Stock trading model for multi-brands optimization based on Genetic Network Programming with control nodes
Yan Chen, Etsushi Ohkawa, Shingo Mabu, Kaoru Shimada, Kotaro Hirasawa · 2008
Many evolutionary computation methods applied to the financial field have been reported. A new evolutionary method named “Genetic Network Programming” (GNP) has been developed and applied to the stock market recently. The efficient Trading rules created by GNP has been confirmed in our previous research. In this paper a Multi-Brands optimization system based on Genetic Network Programming with control nodes is presented. This method makes use of the information from Technical Indices and Candlestick Chart. The proposed optimization system, consisting of technical analysis rules, are trained to generate trading advice. The experimental results on the Japanese stock market show that the proposed optimization system using GNP with control nodes method outperforms other traditional models in terms of both accuracy and efficiency.