Evolving Neural Networks for Static Single-Position Automated Trading. (14th April 2008)

• Record Type: Journal Article
• Authors: Azzini, Antonia; Tettamanzi, Andrea G. B.
• Other Names: Brabazon Anthony Academic Editor.
• Is Part Of: Journal of artificial evolution and applications. Volume 2008(2008)
• Publication Date: 2008-04-14
• Subjects: Evolutionary programming (Computer science) -- Periodicals; Evolutionary programming (Computer science); Periodicals; Electronic journals; 006.3823
• DOI: 10.1155/2008/184286
• Languages: English
• ISSNs: 1687-6229

Abstract

Abstract : This paper presents an approach to single-position, intraday automated trading based on a neurogenetic algorithm. An artificial neural network is evolved to provide trading signals to a simple automated trading agent. The neural network uses open, high, low, and close quotes of the selected financial instrument from the previous day, as well as a selection of the most popular technical indicators, to decide whether to take a single long or short position at market open. The position is then closed as soon as a given profit target is met or at market close. Experimental results indicate that, despite its simplicity, both in terms of input data and in terms of trading strategy, such an approach to automated trading may yield significant returns.