Enhancing Financial Trading Strategies with Pattern Recognition: A Systematic Literature Review of Methods, Trend and Challenge

This study investigates how pattern recognition in financial markets can enhance trading strategies through a systematic literature review (SLR). The research focuses on the most frequently studied financial markets, commonly used pattern recognition methods, identified chart patterns, the integration of trading strategies with pattern recognition, and future challenges. This study is significant because many approaches to pattern recognition analysis are subjective and lack scientific evidence, leading to varied interpretations and uncertainties. The methodology includes formulating research questions, defining inclusion and exclusion criteria, conducting a comprehensive literature search, critically assessing included studies, data extraction and management, and result analysis and interpretation. This SLR covers 46 articles from databases such as Scopus, ACM Digital Library, IEEE Xplore, and Science Direct, published between 2015 and 2024, relevant to the topic. The findings show a dominant use of deep learning technologies, such as Convolutional Neural Networks (CNN), segmentation techniques, and fuzzy logic in recognizing patterns, particularly classic patterns like head and shoulders, triangles, double tops, and others, which contribute to enhancing trading strategies.