Paper Info
Title
A technique for the optimization of the parameters of technical indicators with Multi-Objective Evolutionary Algorithms
Abstract
Technical indicators (TIs) are used to interpret stock market and to predict market trends. The main difficulty in the use of TIs lies in deciding which their optimal parameter values are in each moment, since constant optimal values do not seem to exist. In this work, the use of Multi-Objective Evolutionary Algorithms (MOEAs) is proposed to obtain the best values of the parameters in order to help to buy and sell shares. Those parameters are applied in real time and belong to a collection of indicators. Unlike other previous approaches, the necessity of repeating the parameter optimization process each time a new data enters the system is justified, searching for the best adjustment of the parameters (and hence the TIs) in every moment. The Moving Averages Convergence-Divergence (MACD) indicator and the Relative Strength Index (RSI) oscillator have been chosen as TIs, so the MOEAs will provide the best parameters to use them on investment decisions. Experiments compare up to nine different configurations with the Buy & Hold strategy (B & H). The obtained results show that the Multi-Objective technique proposed here can greatly improve the results of the B & H strategy even operating daily. This statement is also demonstrated by comparing the results to those previously presented in the literature.
Year
DOI
Venue
2012
10.1109/CEC.2012.6256584
Evolutionary Computation
Keywords
Field
DocType
evolutionary computation,investment,optimisation,stock markets,B&H strategy,MACD indicator,MOEA,RSI oscillator,TI,buy & hold strategy,investment decisions,market trend prediction,moving average convergence-divergence indicator,multiobjective evolutionary algorithms,parameter optimization process,relative strength index oscillator,stock market,technical indicators,Evolutionary Algorithms,Financial Trading,Technical Indicators
Time series,Mathematical optimization,MACD,Evolutionary algorithm,Computer science,Relative strength index,Evolutionary computation,Minification,Artificial intelligence,Stock market,Moving average,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4673-1508-1
2
0.40
References 
Authors
0
6
Name
Order
Citations
PageRank
Diego J. Bodas-Sagi1374.40
Francisco J. Soltero2364.04
Jose Ignacio Hidalgo313522.61
Pablo Fernández420.40
Francisco V. Fernández523440.82
Bodas Sagi, D.J.620.40