This paper performs a comprehensive analysis of Vix Index data with Heikin Ashi Transformation of stock index Neural Network Learning. It has been demonstrated that Heikin Ashi Transformation can improve the learning effect of Neural Network and the effect can also be filter out if volume weights are also considered. This paper introduces another improvement beside using volume-weighted data. Instead volatility index is used as an input and its effect for neural network learning process is analyzed.