Affiliations: [a] Universitat de Girona, Spain
| [b] Adaptive and Bioinspired Systems Research Group, Universidad Complutense de Madrid, Spain
| [c]
IE Business School, Madrid, Spain
Correspondence:
[*]
Corresponding author. J. Ignacio Hidalgo, Universidad Complutense de Madrid. Tel.: +34 913947537; Fax: +34 913947527; E-mail: [email protected].
Abstract: This paper describes a hybrid automated trading system (ATS) based on grammatical evolution and microeconomic analysis. The proposed system takes advantage from the flexibility of grammars for introducing and testing novel characteristics. The ATS introduces the self-generation of new technical indicators and multi-strategies for stopping unforeseen losses. Additionally, this work copes with a novel optimization method combining multi-objective optimization with a grammatical evolution methodology. We implemented the ATS testing three different fitness functions under three mono-objective approaches and also two multi-objective ATSs. Experimental results test and compare them to the Buy and Hold strategy and a previous approach, beating both in returns and in number of positive operations. In particular, the multi-objective approach demonstrated returns up to 20% in very volatile periods, proving that the combination of fitness functions is beneficial for the ATS.
Keywords: Time series, stock market, technical analysis, grammatical evolution, multi-objective optimization, automatic trading systems
