Combining rules between PIPs and SAX to identify patterns in financial markets

This paper describes a new pattern discovery approach based on the combination among rules between Perceptually Important Points (PIPs) and the Symbolic Aggregate approximation (SAX) representation optimised by Genetic Algorithm (GA). The rules and SAX are used to represent the financial time series in order to efficiently identify patterns. The GA is used to generate investment rules and find optimal solutions. We decided to call this new approach Symbolic Important Rules (SIR). The proposed approach was tested with real data from the S&P500 index and all the results obtained outperform the Buy&Hold strategy. Three different case studies are presented. With this approach it was possible to obtain in the period 2011-2014 a total return of 76.7%, which outperformed the Buy&Hold strategy (61.9%).