Optimization of technical indicators in real time with multiobjective evolutionary algorithms

Abstract

Technical analysis uses technical indicators to identify changes in market trend. These are composed by a set of parameters and rules, whose values try to determine the future movements of the assets. This paper addresses the optimization of these values depending on the current market, allowing better returns with less risk. The use of Multi-objective Evolutionary Algorithms (MOEAs) is proposed in this work to obtain the best parameter values in real time belonging to a collection of indicators that will help in the buying and selling of shares. Unlike other previous approaches, the necessity of repeating the parameters optimization process each time a new data enters the system is justified, searching for the best adjustment in every moment. This technique can greatly improve the results of Buy & Hold (B & H) strategy even operating daily. This statement will be demonstrated by comparing the results to those presented in the literature.