Abstract
This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387–411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.
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References
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Kocian, V., Volna, E., Janosek, M., Kotyrba, M.: Optimization of training sets for Hebbian-learning-based classifiers. In: Matoušek, R. (ed.) Proceedings of the 17th International Conference on Soft Computing, Mendel 2011, Brno, Czech Republic, pp. 185–190 (2011). ISBN 978–80–214–4302–0, ISSN 1803–3814
Moore, M., Roche, M.: Less of a puzzle: a new look at the forward forex market. J. Int. Econ. 58, 387–411 (2002)
XTB: X-Trader Brokers. http://xtb.cz. Accessed 3 September 2011
Acknowledgements
The research described here has been financially supported by University of Ostrava grant SGS2/PřF/2012. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors.
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Volná, E., Janošek, M., Kocian, V., Kotyrba, M. (2013). Nonlinear Time Series Analysis via Neural Networks. In: Stavrinides, S., Banerjee, S., Caglar, S., Ozer, M. (eds) Chaos and Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33914-1_58
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DOI: https://doi.org/10.1007/978-3-642-33914-1_58
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