Articles with examples of trading robots developed in MQL5

In this article, I will create the chart object class (of a single trading instrument chart) and improve the collection class of MQL5 signal objects so that each signal object stored in the collection updates all its parameters when updating the list.

In this article we look at the possibility of creating an application for the display of RSS feeds. The article will show how aspects of the Standard Library can be used to create interactive programs for MetaTrader 5.

Until today, the CTable was the most advanced type of tables among all presented in the library. This table is assembled from edit boxes of the OBJ_EDIT type, and its further development becomes problematic. Therefore, in terms of maximum capabilities, it is better to develop rendered tables of the CCanvasTable type even at the current development stage of the library. Its current version is completely lifeless, but starting from this article, we will try to fix the situation.

Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode. In the previous article, we started to develop an order system that we will use in our automated EA. However, we have created only one of the necessary functions.

Mean reversion is a type of contrarian trading where the trader expects the price to return to some form of equilibrium which is generally measured by a mean or another central tendency statistic.

Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode.

The article considers creation of the custom indicator object for the use in EAs. Let’s slightly improve library classes and add methods to get data from indicator objects in EAs.

In this article, I will continue the development of the chart object class. I will add the list of chart window objects featuring the lists of available indicators.

This article describes an example of "Snake" game programming. In MQL5, the game programming became possible primarily due to event handling features. The object-oriented programming greatly simplifies this process. In this article, you will learn the event processing features, the examples of use of the Standard MQL5 Library classes and details of periodic function calls.

In this article, I will create the functionality for tracking some chart object events — adding/removing symbol charts and chart subwindows, as well as adding/removing/changing indicators in chart windows.

Whatever trading strategy you use, there will always be a question of what parameters to choose to ensure future profits. This article gives an example of an Expert Advisor with a possibility to optimize multiple symbol parameters at the same time. This method is intended to reduce the effect of overfitting parameters and handle situations where data from a single symbol are not enough for the study.

There is currently a fair number of means for a comfortable remote monitoring of a trading account: mobile terminals, push notifications, working with ICQ. But it all requires Internet connection. This article describes the process of creating an Expert Advisor that will allow you to stay in touch with your trading terminal even when mobile Internet is not available, through calls and text messaging.

Since a program may use different symbols in its work, a separate list should be created for each of them. In this article, I will combine such lists into a tick data collection. In fact, this will be a regular list based on the class of dynamic array of pointers to instances of CObject class and its descendants of the Standard library.

The article deals with the outlook of using MQL4 for more productive work at FOREX markets.

In this article, we will put into practice all the knowledge from this series. We will finally build a 100% automated and functional system. But before that, we still have to learn one last detail.

Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode. At the end of the previous article, I suggested that it would be appropriate to allow manual use of the EA, at least for a while.

We continue to study reinforcement learning methods. In the previous article, we got acquainted with the Deep Q-Learning method. In this method, the model is trained to predict the upcoming reward depending on the action taken in a particular situation. Then, an action is performed in accordance with the policy and the expected reward. But it is not always possible to approximate the Q-function. Sometimes its approximation does not generate the desired result. In such cases, approximation methods are applied not to utility functions, but to a direct policy (strategy) of actions. One of such methods is Policy Gradient.

Although the creation of an automated EA is not a very difficult task, however, many mistakes can be made without the necessary knowledge. In this article, we will look at how to build the first level of automation, which consists in creating a trigger to activate breakeven and a trailing stop level.

This article considers aspects of design and development of custom chart events system in the MQL5 environment. An example of an approach to the events classification can also be found here, as well as a program code for a class of events and a class of custom events handler.

Do you know what a flowchart is? Can you use it? Do you think flowcharts are for beginners? I suggest that we proceed to this new article and learn how to work with flowcharts.

MQL5 provides programmers with a very complete set of functions and object-oriented API thanks to which they can do everything they want within the MetaTrader environment. However, Web Technology is an extremely versatile tool nowadays that may come to the rescue in some situations when you need to do something very specific, want to marvel your customers with something different or simply you do not have enough time to master a specific part of MT5 Standard Library. Today's exercise walks you through a practical example about how you can manage your development time at the same time as you also create an amazing tech cocktail.

The project involves using Python for deep learning-based forecasting in financial markets. We will explore the intricacies of testing the model's performance using key metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2) and we will learn how to wrap everything into an executable. We will also make a ONNX model file with its EA.

The article considers creation of classes of descendant objects of base abstract indicator. Such objects will provide access to features of creating indicator EAs, collecting and getting data value statistics of various indicators and prices. Also, create indicator object collection from which getting access to properties and data of each indicator created in the program will be possible.

In this article, we will see how to use the event handling system to quickly and efficiently process issues related to the order system. With this system the EA will work faster, so that it will not have to constantly search for the required data.