This is the must read article for anyone wanting to improve their programming career. This article series is aimed at making you the best programmer you can possibly be, no matter how experienced you are. The discussed ideas work for MQL5 programming newbies as well as professionals.
In this article, I will define the animation principles to be used in some parts of the library. I will also develop a class for copying a part of the image and pasting it to a specified form object location while preserving and restoring the part of the form background the image is to be superimposed on.
There are a lot of bad habits that newbies and even advanced programmers are doing that are keeping them from becoming the best they can be to their coding career. We are going to discuss and address them in this article. This article is a must read for everyone who wants to become successful developer in MQL5.
In this article, I will create a separate class for the shadow object, which is a descendant of the graphical element object, as well as add the ability to fill the object background with a gradient fill.
In this article, I will describe the concept of building various library GUI design themes, create the Form object, which is a descendant of the graphical element class object, and prepare data for creating shadows of the library graphical objects, as well as for further development of the functionality.
In this article, I will continue the development of the basic graphical element class of all library graphical objects powered by the CCanvas Standard Library class. I will create the methods for drawing graphical primitives and for displaying a text on a graphical element object.
In this article, I will rework the concept of building graphical objects from the previous article and prepare the base class of all graphical objects of the library powered by the Standard Library CCanvas class.
The article opens up a new large section of the library for working with graphics. In the current article, I will create the mouse status object, the base object of all graphical elements and the class of the form object of the library graphical elements.
Cluster analysis is one of the most important elements of artificial intelligence. In this article, I attempt applying the cluster analysis of the indicator slope to get threshold values for determining whether a market is flat or following a trend.
In this article, I will complete working with chart object classes and their collection. I will also implement auto tracking of changes in chart properties and their windows, as well as saving new parameters to the object properties. Such a revision allows the future implementation of an event functionality for the entire chart collection.
These are some tips from a professional programmer about methods, techniques and auxiliary tools which can make programming easier. We will discuss parameters which can be restored after terminal restart (shutdown). All examples are real working code segments from my Cayman project.
In this article, I will create the functionality for tracking some chart object events — adding/removing symbol charts and chart subwindows, as well as adding/removing/changing indicators in chart windows.
In this article I will try to expand the classic concept of swap trading methods. I will explain why I have come to the conclusion that this concept deserves special attention and is absolutely recommended for study.
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.
The article provides the description of the technology aimed at increasing the effectiveness of any automated trading system. It provides a brief explanation of the idea, as well as its underlying basics, possibilities and disadvantages.
The article discusses a popular MVC pattern, as well as the possibilities, pros and cons of its usage in MQL programs. The idea is to split an existing code into three separate components: Model, View and Controller.
With this article, I start the development of the chart object collection class. The class will store the collection list of chart objects with their subwindows and indicators providing the ability to work with any selected charts and their subwindows or with a list of several charts at once.
In this article, I will continue the development of the chart object class. I will add the list of chart window objects featuring the lists of available indicators.
In the previous article, we started considering methods aimed at improving neural network training quality. In this article, we will continue this topic and will consider another approach — batch data normalization.
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 presents an improved brute force version, based on the goals set in the previous article. I will try to cover this topic as broadly as possible using Expert Advisors with settings obtained using this method. A new program version is attached to this article.
In this article, I will create the signal collection class of the MQL5.com Signals service with the functions for managing signals. Besides, I will improve the Depth of Market snapshot object class for displaying the total DOM buy and sell volumes.
As the next step in studying neural networks, I suggest considering the methods of increasing convergence during neural network training. There are several such methods. In this article we will consider one of them entitled Dropout.
In this article, I will create the collection class of Depths of Market of all symbols and start developing the functionality for working with the MQL5.com Signals service by creating the signal object class.
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.
This article describes the machine learning technique applied to grid and martingale trading. Surprisingly, this approach has little to no coverage in the global network. After reading the article, you will be able to create your own trading bots.
I continue filling the algorithm with the minimum necessary functionality and testing the results. The profitability is quite low but the articles demonstrate the model of the fully automated profitable trading on completely different instruments traded on fundamentally different markets.
In this article I will demonstrate some very interesting and useful techniques for automated trading. Some of them may be familiar to you. I will try to cover the most interesting methods and will explain why they are worth using. Furthermore, I will show what these techniques are apt to in practice. We will create Expert Advisors and test all the described techniques using historic quotes.
In the article, I will start developing the functionality for working with the Depth of Market. I will also create the class of the Depth of Market abstract order object and its descendants.
Perhaps one of the most advanced models among currently existing language neural networks is GPT-3, the maximal variant of which contains 175 billion parameters. Of course, we are not going to create such a monster on our home PCs. However, we can view which architectural solutions can be used in our work and how we can benefit from them.
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).
Since a program may use different symbols in its work, a separate list should be created for each of them. In this article, I will combine such lists into a tick data collection. In fact, this will be a regular list based on the class of dynamic array of pointers to instances of CObject class and its descendants of the Standard library.
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.
The popularity of these two methods grows, so a lot of libraries have been developed in Matlab, R, Python, C++ and others, which receive a training set as input and automatically create an appropriate network for the problem. Let us try to understand how the basic neural network type works (including single-neuron perceptron and multilayer perceptron). We will consider an exciting algorithm which is responsible for network training - gradient descent and backpropagation. Existing complex models are often based on such simple network models.
It is impossible to get a truly stable algorithm if we use optimization based on historical data to select parameters. A stable algorithm should be aware of what parameters are needed when working on any trading instrument at any time. It should not forecast or guess, it should know for sure.
The use of computer vision allows training neural networks on the visual representation of the price chart and indicators. This method enables wider operations with the whole complex of technical indicators, since there is no need to feed them digitally into the neural network.
We have previously considered the mechanism of self-attention in neural networks. In practice, modern neural network architectures use several parallel self-attention threads to find various dependencies between the elements of a sequence. Let us consider the implementation of such an approach and evaluate its impact on the overall network performance.
In this article, I will continue the development of the topic by improving the flexibility of the previously created algorithm. The algorithm became more stable with an increase in the number of candles in the analysis window or with an increase in the threshold percentage of the overweight of falling or growing candles. I had to make a compromise and set a larger sample size for analysis or a larger percentage of the prevailing candle excess.
This article provides a continuation to the brute force topic, and it introduces new opportunities for market analysis into the program algorithm, thereby accelerating the speed of analysis and improving the quality of results. New additions enable the highest-quality view of global patterns within this approach.
