Improving trading systems using the RSI financial indicator and neural networks

Authors:
Alejandro Rodríguez-González;Fernando Guldrís-Iglesias;Ricardo Colomo-Palacios;Juan Miguel Gomez-Berbis;Enrique Jimenez-Domingo;Giner Alor-Hernandez;Rubén Posada-Gomez;Guillermo Cortes-Robles
Affiliations:
Universidad Carlos III de Madrid, Leganés, Madrid, Spain;Universidad Carlos III de Madrid, Leganés, Madrid, Spain;Universidad Carlos III de Madrid, Leganés, Madrid, Spain;Universidad Carlos III de Madrid, Leganés, Madrid, Spain;Universidad Carlos III de Madrid, Leganés, Madrid, Spain;Division of Research and Postgraduate Studies, Instituto Tecnológico de Orizaba, México;Division of Research and Postgraduate Studies, Instituto Tecnológico de Orizaba, México;Division of Research and Postgraduate Studies, Instituto Tecnológico de Orizaba, México
Venue:
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Year:
2010

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Abstract

Trading and Stock Behavioral Analysis Systems require efficient Artificial Intelligence techniques for analyzing Large Financial Datasets (LFD) and have become in the current economic landscape a significant challenge for multi-disciplinary research. Particularly, Trading-oriented Decision Support Systems based on the Chartist or Technical Analysis Relative Strength Indicator (RSI) have been published and used worldwide. However, its combination with Neural Networks as a branch of computational intelligence which can outperform previous results remain a relevant approach which has not deserved enough attention. In this paper, we present the Chartist Analysis Platform for Trading (CAST, in short) platform, a proof-of-concept architecture and implementation of a Trading Decision Support System based on the RSI and Feed-Forward Neural Networks (FFNN). CAST provides a set of relatively more accurate financial decisions yielded by the combination of Artificial Intelligence techniques to the RSI calculation and a more precise and improved upshot obtained from feed-forward algorithms application to stock value datasets.