Simultaneous multi-objective optimization method for trading indicators

This paper proposes a novel framework for optimizing trading indicators using a multi-objective Particle Swarm Optimization approach. By simultaneously optimizing multiple technical indicators, the method overcomes the limitations of single-objective optimization and complex strategies, resulting in a more robust trading approach. Experiments on VN30-Index daily data demonstrate that the optimized strategy outperforms benchmark and buy-and-hold strategies in terms of returns and Sharpe ratios. Our findings prove that the multi-objective Particle Swarm Optimization method efficiently balances the complexity to combine various technical indicators in a way that keeps the logic of the strategy simple. The technique not only reduces the risks of relying on one indicator but also reduces behavioral influences in the stock selection process. Furthermore, our study adds to the literature a simple and effective method that helps traders identify profitable investment opportunities in different market scenarios.