Articles on the MQL5 programming and use of trading robots

Automation of trading strategies involving graphical patterns requires the ability to search for extreme points on the charts for further processing and interpretation. Existing tools do not always provide such an ability. The algorithms described in the article allow finding all extreme points on charts. The tools discussed here are equally efficient both during trends and flat movements. The obtained results are not strongly affected by a selected timeframe and are only defined by a specified scale.

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.

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.

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.

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

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.

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.

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 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.

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.

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.

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 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.

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 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.

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.

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 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.

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.

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.

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.

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.

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 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

In this article, we develop a Nick Rypock Trailing Reverse (NRTR) trading system in MQL5 that uses channel indicators for reversal signals, enabling trend-following entries with hedging support for buys and sells. We incorporate risk management features like auto lot sizing based on equity or balance, fixed or dynamic stop-loss and take-profit levels using ATR multipliers, and position limits.

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.

Functions and code snippets that simplify and clarify the handling of time, broker offset, and the changes to summer or winter time. Accurate timing may be a crucial element in trading. At the current hour, is the stock exchange in London or New York already open or not yet open, when does the trading time for Forex trading start and end? For a trader who trades manually and live, this is not a big problem.

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.

The main advantage of trading robots lies in the ability to work 24 hours a day on a remote VPS server. But sometimes it is necessary to intervene in their work, while there may be no direct access to the server. Is it possible to manage EAs remotely? The article proposes one of the options for controlling EAs via external commands.

In this article, we will continue our series about designing a trading system using the most popular indicators and we will talk about the average directional index (ADX) indicator. We will learn this indicator in detail to understand it well and we will learn how we to use it through a simple strategy. By learning something deeply we can get more insights and we can use it better.

For a long time multi-currency analysis and multi-currency trading has been of interest to people. The opportunity to implement a full fledged multi-currency regime became possible only with the public release of MetaTrader 5 and the MQL5 programming language. In this article we propose a way to analyze and process all incoming ticks for several symbols. As an illustration, let's consider a multi-currency RSI indicator of the USDx dollar index.

This class library can be added to an MetaTrader 5 Expert Advisor to enable it to be written with an order-centric approach broadly similar to MetaTrader 4, in comparison to the position-based approach of MetaTrader 5. It does this by keeping track of virtual orders at the MetaTrader 5 client terminal, while maintaining a protective broker stop for each position for disaster protection.

In this article, we build a Liquidity Sweep on Break of Structure (BoS) system in MQL5 that detects swing highs/lows over a user-defined length, labels them as HH/HL/LH/LL to identify BOS (HH in uptrend or LL in downtrend), and spots liquidity sweeps when price wicks beyond the swing but closes back inside on a bullish/bearish candle.