Neural Pattern Recognition with Self-organizing Maps for Efficient Processing of Forex Market Data Streams

The paper addresses the problem of using Japanese candlestick methodology to analyze stock or forex market data by neural nets. Self organizing maps are presented as tools for providing maps of known candlestick formations. They may be used to visualize these patterns, and as inputs for more complex trading decision systems. in that case their role is preprocessing, coding and pre-classification of price data. An example of a profitable system based on this method is presented. Simplicity and efficiency of training and network simulating algorithms is emphasized in the context of processing streams of market data.