MQL4 and MQL5 Programming Articles

In this article, I will add new mouse events relative to the boundaries of the working areas of WinForms objects and fix some shortcomings in the functioning of the TabControl and SplitContainer controls.

In the current article, I will implement the ability to change the properties and appearance of the newly created SplitContainer control.

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.

We continue to study reinforcement learning methods. In the previous article, we got acquainted with the Deep Q-Learning method. In this method, the model is trained to predict the upcoming reward depending on the action taken in a particular situation. Then, an action is performed in accordance with the policy and the expected reward. But it is not always possible to approximate the Q-function. Sometimes its approximation does not generate the desired result. In such cases, approximation methods are applied not to utility functions, but to a direct policy (strategy) of actions. One of such methods is Policy Gradient.

In this article, I will create the class of an auxiliary panel separator object for the SplitContainer control.

In the current article, I will start developing the SplitContainer control from the MS Visual Studio toolkit. This control consists of two panels separated by a vertical or horizontal movable separator.

We continue to study reinforcement learning. In this article, we will get acquainted with the Deep Q-Learning method. The use of this method has enabled the DeepMind team to create a model that can outperform a human when playing Atari computer games. I think it will be useful to evaluate the possibilities of the technology for solving trading problems.

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, I will create the functionality for scrolling tab headers in TabControl using scrolling buttons. The functionality is meant to place tab headers into a single line from either side of the control.

In this article, I will place header scrolling control buttons in TabControl WinForms object in case the header bar does not fit the size of the control. Besides, I will implement the shift of the header bar when clicking on the cropped tab header.

In this article, I will create the functionality for hiding object sections located beyond their containers. Besides, I will create auxiliary arrow button objects to be used as part of other WinForms objects.

In this article, I will continue the development of TabControl and implement the arrangement of tab headers on all four sides of the control for all modes of setting the size of headers: Normal, Fixed and Fill To Right.

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 show you how to calculate the total profit or loss of any trade, including commission and swap. I will provide the most accurate mathematical model and use it to write the code and compare it with the standard. Besides, I will also try to get on the inside of the main MQL5 function to calculate profit and get to the bottom of all the necessary values from the specification.

In this article, I will continue working on the TabControl WinForm object — I will create a tab field object class, make it possible to arrange tab headers in several rows and add methods for handling object tabs.

This is an introductory article on optimization algorithm (OA) classification. The article attempts to create a test stand (a set of functions), which is to be used for comparing OAs and, perhaps, identifying the most universal algorithm out of all widely known ones.

In this series of articles, we have already mentioned Transfer Learning more than once. However, this was only mentioning. in this article, I suggest filling this gap and taking a closer look at Transfer Learning.

In this article, I will create a new algorithm for naming all graphical elements meant for building custom graphics, as well as continue developing the TabControl WinForms object.

In this article, I will fix and optimize handling the appearance of WinForms objects after moving the mouse cursor away from the object, as well as start the development of the TabControl WinForms object.

In this article, I will make additional changes to the CCI affecting the very logic of this indicator. Moreover, we will be able to see it in the main chart window.

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 am going to create the base object of WinForms object lists, as well as the two new objects: ListBox and ButtonListBox.