Generating ternary stock trading signals using fuzzy genetic network programming

In this paper, an expert system is developed using fuzzy genetic network programming with reinforcement learning (GNP-RL) in order to generate stock trading signals based on technical indices of the stock prices. In order to increase the accuracy and reliability of results, we applied Wavelet Transform to eliminate noises and irregularities in prices. Since choosing the most appropriate wavelet base is an important decision, the Energy to Shannon Entropy Ratio, as an objective method, is used in order to address this concern. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. The proposed model has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in testing time period shows that the developed system has more favorable performance in comparison with the simple GNP-RL with binary signals and Buy and Hold strategy.