Investigating technical trading strategy via an multi-objective evolutionary platform

Conventional approach in evolutionary technical trading strategies adopted the raw excess returns as the sole performance measure, without considering the associated risk involved. However, every individual has a different degree of risk averseness and thus different preferences between risk and returns. Acknowledging that these two factors are inherently conflicting in nature, this paper considers the multi-objective evolutionary optimization of technical trading strategies, which involves the development of trading rules that are able to yield high returns at minimal risk. Popular technical indicators used commonly in real-world practices are used as the building blocks for the strategies, which allow the examination of their trading characteristics and behaviors on the multi-objective evolutionary platform. While the evolved Pareto front accurately depicts the inherent tradeoff between risk and returns, the experimental results suggest that the positive correlation between the returns from the training data and test data, which is generally assumed in the single-objective approach of this optimization problem, does not necessarily hold in all cases.