Abstract
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.
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 language | English |
|---|---|
| Title of host publication | 2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr 2012) |
| Subtitle of host publication | New York City, New York, USA, 29-30 March 2012 |
| Place of Publication | New York |
| Publisher | IEEE |
| Pages | 40-47 |
| Number of pages | 8 |
| ISBN (Electronic) | 978-1-4673-1801-3 |
| ISBN (Print) | 978-1-4673-1802-0 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | 2012 IEEE Computational Intelligence for Financial Engineering and Economics (CIFEr 2012) - New York, United States Duration: 29 Mar 2012 → 30 Mar 2012 |
Conference
| Conference | 2012 IEEE Computational Intelligence for Financial Engineering and Economics (CIFEr 2012) |
|---|---|
| Abbreviated title | CIFEr 2012 |
| Country/Territory | United States |
| City | New York |
| Period | 29/03/12 → 30/03/12 |