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Coevolutionary particle swarm optimization for evolving trend reversal indicators

A competitive coevolutionary particle swarm optimization approach is proposed in this paper to train neural networks from zero knowledge to act as security trading agents. The coevolved neural networks are used for timing buying and short selling securities to maximize net profit and minimize risk over time. The proposed model attempts to identify security trend reversals using technical market indicators. No expert trading knowledge is presented to the model, only the technical market indicator data. A competitive fitness function is defined that allows the evaluation of each solution relative to other solutions, based on predefined performance metric objectives. The relative fitness function in this study considers net profit and the Sharpe ratio as a risk measure. For the purposes of this study, the stock prices of eight large market capitalisation companies were chosen. Two benchmarks were used to evaluate the discovered trading agents, consisting of a Bollinger Bands/Relative Strength Index rule-based strategy and the popular buy-and-hold strategy. The agents that were discovered from the proposed model outperformed both benchmarks by producing higher returns for in-sample and out-sample data at a low risk.