Deep Learning in Quantitative Trading

This Element provides a comprehensive guide to deep learning in quantitative trading, merging foundational theory with hands-on applications.It is organized into two parts. The first part introduces the fundamentals of financial time-series and supervised learning, exploring various network architectures, from feedforward to state-of-the-art.To ensure robustness and mitigate overfitting on complex real-world data, a complete workflow is presented, from initial data analysis to cross-validation techniques tailored to financial data.Building on this, the second part applies deep learning methods to a range of financial tasks.The authors demonstrate how deep learning models can enhance both time-series and cross-sectional momentum trading strategies, generate predictive signals, and be formulated as an end-to-end framework for portfolio optimization.Applications include a mixture of data from daily data to high-frequency microstructure data for a variety of asset classes.Throughout, they include illustrative code examples and provide a dedicated GitHub repository with detailed implementations.