Table of Contents
Journal of Artificial Evolution and Applications
Volume 2008, Article ID 184286, 17 pages
Research Article

Evolving Neural Networks for Static Single-Position Automated Trading

Information Technology Department, University of Milan, Via Bramante 65, 26013 Crema (CR), Italy

Received 30 July 2007; Revised 30 November 2007; Accepted 16 January 2008

Academic Editor: Anthony Brabazon

Copyright © 2008 Antonia Azzini and Andrea G. B. Tettamanzi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.