A machine learning platform for stock investment recommendation systems

Abstract

This research aims to create an investment recommendation system based on the extraction of buy/sell signals from the results of technical analysis and prediction. In this case it focuses on the Spanish continuous market. As part of this research, different techniques have been studied for data extraction and analysis. After having reviewed the work related to the initial idea of the research, it is shown the development carried out, together with the data extraction and the machine learning algorithms for prediction used. The calculation of technical analysis metrics is also included. The development of a visualization platform has been proposed for high-level interaction between the user and the recommendation system. The result is a platform that provides a user interface for both data visualization, analysis, prediction and investment recommendation. The platform’s objective is not only to be usable and intuitive, but also to enable any user, whether an expert or not in the stock market, to abstract their own conclusions from the data and evaluate the information analyzed by the system.