Creating a Customized Dataset for Financial Pattern Recognition in Deep Learning

In the evolving domain of financial markets, the precision of pattern recognition significantly influences investment decisions. This paper presents a novel approach to creating and evaluating custom datasets tailored for deep reinforcement learning (DRL) applications in stock chart pattern recognition. Addressing the gap in standardized dataset preparation, we meticulously develop datasets, focusing on popular patterns like Double Tops and Double Bottoms. Utilizing line charts, we emphasize the importance of dataset quality over quantity, a paradigm shift from conventional candlestick reliance. Our methodology entails a multi-faceted process involving image collection, annotation, and segmentation, ensuring robustness and diversity. We explore various image modes and enhancements to ascertain optimal dataset characteristics, resulting in superior model performance. Through rigorous experimentation, we demonstrate that grayscale datasets, particularly without contrast enhancements, yield the highest accuracy in pattern recognition tasks. Our findings challenge existing norms in financial data analysis and propose new standards for dataset creation. The resulting publicly available dataset, a first of its kind, offers a valuable resource for future research in financial pattern analysis using deep learning. This study not only advances the field of financial analytics but also opens avenues for applying similar methodologies in other domains requiring precise pattern recognition.