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

作者：Canelas Antonio; Neves Rui; Horta Nuno^*

来源：Expert Systems with Applications, 2013, 40(5): 1579-1590.

DOI：10.1016/j.eswa.2012.09.002

• Pattern discovery
• Frequent patterns
• Pattern recognition
• Financial markets
• Time series
• Genetic algorithm
• SAX representation

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.

• 出版日期2013-4