Discussion of article "Practical Use of Kohonen Neural Networks in Algorithmic Trading. Part I. Tools"

[MetaQuotes]

New article Practical Use of Kohonen Neural Networks in Algorithmic Trading. Part I. Tools has been published:

The present article develops the idea of using Kohonen Maps in MetaTrader 5, covered in some previous publications. The improved and enhanced classes provide tools to solve application tasks.

In inherited source codes, no input data normalization is used. However, it is very important in case where different components (features) of input vectors have different ranges of values. And this is the case in the EAs' optimization results and in pooling the data of different indicators. As to the optimization results, we can see there that the values having the total profits of dozens of thousands rub shoulders with small values, such as the fractions of Sharp ratio or the one-digit values of the restitution factor.

You should not teach a Kohonen network using such different-scale data, since the network would practically consider the larger components only and ignore the smaller ones. You can see this in the image below obtained using the program that we are going to consider in a step-wise manner within this article and attach hereto in the end. The program allows generating random input vectors, in which three components are defined within the ranges of [0, 1000], [0, 1], and [-1, +1], respectively. A special input, UseNormalization, allows enabling/disabling normalization.

Let us have a look at the final structure of the Kohonen network in three planes relevant to three dimensions of the vectors. First, the network learning result without normalization.

Kohonen network learning result without normalizing the inputs

Now, the same with normalization.

Kohonen network learning result with normalizing the inputs

Author: Stanislav Korotky