In the article, I will create the class of the standard graphical object memory allowing the object to save its states when its properties are modified. In turn, this allows retracting to the previous graphical object states.
In this article, I will refine the basic functionality for providing control over graphical object events from a library-based program. I will start from implementing the functionality for storing the graphical object change history using the "Object name" property as an example.
In this article, I will implement the basic functionality for tracking standard graphical object events. I will start from a double click event on a graphical object.
This article which is part of a series that follows Category Theory implementation of Graphs in MQL5, delves in Orders. We examine how concepts of Order-Theory can support monoid sets in informing trade decisions by considering two major ordering types.
Currently, the library is able to track standard graphical objects on the client terminal chart, including their removal and modification of some of their parameters. At the moment, it lacks the ability to create standard graphical objects from custom programs.
In this article, I will create a dynamic multidimensional array class with the ability to change the amount of data in any dimension. Based on the created class, I will create a two-dimensional dynamic array to store some dynamically changed properties of graphical objects.
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.
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In this article, I will complete the development of the descendant classes of the abstract graphical object class and start implementing the ability to store these objects in the collection class. In particular, I will create the functionality for adding newly created standard graphical objects to the collection class.
In this article, I will consider creation of descendant objects for the terminal abstract standard graphical object. The class object describes the properties that are common for all graphical objects. So, it is simply some kind of a graphical object. To clarify its affiliation with a real graphical object, we need to set the properties inherent in this particular graphical object in the descendant object class.
In this article, we will create a mathematical model for simulating multicurrency pricing and complete the study of the diversification principle as part of the search for mechanisms to increase the trading efficiency, which I started in the previous article with theoretical calculations.
As developers, we need to learn how to create and develop software that can be reusable and flexible without duplicated code especially if we have different objects with different behaviors. This can be smoothly done by using object-oriented programming techniques and principles. In this article, we will present the basics of MQL5 Object-Oriented programming to understand how we can use principles and practices of this critical topic in our software.
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.
In this article, I will improve all library objects by assigning a unique type to each object and continue the development of the library graphical objects collection class.
It is time to start the integration of the already created objects into the previously created library objects. This will ultimately endow each library object with its own graphical object allowing users to interact with the program.
This article is a continuation in a series that look at Category Theory implementation in MQL5. In here we examine how Graph-Theory could be integrated with monoids and other data structures when developing a close-out strategy to a trading system.
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 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.
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 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.
Here we will describe only one of the aspects of machine learning - activation functions. In artificial neural networks, a neuron activation function calculates an output signal value based on the values of an input signal or a set of input signals. We will delve into the inner workings of the process.
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 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.
This article continues the series on category theory implementation in MQL5. Here we look at monoid-groups as a means normalising monoid sets making them more comparable across a wider span of monoid sets and data types..
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.
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.
In this article, we will put into practice all the knowledge from this series. We will finally build a 100% automated and functional system. But before that, we still have to learn one last detail.
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.
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.
The multilayer perceptron is an evolution of the simple perceptron which can solve non-linear separable problems. Together with the backpropagation algorithm, this neural network can be effectively trained. In Part 3 of the Multilayer Perceptron and Backpropagation series, we'll see how to integrate this technique into the Strategy Tester. This integration will allow the use of complex data analysis aimed at making better decisions to optimize your trading strategies. In this article, we will discuss the advantages and problems of this technique.
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.
From this article on, start creating library functionality to work with price data. Today, create an object class which will store all price data which arrived with yet another tick.
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.
The article considers the grid trading approach based on stop pending orders and implemented in an MQL5 Expert Advisor on the Moscow Exchange (MOEX). When trading in the market, one of the simplest strategies is a grid of orders designed to "catch" the market price.
In the article, develop an object which will contain all data of one buffer for one indicator. Such objects will be necessary for storing serial data of indicator buffers. With their help, it will be possible to sort and compare buffer data of any indicators, as well as other similar data with each other.
