In the complex world of financial markets, the quest for innovative methods to analyze and interpret market behavior is ongoing. This thesis explores the potential of Supervised Contrastive Learning (SCL) as a novel approach to classifying stock market states, aiming to offer a more nuanced understanding of market dynamics. By constructing a dataset from NASDAQ 100 index prices, the study employs a deep neural network model to examine how SCL performs in comparison to traditional machine learning and deep learning techniques. This thesis attempts to contribute to the broader discourse on financial technology innovation, underlining the importance of continued exploration and experimentation in the development of financial analysis tools.\ The focus is on the process and methodology of applying SCL to financial time series analysis, emphasizing the exploratory nature of this research in seeking new pathways for financial analysis.