Optimization of Multi-Threshold Trading Strategies in the Directional Changes Paradigm

The objective of this paper is to investigate the potential of using the Directional Changes (DC) paradigm for financial forecasting. 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 enhance the performance of both the thresholds and trading strategies, a genetic algorithm was utilized to optimize their respective weights. This approach provided a more comprehensive analysis and enriched the information available to our trading strategy. To analyze our model in our experiment, we utilized 200 stocks listed on the New York Stock Exchange. Furthermore, the method proposed in this study successfully generated profitable trading strategies that exhibited superior performance when compared to specific DC-based benchmarks commonly utilized in the existing literature. Additionally, the proposed method outperformed conventional strategies based on technical indicators such as ADX, Aroon, CCI, EMA, MACD, RSI, and WilliamR.