  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.
