Moving Average-based Stock Trading Rules from Particle Swarm Optimization

Trading rules derived from technical analysis are valuable tools in making profits from the financial market. Among those trading rules, the moving average-based rule has been the most widely adopted choice by a large number of investors. Buy/sell signals are identified when curves of long/short averages cross each other. With an attempt to optimize the rule and maximize the trading profit, this paper propose the use of the particle swarm optimization algorithm to determine the appropriate long/short durations when calculating the averages. Trading signals are subsequently generated by the golden cross strategy. The best combination of long/short durations is determined by comparing the profits that can be made among alternative durations. Real-world indices, covering three years approximately, from several established and emerging stock markets are used to verify the effectiveness of the proposed method.