An approach to reconstruction of data set via supervised and unsupervised learning

Abstract

Stock markets are nowadays producing vast portions of data. In order to keep up with the pace also the technology stack of research institutions needs to adapt. However, the portion of data is not the only reason. With the boom in the area of Artificial Intelligence one can seize the advantage of insights hidden in the data. In order to develop trading strategies, describe the behavior present in the market, one can grasp the concepts of supervised and unsupervised learning. In this paper we describe the basic idea behind these types of learning and propose a framework which ingests the limit order book data and prepares them onto a form where the learning is easily applicable.