Improving technical trading systems by using a new MATLAB-based genetic algorithm procedure

Recent studies in financial markets suggest that technical analysis can be a very useful tool in predicting the trend. Trading systems are widely used for market assessment; however, parameter optimization of these systems has attracted little interest. In this paper, to explore the potential power of digital trading, we present a new MATLAB tool based on genetic algorithms; the tool specializes in parameter optimization of technical rules. It uses the power of genetic algorithms to generate fast and efficient solutions in real trading terms. Our tool was tested extensively on historical data of a UBS fund investing in emerging stock markets through our specific technical system. Results show that our proposed GATradeTool outperforms commonly used, non-adaptive, software tools with respect to the stability of return and time saving over the whole sample period. However, we provided evidence of a possible population size effect in quality of solutions.