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 adopted little concern. In this paper, to explore the potential power of digital trading, we present a new MATLAB tool based on genetic algorithms, which 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 a 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. Key-Words: financial markets; prediction; genetic algorithms; non-linear technical rules