Articles with MQL5 programming examples

The article shows an example of how to develop an MQL5-program implementing the adaptive algorithm of clustering called Growing neural gas (GNG). The article is intended for the users who have studied the language documentation and have certain programming skills and basic knowledge in the area of neuroinformatics.

In this article, I will consider the algorithms allowing you to create your own technical indicator. You will learn how to obtain pretty complex and interesting results with very simple initial assumptions.

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.

In this article, I will expand the functionality of chart objects and arrange navigation through charts, creation of screenshots, as well as saving and applying templates to charts. Also, I will implement auto update of the collection of chart objects, their windows and indicators within them.

We will begin the journey to explore the steps and workflow on how to base development for MetaTrader 5 platform solely on Linux system in which the final product works seamlessly on both Windows and Linux system. We will get to know Wine, and Mingw; both are the essential tools to make cross-platform development works. Especially Mingw for its threading implementations (POSIX, and Win32) that we need to consider in choosing which one to go with. We then build a proof-of-concept DLL and consume it in MQL5 code, finally compare the performance of both threading implementations. All for your foundation to expand further on your own. You should be comfortable building MT related tools on Linux after reading this article.

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.

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.

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.

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.

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.

Indicator emissions are a little-studied area of market research. Primarily, this is due to the difficulty of analysis that is caused by the processing of very large arrays of time-varying data. Existing graphical analysis is too resource intensive and has therefore triggered the development of a parsimonious algorithm that uses time series of emissions. This article demonstrates how visual (intuitive image) analysis can be replaced with the study of integral characteristics of emissions. It can be of interest to both traders and developers of automated trading systems.

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.

In the current article, I will get rid of some errors related to handling graphical elements and continue the development of the CPanel control. In particular, I will implement the methods for setting the parameters of the font used by default for all panel text objects.

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).

Simplify your MetaTrader  5 charts with the Multi  Indicator  Handler EA. This interactive dashboard merges trend, momentum, and volatility indicators into one real‑time panel. Switch instantly between profiles to focus on the analysis you need most. Declutter with one‑click Hide/Show controls and stay focused on price action. Read on to learn step‑by‑step how to build and customize it yourself in MQL5.

In conclusion of the topic of working with timeseries organise storage, search and sort of data stored in indicator buffers which will allow to further perform the analysis based on values of the indicators to be created on the library basis in programs. The general concept of all collection classes of the library allows to easily find necessary data in the corresponding collection. Respectively, the same will be possible in the class created today.

In this article, I will develop the class of a single animation frame and its descendants. The class is to allow drawing shapes while maintaining and then restoring the background under them.

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.

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.

The article continues developing indicator object classes and their collections. For each indicator object create its description and correct collection class for error-free storage and getting indicator objects from the collection list.

In this article, we will discuss the implementation of MQL5 in partnership with Python to perform broker-related operations. Imagine having a continuously running Expert Advisor (EA) hosted on a VPS, executing trades on your behalf. At some point, the ability of the EA to manage funds becomes paramount. This includes operations such as topping up your trading account and initiating withdrawals. In this discussion, we will shed light on the advantages and practical implementation of these features, ensuring seamless integration of fund management into your trading strategy. Stay tuned!

The article considers creation of the custom indicator object for the use in EAs. Let’s slightly improve library classes and add methods to get data from indicator objects in EAs.