Multi-objective optimization for algorithmic trading in the Vietnamese stock market

Abstract

This study aims to optimize algorithmic trading strategies using the relative strength index (RSI) and the moving average convergence divergence (MACD) indicators in the Vietnamese stock market. An automated trading system is constructed to optimize indicator parameters using multi-objective particle swarm optimization (PSO) over three objective functions: total return, win rate, and number of trades. The system employs simultaneous optimization of parameters and signal aggregation for developing the optimal selection strategy. Based on daily Vietnam index data from 2018 to 2024, the results show that the PSO method surpasses the differential evolution (DE) method in both returns and execution time. Additionally, the optimal selection strategy achieves superior performance compared to benchmark strategies. It also demonstrates the ability to adapt to the preferences of traders by selecting appropriate indicators. Traders can use the MACD indicator to seek higher profits, while the RSI indicator is more suitable for minimizing transaction costs in a volatile market.