Articles with examples of trading robots developed in MQL5

We will present a modified version of the Expert Advisor from the previous article "MQL5 Cookbook: Position Properties on the Custom Info Panel". Some of the issues we will address include getting data from bars, checking for new bar events on the current symbol, including a trade class of the Standard Library to a file, creating a function to search for trading signals and a function for executing trading operations, as well as determining trade events in the OnTrade() function.

We continue to add new features to the rendered table: data sorting, managing the number of columns and rows, setting the table cell types to place controls into them.

A new version of the graphics library for creating scientific charts (the CGraphic class) has been presented recently. This update of the developed library for creating graphical interfaces will introduce a version with a new control for creating charts. Now it is even easier to visualize data of different types.

In the previous chapter, we wrote classes for creating vertical and horizontal scrollbars. In this chapter, we will implement them. We will write a class for creating the list view element, a compound part of which will be a vertical scrollbar.

Volatility based breakout system identifies market ranges, then trades when price breaks above or below those levels, filtered by volatility measures such as ATR. This approach helps capture strong directional moves.

In this article, we develop a Trendline Breakout System in MQL5 that identifies support and resistance trendlines using swing points, validated by R-squared goodness of fit and angle constraints, to automate breakout trades. Our plan is to detect swing highs and lows within a specified lookback period, construct trendlines with a minimum number of touch points, and validate them using R-squared metrics and angle constraints to ensure reliability.

This article provides programmatic definition of one of the movement continuation models. The main idea is defining two waves — the main and the correction one. For extreme points, I apply fractals as well as "potential" fractals - extreme points that have not yet formed as fractals.

In this update of the library, the Table control (the CTable class) will be supplemented with new options. The lineup of controls in the table cells is expanded, this time adding text edit boxes and combo boxes. As an addition, this update also introduces the ability to resize the window of an MQL application during its runtime.

In the previous articles of this series, we have seen two reinforced learning algorithms. Each of them has its own advantages and disadvantages. As often happens in such cases, next comes the idea to combine both methods into an algorithm, using the best of the two. This would compensate for the shortcomings of each of them. One of such methods will be discussed in this article.

We have earlier discussed some types of neural network implementations. In the considered networks, the same operations are repeated for each neuron. A logical further step is to utilize multithreaded computing capabilities provided by modern technology in an effort to speed up the neural network learning process. One of the possible implementations is described in this article.

In this article, I will show the criteria to be used when selecting a system or a signal for investing your funds, as well as describe the optimal approach to the development of trading systems and highlight the importance of this matter in Forex trading.

In the new version of the library, all controls will be drawn on separate graphical objects of the OBJ_BITMAP_LABEL type. We will also continue to describe the optimization of code: changes in the core classes of the library will be discussed.

Efficient extraction and integration of long-term dependencies and short-term features remain an important task in time series analysis. Their proper understanding and integration are necessary to create accurate and reliable predictive models.

The article guides in demonstrating an automated algorithm based on EMA Crossovers for MetaTrader 5. Detailed information on all aspects of demonstrating an Expert Advisor in MQL5 and testing it in MetaTrader 5 - from analyzing price range behaviors to risk management.

The optimization process can require significant resources of your computer or even of the MQL5 Cloud Network test agents. This article comprises some simple ideas that I use for work facilitation and improvement of the MetaTrader 5 Strategy Tester. I got these ideas from the documentation, forum and articles.

Today's article shows intermediate MQL5 programmers how they can get more profit from their linear trading systems (Fixed Lot) by easily implementing the so-called technique of exponentiation. This is because the resulting equity curve growth is then geometric, or exponential, taking the form of a parabola. Specifically, we will implement a practical MQL5 variant of the Fixed Fractional position sizing developed by Ralph Vince.

This time we are going to create a multi-currency Expert Advisor with a trading algorithm based on work with the pending orders Buy Stop and Sell Stop. This article considers the following matters: trading in a specified time range, placing/modifying/deleting pending orders, checking if the last position was closed at Take Profit or Stop Loss and control of the deals history for each symbol.

The previous articles contain the implementation of the classes for creating constituent parts of the main menu. Now, it is time to take a close look at the event handlers in the principle base classes and in the classes of the created controls. We will also pay special attention to managing the state of the chart depending on the location of the mouse cursor.

Today we will learn how to link an MetaTrader 5 terminal with Twitter so that you can tweet your EAs' trading signals. We are developing a Social Decision Support System in PHP based on a RESTful web service. This idea comes from a particular conception of automatic trading called computer-assisted trading. We want the cognitive abilities of human traders to filter those trading signals which otherwise would be automatically placed on the market by the Expert Advisors.

This article explains the idea of multi-mode trading robot programming in MQL5. Every mode is implemented with the object-oriented approach. Instances of both mode classes hierarchy and classes for testing are provided. Multi-mode programming of trading robots is supposed to take into account all peculiarities of every operational mode of an EA written in MQL5. Functions and enumeration are created for identifying the mode.

In order to develop an expert to participate in Automated Trading Championship 2010, let's use a template of ready expert advisor. Even novice MQL5 programmer will be capable of this task, because for your strategies the basic classes, functions, templates are already developed. It's enough to write a minimal amount of code to implement your trading idea.

Create a trading journal using MetaTrader and Google Sheets! You will learn how to sync your trading data via HTTP POST and retrieve it using HTTP requests. In the end, You have a trading journal that will help you keep track of your trades effectively and efficiently.

In the previous chapters of the eighth part of the series, our library has been reinforced by several classes for developing mouse pointers, calendars and tree views. The current article deals with the file navigator control that can also be used as part of an MQL application graphical interface.

In the article, I will start developing the functionality for working with the Depth of Market. I will also create the class of the Depth of Market abstract order object and its descendants.

An automated system will not be successful without proper security. However, security will not be ensured without a good understanding of certain things. In this article, we will explore why achieving maximum security in automated systems is such a challenge.