MQL4 and MQL5 Programming Articles

This article describes one of the possible approaches to data transformation aimed at improving the generalizability of the model, and also discusses sampling and selection of CatBoost models.

In the last article, we got acquainted with the Autoencoder algorithm. Like any other algorithm, it has its advantages and disadvantages. In its original implementation, the autoenctoder is used to separate the objects from the training sample as much as possible. This time we will talk about how to deal with some of its disadvantages.

In this article, I will create subordinate controls bound to the base element. The development will be performed using the base control functionality. In addition, I will tinker with the graphical element shadow object a bit since it still suffers from some logic errors when applied to any of the objects capable of having a shadow.

The article describes a method of fast optimization using the particle swarm algorithm. It also presents the method implementation in MQL, which is ready for use both in single-threaded mode inside an Expert Advisor and in a parallel multi-threaded mode as an add-on that runs on local tester agents.

In this article, we create several classes to facilitate real-time communication between MQL5 and Telegram. We focus on retrieving commands from Telegram, decoding and interpreting them, and sending appropriate responses back. By the end, we ensure that these interactions are effectively tested and operational within the trading environment

MetaTrader 4 gained its popularity with traders from all over the world, and it seemed like nothing more could be wished for. With its high processing speed, stability, wide array of possibilities for writing indicators, Expert Advisors, and informatory-trading systems, and the ability to chose from over a hundred different brokers, - the terminal greatly distinguished itself from the rest. But time doesn’t stand still, and we find ourselves facing a choice of MetaTrade 4 or MetaTrade 5. In this article, we will describe the main differences of the 5th generation terminal from our current favor.

Here I will show how to create screens with price tickers which are usually used to display quotes on the exchange. I will do it by only using MQL5, without using complex external programming.

This article tries to analyze several peculiarities of representation of quotes available in the MetaTrader client terminal. The article is general, it doesn't concern programming.

We will walk around statistical arbitrage, we will search with python for correlation and cointegration symbols, we will make an indicator for Pearson's coefficient and we will make an EA for trading statistical arbitrage with predictions done with python and ONNX models.

We got acquainted with the Q-learning method in one of the earlier articles within this series. This method averages rewards for each action. Two works were presented in 2017, which show greater success when studying the reward distribution function. Let's consider the possibility of using such technology to solve our problems.

In this article, I will start developing the functionality for creating composite graphical objects. The library will support creating composite graphical objects allowing those objects have any hierarchy of connections. I will prepare all the necessary classes for subsequent implementation of such objects.

This article which is part of a broader series on Category Theory implementation in MQL5, delves into Functors. We examine how a Linear Order can be mapped to a set, thanks to Functors; by considering two sets of data that one would typically dismiss as having any connection.

The article considers creation of classes of descendant objects of base abstract indicator. Such objects will provide access to features of creating indicator EAs, collecting and getting data value statistics of various indicators and prices. Also, create indicator object collection from which getting access to properties and data of each indicator created in the program will be possible.

We continue to enhance the Signals service, improve the mechanisms, add new functions and fix flaws. The MetaTrader Signals Service of 2012 and the current MetaTrader Signals Service are like two completely different services. Currently, we are implementing A Virtual Hosting Cloud service which consists of a network of servers to support specific versions of the MetaTrader client terminal.

In the current article, I will study and test the most powerful optimization algorithm - harmonic search (HS) inspired by the process of finding the perfect sound harmony. So what algorithm is now the leader in our rating?

A programmer from Krasnoyarsk Nikolay Ivanov (Techno) is a leader among the developers in terms of the number of completed orders - he has implemented already more than 200 applications in the Jobs service. In this interview, he is talking about the Jobs service, its specific features and challengers faced by programmers.

The best practices, defining how to safely us an indicator, are not always easy to follow. Quiet market conditions may surprisingly produce readings on the indicator that do not qualify as a trading signal, leading to missed opportunities for algorithmic traders. This article will suggest a potential solution to this problem, as we discuss how to build trading applications capable of adapting their trading rules to the available market data.