A learning adaptive Bollinger band system

Matthew Richard Butler, Dimitar Lubomirov Kazakov

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper introduces a novel forecasting algorithm
that is a blend of micro and macro modelling perspectives
when using Artificial Intelligence (AI) techniques. The micro
component concerns the fine-tuning of technical indicators with
population based optimization algorithms. This entails learning
a set of parameters that optimize some economically desirable
fitness function as to create a dynamic signal processor which
adapts to changing market environments. The macro component
concerns combining the heterogeneous set of signals produced
from a population of optimized technical indicators. The combined
signal is derived from a Learning Classifier System (LCS)
framework that combines population based optimization and
reinforcement learning (RL). This research is motivated by two
factors, that of non-stationarity and cyclical profitability (as
implied by the adaptive market hypothesis [10]). These two
properties are not necessarily in contradiction but they do
highlight the need for adaptation and creation of new models,
while synchronously being able to consult others which were
previously effective. The results demonstrate that the proposed
system is effective at combining the signals into a coherent
profitable trading system but that the performance of the system
is bounded by the quality of the solutions in the population.
Original languageEnglish
Title of host publication2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr 2012)
Subtitle of host publicationNew York City, New York, USA, 29-30 March 2012
Place of PublicationNew York
Number of pages8
ISBN (Electronic)978-1-4673-1801-3
ISBN (Print)978-1-4673-1802-0
Publication statusPublished - 2012
Event2012 IEEE Computational Intelligence for Financial Engineering and Economics (CIFEr 2012) - New York, United States
Duration: 29 Mar 201230 Mar 2012


Conference2012 IEEE Computational Intelligence for Financial Engineering and Economics (CIFEr 2012)
Abbreviated titleCIFEr 2012
Country/TerritoryUnited States
CityNew York

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