Price Trackers Inspired by Immune Memory
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.
|Date of creation:||Apr 2010|
|Date of revision:|
|Publication status:||Published in Proceedings of the 5th International Conference on Artificial Immune Systems (ICARIS2006), Lecture Notes in Computer Science 4163, p362-375, 2006|
|Contact details of provider:|| Web page: http://arxiv.org/|
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