Articles on trading system automation in MQL5

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.

In this article, I will tell you how to successfully trade by merging a very well-known strategy and a neural network. It will be about the Thomas DeMark's Sequential strategy with the use of an artificial intelligence system. Only the first part of the strategy will be applied, using the Setup and Intersection signals.

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.

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.

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 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.

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.

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.

The aim of this article is to investigate the possibilities of trade automation and the analysis, on the basis of some ideas from a book by James Hyerczyk "Pattern, Price & Time: Using Gann Theory in Trading Systems" in the form of indicators and Expert Advisor. Without claiming to be exhaustive, here we investigate only the Model - the first part of the Gann theory.

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.

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.

There are lots of chart types that provide information on the current market situation. Many of them, such as Point and Figure chart, are the legacy of the remote past. The article describes an example of Point and Figure charting using a real time indicator.

This article suggests the ways of improving manual trading strategy by applying fuzzy set theory. As an example we have provided a step-by-step description of the strategy search and the selection of its parameters, followed by fuzzy logic application to blur overly formal criteria for the market entry. This way, after strategy modification we obtain flexible conditions for opening a position that has a reasonable reaction to a market situation.

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.

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 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 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.

In the previous articles, we started creating a large cross-platform library simplifying the development of programs for MetaTrader 5 and MetaTrader 4 platforms. In the fourth part, we tested tracking trading events on the account. In this article, we will develop trading event classes and place them to the event collections. From there, they will be sent to the base object of the Engine library and the control program chart.

The article deals with programmed tracking of events in the МetaТrader 4 Client Terminal, such as opening/closing/modifying orders, and is targeted at a user who has basic skills in working with the terminal and in programming in MQL 4.

This article dwells on implementation algorithm of simplest trading systems. The article will be useful for beginning traders and EA writers.

This article concludes the series devoted to trading currency pair baskets. Here we test the remaining pattern and discuss applying the entire method in real trading. Market entries and exits, searching for patterns and analyzing them, complex use of combined indicators are 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.

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.

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 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.

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 original basic article has not lost its relevance and thus if you are interested in this topic, be sure to read the first article. However much time has passed since then, so the current Visual Studio 2017 features an updated interface. The MetaTrader 5 platform has also acquired new features. The article provides a description of dll project development stages, as well as DLL setup and interaction with MetaTrader 5 tools.

Following up our previous article on the currency baskets trading principles, here we are going to analyze the patterns traders can detect. We will also consider the advantages and the drawbacks of each pattern and provide some recommendations on their use. The indicators based on Williams' oscillator will be used as analysis tools.

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 considers working with account events for tracking important changes in account properties affecting the automated trading. We have already implemented some functionality for tracking account events in the previous article when developing the account object collection.

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.

This article's objective is to study profitability of algorithms with different entries into trades and exits using trailing stop. Entry types to be used are random entry and reverse entry. Stop orders to be used are trailing stop and trailing take. The article demonstrates money-making algorithms with a profitability of about 30% per annum.

This article presents Pivot Mean Oscillator (PMO), an implementation of the cumulative moving average (CMA) as a trading indicator for the MetaTrader platforms. In particular, we first introduce Pivot Mean (PM) as a normalization index for timeseries that computes the fraction between any data point and the CMA. We then build PMO as the difference between the moving averages applied to two PM signals. Some preliminary experiments carried out on the EURUSD symbol to test the efficacy of the proposed indicator are also reported, leaving ample space for further considerations and improvements.