Abstract
In this paper we outline initial concepts for an immune inspired algorithm to evaluate price time series data. The proposed solution evolves a short term pool of trackers dynamically through a process of proliferation and mutation, with each member attempting to map to trends in price movements. Successful trackers feed into a long term memory pool that can generalise across repeating trend patterns. Tests are performed to examine the algorithm’s ability to successfully identify trends in a small data set. The influence of the long term memory pool is then examined. We find the algorithm is able to identify price trends presented successfully and efficiently.
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Wilson, W.O., Birkin, P., Aickelin, U. (2006). Price Trackers Inspired by Immune Memory. In: Bersini, H., Carneiro, J. (eds) Artificial Immune Systems. ICARIS 2006. Lecture Notes in Computer Science, vol 4163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11823940_28
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DOI: https://doi.org/10.1007/11823940_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37749-8
Online ISBN: 978-3-540-37751-1
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