A genetic algorithm for the optimization of multi-threshold trading strategies in the directional changes paradigm

This paper proposes a novel genetic algorithm to optimize recommendations from multiple trading strategies derived from the Directional Changes (DC) paradigm. DC is an event-based approach that differs from the traditional physical time data, which employs fixed time intervals and uses a physical time scale. The DC method records price movements when specific events occur instead of using fixed intervals. The determination of these events relies on a threshold, which captures significant changes in price of a given asset. This work employs eight trading strategies that are developed based on directional changes. These strategies were profiled using varying values of thresholds to provide a comprehensive analysis of their effectiveness. In order to optimize and prioritize the conflicting recommendations given by the different trading strategies under different DC thresholds, we are proposing a novel genetic algorithm (GA). To analyze the GA’s trading performance, we utilize 200 stocks listed on the New York Stock Exchange. Our findings show that it can generate highly profitable trading strategies at very low risk levels. The GA is also able to statistically and significantly outperform other DC-based trading strategies, as well as 8 financial trading strategies that are based on technical indicators such as aroon, exponential moving average, and relative strength index, and also buy-and-hold. The proposed GA is also able to outperform the trading performance of 7 market indices, such as the Dow Jones Industrial Average, and the Standard & Poors (S&P) 500.