An improved fuzzy rule-based automated trading agent

Héctor Allende-Cid, Enrique Canessa, Ariel Quezada

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In this paper an improved Fuzzy Rule-Based Trading Agent is presented. The proposal consists in adding machine-learning-based methods to improve the overall performance of an automated agent that trades in futures markets. The modified Fuzzy Rule-Based Trading Agent has to decide whether to buy or sell goods, based on the spot and futures time series, gaining a profit from the price speculation. The proposal consists first in changing the membership functions of the fuzzy inference model (gaussian and sigmoidal, instead of triangular and trapezoidal). Then using the NFAR (Neuro-Fuzzy Autorregresive) model the relevant lags of the time series are detected, and finally a fuzzy inference system (Self-Organizing Neuro-Fuzzy Inference System) is implemented to aid the decision making process of the agent. Experimental results demonstrate that with the addition of these techniques, the improved agent considerably outperforms the original one.

Original languageEnglish
Title of host publicationProceedings - 29th International Conference of the Chilean Computer Science Society, SCCC 2010
PublisherIEEE Computer Society
Number of pages6
ISBN (Print)9780769544007
StatePublished - 2010

Publication series

NameProceedings - International Conference of the Chilean Computer Science Society, SCCC
ISSN (Print)1522-4902


  • Automated trading agent
  • Fuzzy rule-based agent


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