Trading with optimized uptrend and downtrend pattern templates using a genetic algorithm kernel

Author

Parracho, Paulo ; Neves, Rui ; Horta, Nuno

Author_Institution

Inst. de Telecomun., Inst. Super. Tecnico, Lisbon, Portugal

fYear

2011

fDate

5-8 June 2011

Firstpage

1895

Lastpage

1901

Abstract

This paper describes a new computational finance approach. This approach combines pattern recognition techniques with an evolutionary computation kernel applied to financial markets time series in order to optimize trading strategies. Moreover, for pattern matching a template-based approach is used in order to describe the desired trading patterns. The parameters for the pattern templates, as well as, for the decision making rules are optimized using a genetic algorithm kernel. The approach was tested considering actual data series and presents a robust profitable trading strategy which clearly beats the market, S&P 500 index, reducing the investment risk significantly.

Keywords

decision making; genetic algorithms; investment; pattern matching; profitability; risk management; stock markets; time series; computational finance approach; decision making rules; evolutionary computation kernel; financial markets time series; genetic algorithm kernel; investment risk; optimized downtrend pattern template; optimized uptrend pattern templates; pattern matching; pattern recognition techniques; robust profitable trading strategy; Genetic algorithms; Indexes; Kernel; Optimization; Profitability; Testing; Training; Financial Markets; Genetic Algorithms; Optimization; Pattern Templates;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation (CEC), 2011 IEEE Congress on

Conference_Location

New Orleans, LA

ISSN

Pending

Print_ISBN

978-1-4244-7834-7

Type

conf

DOI

10.1109/CEC.2011.5949846

Filename

5949846