Particle swarm intelligence tunning of fuzzy geometric protoforms for price patterns recognition and stock trading

A novel approach for detecting patterns in price time series is shown. The proposed system for identifying consolidation phases is based on fuzzy geometric protoforms and classification trees. Promising results of the empirical studies prove that the suggested fuzzy geometric protoforms are very useful for identifying patterns in graphical visualizations of data. Moreover, the architecture of the system enables successful incorporation of genetic optimization what enables capturing various data sets structure and unstable conditions on financial markets.

► We propose a novel approach for detecting patterns in price time series.
► Our system identifies the consolidation phases is based using fuzzy geometric protoforms and classification trees.
► The empirical studies prove that fuzzy geometric protoforms are very useful for identifying patterns in data.
► The architecture of the system enables capturing various data sets structure and unstable conditions on financial markets.