In the article, we will consider a sample multi-symbol multi-period indicator using the timeseries classes of the DoEasy library displaying the chart of a selected currency pair on a selected timeframe as candles in a subwindow. I am going to modify the library classes a bit and create a separate file for storing enumerations for program inputs and selecting a compilation language.
This article describes the connection of the graphical part of the auto optimizer program with its logical part. It considers the optimization launch process, from a button click to task redirection to the optimization manager.
The article considers the development of a simple multi-period indicator based on the DoEasy library. Let's improve the timeseries classes to receive data from any timeframes to display it on the current chart period.
Markets dropped down by more that 30% within one month. It seems to be the best time for testing grid- and martingale-based Expert Advisors. This article is an unplanned continuation of the series "Creating a Cross-Platform Grid EA". The current market provides an opportunity to arrange a stress rest for the grid EA. So, let's use this opportunity and test our Expert Advisor.
We have previously considered the creation of automatic walk-forward optimization. This time, we will proceed to the internal structure of the auto optimizer tool. The article will be useful for all those who wish to further work with the created project and to modify it, as well as for those who wish to understand the program logic. The current article contains UML diagrams which present the internal structure of the project and the relationships between objects. It also describes the process of optimization start, but it does not contain the description of the optimizer implementation process.
This paper continues checking the new conception to describe the window interface of MQL programs, using the structures of MQL. Automatically creating GUI based on the MQL markup provides additional functionality for caching and dynamically generating the elements and controlling the styles and new schemes for processing the events. Attached is an enhanced version of the standard library of controls.
The article deals with applying DoEasy library for creating multi-symbol multi-period indicators. We are going to prepare the library classes to work within indicators and test creating timeseries to be used as data sources in indicators. We will also implement creating and sending timeseries events.
In this part, we expand the trading signal searching and editing system, as well as introduce the possibility to use custom indicators and add program localization. We have previously created a basic system for searching signals, but it was based on a small set of indicators and a simple set of search rules.
This paper proposes a new conception to describe the window interface of MQL programs, using the structures of MQL. Special classes transform the viewable MQL markup into the GUI elements and allow manage them, set up their properties, and process the events in a unified manner. It also provides some examples of using the markup for the dialogs and elements of a standard library.
The article considers real-time update of timeseries data and sending messages about the "New bar" event to the control program chart from all timeseries of all symbols for the ability to handle these events in custom programs. The "New tick" class is used to determine the need to update timeseries for the non-current chart symbol and periods.
In the previous article, we developed the visual part of the application, as well as the basic interaction of GUI elements. This time we are going to add internal logic and the algorithm of trading signal data preparation, as well us the ability to set up signals, to search them and to visualize them in the monitor.
The article offers basic tools for the OLAP analysis of tester reports relating to single passes and optimization results. The tool can work with standard format files (tst and opt), and it also provides a graphical interface. MQL source codes are attached below.
The article deals with the development of the timeseries collection of specified timeframes for all symbols used in the program. We are going to develop the timeseries collection, the methods of setting collection's timeseries parameters and the initial filling of developed timeseries with historical data.
This article provides further description of the walk-forward optimization in the MetaTrader 5 terminal. In previous articles, we considered methods for generating and filtering the optimization report and started analyzing the internal structure of the application responsible for the optimization process. The Auto Optimizer is implemented as a C# application and it has its own graphical interface. The fifth article is devoted to the creation of this graphical interface.
This article deals with the theory and practical application of the algorithm for forecasting time series, based on support-vector method. It also proposes its implementation in MQL and provides test indicators and Expert Advisors. This technology has not been implemented in MQL yet. But first, we have to get to know math for it.
A big program starts with a small file, which then grows in size as you keep adding more functions and objects. Most robot developers utilize include files to handle this problem. However, there is a better solution: start developing any trading application in a project. There are so many reasons to do so.
The main purpose of the article is to describe the mechanism of working with our application and its capabilities. Thus the article can be treated as an instruction on how to use the application. It covers all possible pitfalls and specifics of the application usage.
In this article, we will consider combining the lists of bar objects for each used symbol period into a single symbol timeseries object. Thus, each symbol will have an object storing the lists of all used symbol timeseries periods.
This article deals with the theory and practical use of the algorithm for forecasting time series, based on the empirical decomposition mode. It proposes the MQL implementation of this method and presents test indicators and Expert Advisors.
This article starts a new series about the creation of the DoEasy library for easy and fast program development. In the current article, we will implement the library functionality for accessing and working with symbol timeseries data. We are going to create the Bar object storing the main and extended timeseries bar data, and place bar objects to the timeseries list for convenient search and sorting of the objects.
In the previous article, we created the application framework, which we will use as the basis for all further work. In this part, we will proceed with the development: we will create the visual part of the application and will configure basic interaction of interface elements.
In the previous part, we considered the implementation of the MySQL connector. In this article, we will consider its application by implementing the service for collecting signal properties and the program for viewing their changes over time. The implemented example has practical sense if users need to observe changes in properties that are not displayed on the signal's web page.
In this article, we will complete the description of the pending request trading concept and create the functionality for removing pending orders, as well as modifying orders and positions under certain conditions. Thus, we are going to have the entire functionality enabling us to develop simple custom strategies, or rather EA behavior logic activated upon user-defined conditions.
MetaTrader 5 has received network functions recently. This opened up great opportunities for programmers developing products for the Market. Now they can implement things that required dynamic libraries before. In this article, we will consider them using the implementation of the MySQL as an example.
3D graphics provide excellent means for analyzing huge amounts of data as they enable the visualization of hidden patterns. These tasks can be solved directly in MQL5, while DireсtX functions allow creating three-dimensional object. Thus, it is even possible to create programs of any complexity, even 3D games for MetaTrader 5. Start learning 3D graphics by drawing simple three-dimensional shapes.
We continue the development of the library functionality featuring trading using pending requests. We have already implemented sending conditional trading requests for opening positions and placing pending orders. In the current article, we will implement conditional position closure – full, partial and closing by an opposite position.
In this article we will continue dealing with the OLAP technology applied to trading. We will expand the functionality presented in the first two articles. This time we will consider the operational analysis of quotes. We will put forward and test the hypotheses on trading strategies based on aggregated historical data. The article presents Expert Advisors for studying bar patterns and adaptive trading.
We continue the development of the functionality allowing users to trade using pending requests. In this article, we are going to implement the ability to place pending orders under certain conditions.
Starting with this article, we are going to develop a functionality allowing users to trade using pending requests under certain conditions, for example, when reaching a certain time limit, exceeding a specified profit or closing a position by stop loss.
In this article, we will discuss the idea of creating a multicurrency monitor of trading signals and will develop a future application structure along with its prototype, as well as create its framework for further operation. The article presents a step-by-step creation of a flexible multicurrency application which will enable the generation of trading signals and which will assist traders in finding the desired signals.
The article presents an extended study of seasonal characteristics: autocorrelation heat maps and scatter plots. The purpose of the article is to show that "market memory" is of seasonal nature, which is expressed through maximized correlation of increments of arbitrary order.
The third part serves as a bridge between the previous two parts: it describes the mechanism of interaction with the DLL considered in the first article and the objects for report downloading, which were described in the second article. We will analyze the process of wrapper creation for a class which is imported from DLL and which forms an XML file with the trading history. We will also consider a method for interacting with this wrapper.
In the previous article, we have created the classes of pending request objects corresponding to the general concept of library objects. This time, we are going to deal with the class allowing the management of pending request objects.
In the previous articles, we checked the concept of pending trading requests. A pending request is, in fact, a common trading order executed by a certain condition. In this article, we are going to create full-fledged classes of pending request objects — a base request object and its descendants.
Artificial intelligence is often associated with something fantastically complex and incomprehensible. At the same time, artificial intelligence is increasingly mentioned in everyday life. News about achievements related to the use of neural networks often appear in different media. The purpose of this article is to show that anyone can easily create a neural network and use the AI achievements in trading.
This is the third article about the concept of pending requests. We are going to complete the tests of pending trading requests by creating the methods for closing positions, removing pending orders and modifying position and pending order parameters.
The development of trading strategies is associated with handling large amounts of data. Now, you are able to work with databases using SQL queries based on SQLite directly in MQL5. An important feature of this engine is that the entire database is placed in a single file located on a user's PC.
The first article within the Walk-Through Optimization series described the creation of a DLL to be used in our auto optimizer. This continuation is entirely devoted to the MQL5 language.
In this article, we will continue the development of trading requests, implement placing pending orders and eliminate detected shortcomings of the trading class operation.
In this article, we are going to store some data in the value of the orders and positions magic number and start the implementation of pending requests. To check the concept, let's create the first test pending request for opening market positions when receiving a server error requiring waiting and sending a repeated request.
