MQL5 Programming Articles

The MetaTrader 5 platform allows developing and testing trading robots that simultaneously trade multiple financial instruments. The built-in Strategy Tester automatically downloads required tick history from the broker's server taking into account contract specifications, so the developer does not need to do anything manually. This makes it possible to easily and reliably reproduce trading environment conditions, including even millisecond intervals between the arrival of ticks on different symbols. In this article we will demonstrate the development and testing of a spread strategy on two Moscow Exchange futures.

MQL5 Standard Library makes your life as a developer easier. Nevertheless, it does not implement all the needs of all developers in the world, so if you feel that you need some more custom stuff you can take a step further and extend. This article walks you through integrating MetaQuotes' Zig-Zag technical indicator into the Standard Library. We get inspired by MetaQuotes' design philosophy to achieve our goal.

In the previous chapter of the first part of the series about graphical interfaces, the form class was enriched by methods which allowed managing the form by pressing its controls. In this article, we will test our work in different types of MQL program such as indicators and scripts. As the library was designed to be cross-platform so it could be used in all MetaTrader platforms, we will also test it in MetaTrader 4.

We continue to develop the Multiline Text box control. This time our task is to implement an automatic word wrapping in case a text box width overflow occurs, or a reverse word wrapping of the text to the previous line if the opportunity arises.

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, that is what I'm trying to teach in this article.

With this article we begin chapter nine of series of articles dedicated to creating graphical interfaces in MetaTrader trading terminals. It consists of two chapters where new elements of controls and interface, such as color picker, color button, progress bar and line chart are presented.

Based on universal tools designed for working with Kohonen networks, we construct the system of analyzing and selecting the optimal EA parameters and consider forecasting time series. In Part I, we corrected and improved the publicly available neural network classes, having added necessary algorithms. Now, it is time to apply them to practice.

If an indicator uses values of many other indicators for its calculations, it consumes a lot of memory. The article describes several methods of decreasing the memory consumption when using auxiliary indicators. Saved memory allows increasing the number of simultaneously used currency pairs, indicators and strategies in the client terminal. It increases the reliability of trade portfolio. Such a simple care about technical resources of your computer can turn into money resources at your deposit.

This is the last of the three articles devoted to graphical controls. It covers the creation of the main graphical interface component - the form - and its use in combination with other controls. In addition to the form classes, CFrame, CButton, CLabel classes have been added to the control library.

Hiding of the implementation details of classes/functions in an .ex5 file will enable you to share your know-how algorithms with other developers, set up common projects and promote them in the Web. And while the MetaQuotes team spares no effort to bring about the possibility of direct inheritance of ex5 library classes, we are going to implement it right now.

Matrices and vectors have been introduced in MQL5 for efficient operations with mathematical solutions. The new types offer built-in methods for creating concise and understandable code that is close to mathematical notation. Arrays provide extensive capabilities, but there are many cases in which matrices are much more efficient.

We continue to study machine learning methods. With this article, we begin another big topic, Reinforcement Learning. This approach allows the models to set up certain strategies for solving the problems. We can expect that this property of reinforcement learning will open up new horizons for building trading strategies.

In this article, we will consider combining the lists of bar objects for each used symbol period into a single symbol timeseries object. Thus, each symbol will have an object storing the lists of all used symbol timeseries periods.

We will present a modified version of the Expert Advisor from the previous article "MQL5 Cookbook: Position Properties on the Custom Info Panel". Some of the issues we will address include getting data from bars, checking for new bar events on the current symbol, including a trade class of the Standard Library to a file, creating a function to search for trading signals and a function for executing trading operations, as well as determining trade events in the OnTrade() function.

In this article, I will create two classes (the class of DOM snapshot object and the class of DOM snapshot series object) and test creation of the DOM data series.

The article considers the methodology for developing trading algorithms, in which a consistent scientific approach is used to analyze possible price patterns and to build trading algorithms based on these patterns. Development ideals are demonstrated using examples.

We continue to add new features to the rendered table: data sorting, managing the number of columns and rows, setting the table cell types to place controls into them.

A new version of the graphics library for creating scientific charts (the CGraphic class) has been presented recently. This update of the developed library for creating graphical interfaces will introduce a version with a new control for creating charts. Now it is even easier to visualize data of different types.

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.

In this article, I will create the class of the abstract graphical object. This object is to serve as a basis for creating the class of standard graphical objects. Graphical objects feature multiple properties. Therefore, I will need to do a lot of preparatory work before actually creating the abstract graphical object class. This work includes setting the properties in the library enumerations.

After I wrote a previous article on semi-automatic Expert Advisor with GUI interface it turned out that it would be desirable to enhance interface with some new functionalities for more complex indicators and Expert Advisors. After getting acquainted with MQL5 standard library classes I implemented new widgets. This article describes a process of designing and implementing new MQL5 GUI widgets that can be used in indicators and Expert Advisors. The widgets presented in the article are CChartObjectSpinner, CChartObjectProgressBar and CChartObjectEditTable.

In this article, I will consider tracking property value modification, as well as removing and renaming graphical objects in the library.

Volatility based breakout system identifies market ranges, then trades when price breaks above or below those levels, filtered by volatility measures such as ATR. This approach helps capture strong directional moves.

In this article, we develop a Trendline Breakout System in MQL5 that identifies support and resistance trendlines using swing points, validated by R-squared goodness of fit and angle constraints, to automate breakout trades. Our plan is to detect swing highs and lows within a specified lookback period, construct trendlines with a minimum number of touch points, and validate them using R-squared metrics and angle constraints to ensure reliability.

In the previous chapter, we wrote classes for creating vertical and horizontal scrollbars. In this chapter, we will implement them. We will write a class for creating the list view element, a compound part of which will be a vertical scrollbar.

This article provides programmatic definition of one of the movement continuation models. The main idea is defining two waves — the main and the correction one. For extreme points, I apply fractals as well as "potential" fractals - extreme points that have not yet formed as fractals.

Calls to user and technical indicators takes up very little space in the program code of automated trading systems. Often it’s simply a few code lines. But it often happens that it is these few lines of code which use up the largest portion of time, which needs to be spent on testing the Expert Advisor. Therefore, everything that is related to data calculations within an indicator, needs to be considered much more thoroughly than would seem at first glance. This article will talk precisely about this.

In this update of the library, the Table control (the CTable class) will be supplemented with new options. The lineup of controls in the table cells is expanded, this time adding text edit boxes and combo boxes. As an addition, this update also introduces the ability to resize the window of an MQL application during its runtime.

In the previous articles of this series, we have seen two reinforced learning algorithms. Each of them has its own advantages and disadvantages. As often happens in such cases, next comes the idea to combine both methods into an algorithm, using the best of the two. This would compensate for the shortcomings of each of them. One of such methods will be discussed in this article.

This article starts an extensive topic of creating controls in Windows Forms style using MQL5. My first object of interest is creating the panel class. It is already becoming difficult to manage things without controls. Therefore, I will create all possible controls in Windows Forms style.

This article is a continuation of the series of articles about deep neural networks. Here we will consider selecting samples (removing noise), reducing the dimensionality of input data and dividing the data set into the train/val/test sets during data preparation for training the neural network.