This article has been made to show you how to use neural networks, via FANN2MQL, using an easy example: teaching a simple pattern to the neuralnetwork, and testing it to see if it can recognize patterns it has never seen.
I will not list all of the new possibilities and features of the new terminal and language. They are numerous, and some novelties are worth the discussion in a separate article. Also there is no code here, written with object-oriented programming, it is a too serous topic to be simply mentioned in a context as additional advantages for developers. In this article we will consider the indicators, their structure, drawing, types and their programming details, as compared to MQL4. I hope that this article will be useful both for beginners and experienced developers, maybe some of them will find something new.
Most Java coders will be familiar with the auto-generated documentation that can be created with JavaDocs. The idea is to add comments into the code in a semi-structured way that can then be extracted into an easy to navigate help file. The C++ world also has a number of documentation auto-generators, with Microsoft's SandCastle and Doxygen being two leaders. The article describes the use of Doxygen to create HTML help file from structured comments in MQL5 code. The experiment worked very well and I believe the help documentation that Doxygen produces from MQL5 code will add a great deal of value.
In this article you will find main tasks when decorating indicators, their solution and automation.