History of MQL5.community development

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..

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.

My main goal in this article is to provide you with a simple and accurate account of the steps that will help you become a top signal provider on MQL5.com. Drawing upon my knowledge and experience, I will explain what it takes to become a successful signal provider, including how to find, test, and optimize a good strategy. Additionally, I will provide tips on publishing your signal, writing a compelling description and effectively promoting and managing it.