History of MQL5.community development

One of the most interesting aspects of Self-Organizing Feature Maps (Kohonen maps) is that they learn to classify data without supervision. In its basic form it produces a similarity map of input data (clustering). The SOM maps can be used for classification and visualizing of high-dimensional data. In this article we will consider several simple applications of Kohonen maps.

In this article, we continue studying the principles of working with Internet using HTTP requests and exchange of information with server. It describes new functions of the CMqlNet class, methods of sending information from forms and sending of files using POST requests as well as authorization on websites under your login using Cookies.

If an indicator uses values of many other indicators for its calculations, it consumes a lot of memory. The article describes several methods of decreasing the memory consumption when using auxiliary indicators. Saved memory allows increasing the number of simultaneously used currency pairs, indicators and strategies in the client terminal. It increases the reliability of trade portfolio. Such a simple care about technical resources of your computer can turn into money resources at your deposit.

This article will describe advanced adaptive indicators and their implementation in MQL5: Adaptive Cyber Cycle, Adaptive Center of Gravity and Adaptive RVI. All indicators were originally presented in "Cybernetic Analysis for Stocks and Futures" by John F. Ehlers.