Optimization of Trading Strategies using a Genetic Algorithm under the Directional Changes Paradigm with Multiple Thresholds

This paper explores the use of the Directional Changes (DC) paradigm for financial forecasting. DC is an event-based alternative to the traditional approach of time-series with fixed intervals. In the DC approach, price movements are recorded when specific events occur, rather than in fixed time intervals, while significant price changes are identified using a threshold. Here, we consider a more general model that allows multiple weighted thresholds, and propose three novel trading strategies built within the DC paradigm. To optimize the weights of the thresholds, we use a genetic algorithm and manage to find strategies that outperform previously known single-threshold strategies under the common efficiency metrics. Furthermore, our method manages to create profitable trading strategies that outperform some traditional ones, such as buy-and-hold, MACD, and RSI.