MQL5 code. This article explores the specifics of procedural programming, offering hands-on experience through a practical example. You'll learn how to develop a price action expert advisor using the EMA indicator and candlestick price data. Additionally, the article introduces you to the functional programming paradigm.

Brute force approach to patterns search (Part VI): Cyclic optimization

In this article I will show the first part of the improvements that allowed me not only to close the entire automation chain for MetaTrader 4 and 5 trading, but also to do something much more interesting. From now on, this solution allows me to fully automate both creating EAs and optimization, as well as to minimize labor costs for finding effective trading configurations.

Filtering and feature extraction in the frequency domain

In this article we explore the application of digital filters on time series represented in the frequency domain so as to extract unique features that may be useful to prediction models.

Data label for time series mining (Part 4)：Interpretability Decomposition Using Label Data

This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!

Developing a Replay System — Market simulation (Part 17): Ticks and more ticks (I)

Here we will see how to implement something really interesting, but at the same time very difficult due to certain points that can be very confusing. The worst thing that can happen is that some traders who consider themselves professionals do not know anything about the importance of these concepts in the capital market. Well, although we focus here on programming, understanding some of the issues involved in market trading is paramount to what we are going to implement.

Trade transactions. Request and response structures, description and logging

The article considers handling trade request structures, namely creating a request, its preliminary verification before sending it to the server, the server's response to a trade request and the structure of trade transactions. We will create simple and convenient functions for sending trading orders to the server and, based on everything discussed, create an EA informing of trade transactions.

Developing a quality factor for Expert Advisors

In this article, we will see how to develop a quality score that your Expert Advisor can display in the strategy tester. We will look at two well-known calculation methods – Van Tharp and Sunny Harris.

Developing a Replay System — Market simulation (Part 11): Birth of the SIMULATOR (I)

In order to use the data that forms the bars, we must abandon replay and start developing a simulator. We will use 1 minute bars because they offer the least amount of difficulty.

Developing a Replay System — Market simulation (Part 10): Using only real data for Replay

Here we will look at how we can use more reliable data (traded ticks) in the replay system without worrying about whether it is adjusted or not.

Developing a Replay System — Market simulation (Part 09): Custom events

Here we'll see how custom events are triggered and how the indicator reports the state of the replay/simulation service.

Permuting price bars in MQL5

In this article we present an algorithm for permuting price bars and detail how permutation tests can be used to recognize instances where strategy performance has been fabricated to deceive potential buyers of Expert Advisors.

Structures in MQL5 and methods for printing their data

In this article we will look at the MqlDateTime, MqlTick, MqlRates and MqlBookInfo strutures, as well as methods for printing data from them. In order to print all the fields of a structure, there is a standard ArrayPrint() function, which displays the data contained in the array with the type of the handled structure in a convenient tabular format.

StringFormat(). Review and ready-made examples

The article continues the review of the PrintFormat() function. We will briefly look at formatting strings using StringFormat() and their further use in the program. We will also write templates to display symbol data in the terminal journal. The article will be useful for both beginners and experienced developers.

Alternative risk return metrics in MQL5

In this article we present the implementation of several risk return metrics billed as alternatives to the Sharpe ratio and examine hypothetical equity curves to analyze their characteristics.

Classification models in the Scikit-Learn library and their export to ONNX

In this article, we will explore the application of all classification models available in the Scikit-Learn library to solve the classification task of Fisher's Iris dataset. We will attempt to convert these models into ONNX format and utilize the resulting models in MQL5 programs. Additionally, we will compare the accuracy of the original models with their ONNX versions on the full Iris dataset.

Studying PrintFormat() and applying ready-made examples

The article will be useful for both beginners and experienced developers. We will look at the PrintFormat() function, analyze examples of string formatting and write templates for displaying various information in the terminal log.

How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 2): Indicator Signals: Multi Timeframe Parabolic SAR Indicator

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 1 symbol pair only from one symbol chart. This time we will use only 1 indicator, namely Parabolic SAR or iSAR in multi-timeframes starting from PERIOD_M15 to PERIOD_D1.

GUI: Tips and Tricks for creating your own Graphic Library in MQL

We'll go through the basics of GUI libraries so that you can understand how they work or even start making your own.

Developing a Replay System — Market simulation (Part 08): Locking the indicator

In this article, we will look at how to lock the indicator while simply using the MQL5 language, and we will do it in a very interesting and amazing way.

Developing a Replay System — Market simulation (Part 07): First improvements (II)

In the previous article, we made some fixes and added tests to our replication system to ensure the best possible stability. We also started creating and using a configuration file for this system.

Evaluating ONNX models using regression metrics

Regression is a task of predicting a real value from an unlabeled example. The so-called regression metrics are used to assess the accuracy of regression model predictions.

Elastic net regression using coordinate descent in MQL5

In this article we explore the practical implementation of elastic net regression to minimize overfitting and at the same time automatically separate useful predictors from those that have little prognostic power.

How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 1): Indicator Signals based on ADX in combination with Parabolic SAR

The Multi-Currency Expert Advisor in this article is Expert Advisor or trading robot that can trade (open orders, close orders and manage orders an more) for more than 1 symbol pair only from one symbol chart.

DoEasy. Controls (Part 32): Horizontal ScrollBar, mouse wheel scrolling

In the article, we will complete the development of the horizontal scrollbar object functionality. We will also make it possible to scroll the contents of the container by moving the scrollbar slider and rotating the mouse wheel, as well as make additions to the library, taking into account the new order execution policy and new runtime error codes in MQL5.

Monte Carlo Permutation Tests in MetaTrader 5

In this article we take a look at how we can conduct permutation tests based on shuffled tick data on any expert advisor using only Metatrader 5.

Developing a Replay System — Market simulation (Part 05): Adding Previews

We have managed to develop a way to implement the market replay system in a realistic and accessible way. Now let's continue our project and add data to improve the replay behavior.

Wrapping ONNX models in classes

Object-oriented programming enables creation of a more compact code that is easy to read and modify. Here we will have a look at the example for three ONNX models.

Developing a Replay System — Market simulation (Part 04): adjusting the settings (II)

Let's continue creating the system and controls. Without the ability to control the service, it is difficult to move forward and improve the system.

Developing a Replay System — Market simulation (Part 03): Adjusting the settings (I)

Let's start by clarifying the current situation, because we didn't start in the best way. If we don't do it now, we'll be in trouble soon.

Category Theory in MQL5 (Part 11): Graphs

This article is a continuation in a series that look at Category Theory implementation in MQL5. In here we examine how Graph-Theory could be integrated with monoids and other data structures when developing a close-out strategy to a trading system.

Creating an EA that works automatically (Part 15): Automation (VII)

To complete this series of articles on automation, we will continue discussing the topic of the previous article. We will see how everything will fit together, making the EA run like clockwork.

Matrices and vectors in MQL5: Activation functions

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.

Category Theory in MQL5 (Part 10): Monoid Groups

This article continues the series on category theory implementation in MQL5. Here we look at monoid-groups as a means normalising monoid sets making them more comparable across a wider span of monoid sets and data types..

Frequency domain representations of time series: The Power Spectrum

In this article we discuss methods related to the analysis of timeseries in the frequency domain. Emphasizing the utility of examining the power spectra of time series when building predictive models. In this article we will discuss some of the useful perspectives to be gained by analyzing time series in the frequency domain using the discrete fourier transform (dft).

Multibot in MetaTrader: Launching multiple robots from a single chart

In this article, I will consider a simple template for creating a universal MetaTrader robot that can be used on multiple charts while being attached to only one chart, without the need to configure each instance of the robot on each individual chart.

Implementing an ARIMA training algorithm in MQL5

In this article we will implement an algorithm that applies the Box and Jenkins Autoregressive Integrated Moving Average model by using Powells method of function minimization. Box and Jenkins stated that most time series could be modeled by one or both of two frameworks.

Population optimization algorithms: ElectroMagnetism-like algorithm (ЕМ)

The article describes the principles, methods and possibilities of using the Electromagnetic Algorithm in various optimization problems. The EM algorithm is an efficient optimization tool capable of working with large amounts of data and multidimensional functions.

Population optimization algorithms: Saplings Sowing and Growing up (SSG)