Finding Optimizations for Trading Using Search Economy Algorithms

The development of artificial intelligence has led to the rapid growth of various industries. In addition to driving innovative applications, it has also brought significant changes to daily life. This transformation has accelerated the evolution of digital finance, enabling financial services to integrate AI and develop new applications. Among these services, smart investment has become one of the most popular. Due to the uncertainty of financial markets, achieving stable profits remains a challenging task. In this article, we propose a novel trading strategy that uses Search Economy Algorithms to analyze stock market trends. Our approach does not focus on a single stock; instead, it enables industry-wide investment decisions through our stock selection mechanism and fitness function, combined with the trading strategies provided by the Search Economy Algorithms. Compared with existing methodologies, our experimental environment is more complex. We benchmark our approach against ETF-0050 and demonstrate its effectiveness by evaluating capital growth rates and risk under specific parameters.