History of MQL5.community development

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.

This article is a continuation in a series that look at Category Theory implementation in MQL5. In here we examine how Graph-Theory could be integrated with monoids and other data structures when developing a close-out strategy to a trading system.

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.

Here we will describe only one of the aspects of machine learning - activation functions. In artificial neural networks, a neuron activation function calculates an output signal value based on the values of an input signal or a set of input signals. We will delve into the inner workings of the process.

This article continues the series on category theory implementation in MQL5. Here we look at monoid-groups as a means normalising monoid sets making them more comparable across a wider span of monoid sets and data types..