In this article, we will consider the main aspects of integration of neural networks and the trading terminal, with the purpose of creating a fully featured trading robot.
This is the first article in a series, in which I am going to describe a toolkit which enables manual application of chart graphics by utilizing keyboard shortcuts. It is very convenient: you press one key and a trendline appears, you press another key — this will create a Fibonacci fan with the necessary parameters. It will also be possible to switch timeframes, to rearrange layers or to delete all objects from the chart.
The article deals with creating a collection class of indicator buffer objects. I am going to test the ability to create and work with any number of buffers for indicators (the maximum number of buffers that can be created in MQL indicators is 512).
In this paper, we are completing the description of our concept of building the window interface of MQL programs, using the structures of MQL. Specialized graphical editor will allow to interactively set up the layout that consists of the basic classes of the GUI elements and then export it into the MQL description to use it in your MQL project. The paper presents the internal design of the editor and a user guide. Source codes are attached.
The article considers the development of indicator buffer object classes as descendants of the abstract buffer object simplifying declaration and working with indicator buffers, while creating custom indicator programs based on DoEasy library.
A Twitter client implemented as MQL class to allow you to send tweets with photos. All you need is to include a single self contained include file and off you go to tweet all your wonderful charts and signals.
Ever wanted to access tweets and/or post your trade signals on Twitter ? Search no more, these on-going article series will show you how to do it without using any DLL. Enjoy the journey of implementing Twitter API using MQL. In this first part, we will follow the glory path of authentication and authorization in accessing Twitter API.
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.
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.
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.
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.
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 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 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.
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.
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.
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 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.
After we send a trading order to the server, we need to check the error codes or the absence of errors. In this article, we will consider handling errors returned by the trade server and prepare for creating pending trading requests.
In this article, we will have a look at the handler of invalid trading order parameters and improve the trading event class. Now all trading events (both single ones and the ones occurred simultaneously within one tick) will be defined in programs correctly.
In the article, we continue the development of the trading class by implementing the control over incorrect trading order parameter values and voicing trading events.
In this article, we will start the development of the library base trading class and add the initial verification of permissions to conduct trading operations to its first version. Besides, we will slightly expand the features and content of the base trading class.
In this article, we will start the development of the new library section - trading classes. Besides, we will consider the development of a unified base trading object for MetaTrader 5 and MetaTrader 4 platforms. When sending a request to the server, such a trading object implies that verified and correct trading request parameters are passed to it.
The article deals with storing data in the program's source code and creating audio and graphical files out of them. When developing an application, we often need audio and images. The MQL language features several methods of using such data.
In this article, we will consider the class of displaying text messages. Currently, we have a sufficient number of different text messages. It is time to re-arrange the methods of their storage, display and translation of Russian or English messages to other languages. Besides, it would be good to introduce convenient ways of adding new languages to the library and quickly switching between them.
The article arranges the work of an account object on a new base object of all library objects, improves the CBaseObj base object and tests setting tracked parameters, as well as receiving events for any library objects.
In this article, we are going to finish the development of the base object of all library objects, so that any library object based on it is able to interact with a user. For example, users will be able to set the maximum acceptable size of a spread for opening a position and a price level, upon reaching which an event from a symbol object is sent to the program with the spread or price level-based signal.
In this article, we will create a new base class of all library objects adding the event functionality to all its descendants and develop the class for tracking symbol collection events based on the new base class. We will also change account and account event classes for developing the new base object functionality.
