A SAX-GA approach to evolve investment strategies on financial markets based on pattern discovery techniques

This paper presents a new computational finance approach, combining a Symbolic Aggregate approXimation (SAX) technique together with an optimization kernel based on genetic algorithms (GA). The SAX representation is used to describe the financial time series, so that, relevant patterns can be efficiently identified. The evolutionary optimization kernel is here used to identify the most relevant patterns and generate investment rules. The proposed approach was tested using real data from S&P500. The achieved results show that the proposed approach outperforms both B&H and other state-of-the-art solutions.

► The proposed system, based on intelligent computation, combines pattern discovery techniques with evolutionary computation.
► The GA combined with the SAX method, creates a dynamic discovery process, which adapts to the variable patterns.
► The time span (2005–2010) selected for testing allowed the performance evaluation under distinct market conditions.
► The results show that the solution clearly beats B&H strategy during the entire period including the recent market crash.