Improving N calculation of the RSI financial indicator using neural networks

Trading and Stock Behavioral Analysis Systems require efficient Artificial Intelligence techniques for analyzing large financial datasets 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 evolutionary computing 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 N value calculation 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 N calculation for RSI and a more precise and improved upshot obtained from feed-forward algorithms application to stock value datasets.