Fuzzy candlesticks forecasting using pattern recognition for stock markets

Abstract

This paper presents a prediction system based on fuzzy modeling of Japanese candlesticks. The prediction is performed using the pattern recognition methodology and applying a lazy and nonparametric classification technique, k-Nearest Neighbours (k-NN). The Japanese candlestick chart summarizes the trading period of a commodity with only 4 parameters (open, high, low and close). The main idea of the decision system implemented in this article is to predict with accuracy, based on this vague information from previous sessions, the performance of future sessions. Therefore, investors could have valuable information about the next session and set their investment strategies.