Multi-objective optimization case study for algorithmic trading strategies in foreign exchange markets

摘要

This research focuses on a case study of two approaches for producing algorithmic trading rules in foreign exchange markets using genetic algorithms: multi-objective optimization and spontaneous optimization of design variables. First, while conventional trading systems explore a single-objective function such as the Sharpe ratio or only profit, multi-objective optimization allows us to manage the essential trade-off among profit, standard deviation, and maximum-drop. Our approach improves present trading systems, thus avoiding the possibility of substantial losses and, in addition, it can increase investment profits. Second, design parameters such as trading volume, the amount of historical data, and trading gateways of technical indicators are continuously optimized in real time, in contrast, to traditional trading algorithms that have mostly relied on a few prefixed values for the design variables in an optimization problem. Incorporating these research approaches into a genetic algorithm methodology will improve the robustness of results.