Articles on the MQL5 programming and use of trading robots

Support Vector Machines have long been used in fields such as bioinformatics and applied mathematics to assess complex data sets and extract useful patterns that can be used to classify data. This article looks at what a support vector machine is, how they work and why they can be so useful in extracting complex patterns. We then investigate how they can be applied to the market and potentially used to advise on trades. Using the Support Vector Machine Learning Tool, the article provides worked examples that allow readers to experiment with their own trading.

The article explores the advantages and disadvantages of trading in flat periods. The ten strategies created and tested within this article are based on the tracking of price movements inside a channel. Each strategy is provided with a filtering mechanism, which is aimed at avoiding false market entry signals.

The article describes a method of automated creation of neural network EAs using MQL5 Wizard and Hlaiman EA Generator. It shows you how you can easily start working with neural networks, without having to learn the entire body of theoretical information and writing your own code.

This article focuses on specifics of choice, preconditioning and evaluation of the input variables (predictors) for use in machine learning models. New approaches and opportunities of deep predictor analysis and their influence on possible overfitting of models will be considered. The overall result of using models largely depends on the result of this stage. We will analyze two packages offering new and original approaches to the selection of predictors.

By creating a sample program of visual design, we demonstrate how to design and construct classes in MQL5. The article is written for beginner programmers, who are working on MT5 applications. We propose a simple and easy grasping technology for creating classes, without the need to deeply immerse into the theory of object-oriented programming.

This is the second part of the article showing the development of a multi-symbol signal Expert Advisor for manual trading. We have already created the graphical interface. It is now time to connect it with the program's functionality.

In this article, we will learn a new tool from our series: we will learn how to design a trading system based on one of the most popular technical indicators Moving Average Convergence Divergence (MACD).

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.

There are many strategies that can be used to filter generated signals based on any strategy, even by using the moving average itself which is the subject of this article. So, the objective of this article is to share with you some of Moving Average Strategies and how to design an algorithmic trading system.

Let me offer you an overview and the program code of the mechanical trading system based on ideas of Stanislav Chuvashov. Triangle's construction is based on the intersection of two trend lines built by the upper and lower fractals.

The article contains descriptions of the new features available in the updated MQL5 Wizard. The modified architecture of signals allow creating trading robots based on the combination of various market patterns. The example contained in the article explains the procedure of interactive creation of an Expert Advisor.

The article considers three methods which can be used to increase the classification quality of bagging ensembles, and their efficiency is estimated. The effects of optimization of the ELM neural network hyperparameters and postprocessing parameters are evaluated.

If we thoroughly examine any complex trading system, we will see that it is based on a set of simple trading signals. Therefore, there is no need for novice developers to start writing complex algorithms immediately. This article provides an example of a trading system that uses semaphore indicators to perform deals.

This series of articles continues exploring deep neural networks (DNN), which are used in many application areas including trading. Here new dimensions of this theme will be explored along with testing of new methods and ideas using practical experiments. The first article of the series is dedicated to preparing data for DNN.

In this article we are going to show how to explore the Standard Library of Trading Strategy Classes and how to add Custom Strategies and Filters/Signals using the Patterns-and-Models logic of the MQL5 Wizard. In the end you will be able easily add your own strategies using MetaTrader 5 standard indicators, and MQL5 Wizard will create a clean and powerful code and fully functional Expert Advisor.

This article discusses creation of Expert Advisors using the UML graphical language, which is used for visual modeling of object-oriented software systems. The main advantage of this approach is the visualization of the modeling process. The article contains an example that shows modeling of the structure and properties of an Expert Advisor using the Software Ideas Modeler.

EA Tree is the first drag and drop MetaTrader MQL5 Expert Advisor builder. You can create complex MQL5 using a very easy to use graphical user interface. In EA Tree, Expert Advisors are created by connecting boxes together. Boxes may contain MQL5 functions, technical indicators, custom indicators, or values. Using the "tree of boxes", EA Tree generates the MQL5 code of the Expert Advisor.

This article provides a small example demonstrating the implementation of an Expert Advisor whose parameters can be controlled from the user panel. When changing the parameters "on the fly", the Expert Advisor writes the values obtained from the info panel to a file to further read them from the file and display accordingly on the panel. This article may be relevant to those who trade manually or in semi-automatic mode.

A simple, universal and reliable solution of information exchange between МetaТrader 4 Expert Advisor and the outside world. Suppliers and consumers of the information can be located on different computers, the connection is performed through the global IP addresses.

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.

Examined are the most frequent mistakes that lead the first-time programmers to creation of a "super-moneymaking" (when tested) trading systems. Exemplary experts that show fantastic results in tester, but result in losses during real trading are presented.

This article considers new capabilities of the darch package (v.0.12.0). It contains a description of training of a deep neural networks with different data types, different structure and training sequence. Training results are included.

This is the continuation of Another MQL5 OOP class article which showed you how to build a simple OO EA from scratch and gave you some tips on object-oriented programming. Today I am showing you the technical basics needed to develop an EA able to trade the news. My goal is to keep on giving you ideas about OOP and also cover a new topic in this series of articles, working with the file system.

In this article we will consider in detail the martingale system. We will review whether this system can be applied in trading and how to use it in order to minimize risks. The main disadvantage of this simple system is the probability of losing the entire deposit. This fact must be taken into account, if you decide to trade using the martingale technique.

We continue to build ensembles. This time, the bagging ensemble created earlier will be supplemented with a trainable combiner — a deep neural network. One neural network combines the 7 best ensemble outputs after pruning. The second one takes all 500 outputs of the ensemble as input, prunes and combines them. The neural networks will be built using the keras/TensorFlow package for Python. The features of the package will be briefly considered. Testing will be performed and the classification quality of bagging and stacking ensembles will be compared.

The article describes the process of developing and implementing a class for sending signals based on the moving channels. Each of the signal version is followed by a trading strategy with testing results. Classes of the Standard Library are used for creating derived classes.

The article describes the development and implementation of a class for sending signals based on pivots — reversal levels. This class is used to form a strategy applying the Standard Library. Improving the pivot strategy by adding filters is considered.

This article discusses the implementation of money management method for a cross-platform expert advisor. The money management classes are responsible for the calculation of the lot size to be used for the next trade to be entered by the expert advisor.

In this article, we will discuss how to develop a trading robot with minimum programming. Of course, MetaTrader 5 provides a high level of control over trading positions. However, using only the manual ability to place orders can be quite difficult and risky for less experienced users.

In the upcoming series of articles, I will demonstrate the development of self-adapting algorithms considering most market factors, as well as show how to systematize these situations, describe them in logic and take them into account in your trading activity. I will start with a very simple algorithm that will gradually acquire theory and evolve into a very complex project.

The article describes the application of text files for storing dynamic objects, arrays and other variables used as properties of Expert Advisors, indicators and scripts. The files serve as a convenient addition to the functionality of standard tools offered by MQL languages.

The article describes the rules of the trading system by Bill Williams, the procedure of application for a developed MQL5 module to search and mark patterns of this system on the chart, automated trading with found patterns, and also presents the results of testing on various trading instruments.

In this article, we will learn how to create Expert Advisors (EAs) working both in MetaTrader 4 and MetaTrader 5. To do this, we are going to develop an EA constructing order grids. Griders are EAs that place several limit orders above the current price and the same number of limit orders below it simultaneously.

This article centers around strategies that actively use pending orders, a metalanguage that can be created to formally describe such strategies and the use of a multi-purpose Expert Advisor whose operation is based on those descriptions

The article details TD points and TD lines discovered by Thomas DeMark. Their practical implementation is revealed. In addition to that, a process of writing three indicators and two Expert Advisors using the concepts of Thomas DeMark is demonstrated.

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.

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.

Do you wish to become a successful Freelance developer on MQL5? If the answer is yes, this article is right for you.

MQL5 gave a mass of innovations, including work with events of various types (timer events, trade events, custom events, etc.). Ability to handle events allows you to create completely new type of programs for automatic and semi-automatic trading. In this article we will consider trade events and write some code for the OnTrade() function, that will process the Trade event.