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Improving trading saystems using the RSI financial indicator and neural networks.

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11 pages

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.
Proceedings of: 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services (PKAW 2010), 20 August-3 September 2010, Daegu (Korea)
Springer
Byeong-Ho Kang et al. (eds.), Knowledge management and acquisition for smart systems and services. 11th International Workshop, PKAW 2010, Daegu, Korea, August 20 - September 3, 2010 (pp. 27-37). Proceedings. Berlin: Springer, 2010
This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the EUREKA project SITIO (TSI-020400-2009-148), SONAR2 (TSI-020100-2008-665 and GO2 (TSI-020400-2009-127). Furthermore, this work is supported by the General Council of Superior Technological Education of Mexico (DGEST). Additionally, this work is sponsored by the National Council of Science and Technology (CONACYT) and the Public Education Secretary (SEP) through PROMEP.
Knowledge management and acquisition for smart systems and services. 11th International Workshop, PKAW 2010, Daegu, Korea, August 20 - September 3, 2010. Proceedings
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Improving Trading Systems Using the RSI Financial Indicator and Neural Networks
1 1 Alejandro RodríguezGonzález , Fernando GuldrísIglesias , 1 1 1 Ricardo ColomoPalacios , Juan Miguel GomezBerbis , Enrique JimenezDomingo , 2 2 2 Giner AlorHernandez , Rubén PosadaGomez , and Guillermo CortesRobles
1 Universidad Carlos III de Madrid, Av. Universidad 30, Leganés, 28918, Madrid, Spain {alejandro.rodriguez,fernando.guldris,ricardo.colomo, juanmiguel.gomez}@uc3m.es 2 Division of Research and Postgraduate Studies, Instituto Tecnológico de Orizaba, México {galor,rposada,gcortes}@itorizaba.edu.mx
Abstract.Trading and Stock Behavioral Analysis Systems require efficient Ar tificial 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 outper form 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 imple mentation 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 Intelli gence techniques to the RSI calculation and a more precise and improved up shot obtained from feedforward algorithms application to stock value datasets.
Keywords:Neural Networks, RSI Financial Indicator.
1 Introduction
There has been growing interest in Trading Decision Support Systems in recent years. Forecasting the price movements in stock markets has been a major challenge for common investors, businesses, brokers and speculators. The stock market is consid ered as a high complex and dynamic system with noisy, nonstationary and chaotic data series [1], and hence, difficult to forecast [2]. However, despite its volatibility, it is not entirely random [3], instead, it is nonlinear and dynamic [4] or highly compli cated and volatile [5]. Stock movement is affected by the mixture of two types of factors [6]: determinant (e.g. gradual strength change between buying side and selling side) and random (e.g. emergent affairs or daily operation variations).