Thursday , December 13 2018

An Improved Fuzzy Rule-Based Automated Trading Agent

Héctor ALLENDE-CID
Universidad Técnica Federico Santa María, Departamento de Informática
Avda. España 1680, Valparaíso, 2390123, Chile

Enrique CANESSA
Universidad Adolfo Ibañez, Facultad de Ingeniería y Ciencias
Balmaceda 1625, Viña del Mar, 3132386, Chile

Ariel QUEZADA
Universidad Adolfo Ibañez, Facultad de Sicología
Balmaceda 1625, Viña del Mar, 3132386, Chile

Héctor ALLENDE
Universidad Técnica Federico Santa María, Departamento de Informática
Avda. España 1680, Valparaíso, 2390123, Chile

Abstract: 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.

Keywords: Automated Trading Agents, Fuzzy Rule-based Agents.

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CITE THIS PAPER AS:
Héctor ALLENDE-CID, Enrique CANESSA, Ariel QUEZADA, Héctor ALLENDE, An Improved Fuzzy Rule-Based Automated Trading Agent, Studies in Informatics and Control, ISSN 1220-1766, vol. 20 (2), pp. 135-142, 2011.