Multi-objective optimisation and genetic programming for trading by combining directional changes and technical indicators

Directional changes (DC) have been shown to form an effective approach in algorithmic trading by converting fixed time series into event-based series and focusing on key events. Previous work has focused on forecasting the inflection point in the market and proposing new indicators under the DC framework, with just a handful of papers concerned with training and using DC indicators through machine learning. Earlier research has shown that genetic programming (GP) combining DC and physical time indicators could achieve positive returns with low risk. However, the fitness function used in that work is simply a risk-adjusted return. In this paper, we investigate whether a multi-objective optimisation approach could improve the performance of GP-based strategies in the market. We evaluate the cumulative return, risk, and rate of return of the proposed approach under 110 datasets from 10 different markets. Furthermore, we compare the proposed strategy against GP-based single objective optimisation (SOO) and buy-and-hold strategies. Our results show that the proposed approach significantly improves the cumulative return com- pared to SOO, from 14.29% to 62.04%, while also outperforming the buy-and-hold strategy.