An improved fuzzy rule-based automated trading agent

Héctor Allende-Cid, Enrique Canessa, Ariel Quezada, Héctor Allende

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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 Autoregressive) 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
Pages (from-to)135-142
Number of pages8
JournalStudies in Informatics and Control
Issue number2
StatePublished - 2011
Externally publishedYes


  • Automated trading agents
  • Fuzzy rule-based agents


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