This paper examines the role of quantitative trading in finance and the potential applications of deep learning. Quantitative trading automates investment strategies using mathematical models, improving efficiency and accuracy. Deep learning optimizes these strategies by extracting complex features from large datasets, enhancing prediction accuracy. Despite its potential, deep learning in quantitative trading faces challenges such as overfitting, high computational costs, and poor interpretability. The paper reviews the theoretical foundations of both fields, discusses deep learning-based trading models, and explores methods for strategy optimization. It concludes by summarizing key findings, analyzing challenges, and suggesting future research directions.