Financial time series forecasting has long been an application area for soft computing techniques. The essential problem is to find a soft computing model that is compatible with technical analysis models used in stock markets. Backpropagation neural networks have often been used to emulate a technical analyst’s prediction. In this paper, two primary technical indicators, William’s %R and Moving Average Convergence Divergence (MACD), have been used for feature extraction from price data. We have focused on optimizing the separabilty of the resulting feature space. Levenberg-Marquardt variation of backpropagation algorithm has been found to be an effective decision engine to predict...