Abstract
The foreign exchange (Forex) market has over 5 trillion USD turnover per day. In addition, it is one of the most volatile and dynamic markets in the world. Market conditions continue to change every second. This paper explores the performance of different variants of dynamic Particle swarm optimization (PSO) (charge and quantum PSO) for the Forex market. Profit is maximized by optimizing technical indicators. Performance of charge and quantum PSO are measured against the standard PSO and the time variant PSO. A sliding window mechanism is also implemented to adapt to the changing nature of the dataset. The results show that the quantum PSO performed better on net profit returns and was followed by the charge PSO, while the standard PSO performed poorly.
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