Articles on trading system automation in MQL5

This article, the 16th in our series, continues with a look at Functors and how they can be implemented using artificial neural networks. We depart from our approach so far in the series, that has involved forecasting volatility and try to implement a custom signal class for setting position entry and exit signals.

Functions are critical things in any programming language, it helps developers apply the concept of (DRY) which means do not repeat yourself, and many other benefits. In this article, you will find much more information about functions and how we can create our own functions in MQL5 with simple applications that can be used or called in any system you have to enrich your trading system without complicating things.

This article which is part of a series that follows Category Theory implementation of Graphs in MQL5, delves in Orders. We examine how concepts of Order-Theory can support monoid sets in informing trade decisions by considering two major ordering types.

As developers, we need to learn how to create and develop software that can be reusable and flexible without duplicated code especially if we have different objects with different behaviors. This can be smoothly done by using object-oriented programming techniques and principles. In this article, we will present the basics of MQL5 Object-Oriented programming to understand how we can use principles and practices of this critical topic in our software.

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.

To complete this series of articles on automation, we will continue discussing the topic of the previous article. We will see how everything will fit together, making the EA run like clockwork.

In this article we continue the development of the CArima class for building ARIMA models by adding intuitive methods that enable forecasting.

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.

The multilayer perceptron is an evolution of the simple perceptron which can solve non-linear separable problems. Together with the backpropagation algorithm, this neural network can be effectively trained. In Part 3 of the Multilayer Perceptron and Backpropagation series, we'll see how to integrate this technique into the Strategy Tester. This integration will allow the use of complex data analysis aimed at making better decisions to optimize your trading strategies. In this article, we will discuss the advantages and problems of this technique.

The article considers the grid trading approach based on stop pending orders and implemented in an MQL5 Expert Advisor on the Moscow Exchange (MOEX). When trading in the market, one of the simplest strategies is a grid of orders designed to "catch" the market price.

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.

In this article, we will use the rebuy algorithm for a deeper understanding of the efficiency of trading systems and start working on the general principles of improving trading efficiency using mathematics and logic, as well as apply the most non-standard methods of increasing efficiency in terms of using absolutely any trading system.

If you think automated systems are simple, then you probably don't fully understand what it takes to create them. In this article, we will talk about the problem that kills a lot of Expert Advisors. The indiscriminate triggering of orders is a possible solution to this problem.

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.

Automation means nothing if you cannot control its schedule. No worker can be efficient working 24 hours a day. However, many believe that an automated system should operate 24 hours a day. But it is always good to have means to set a working time range for the EA. In this article, we will consider how to properly set such a time range.

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.