Abstract
Automated Trading is the activity of buying and selling financial instruments for the purpose of gaining a profit, through the use of automated trading rules. This work presents an evolutionary approach for the design and optimization of artificial neural networks to the discovery of profitable automated trading rules. 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. © 2008 Springer-Verlag Berlin Heidelberg.
Cite
CITATION STYLE
Azzini, A., & Tettamanzi, A. G. B. (2008). Evolutionary single-position automated trading. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4974 LNCS, pp. 62–72). https://doi.org/10.1007/978-3-540-78761-7_7
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