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

Part I: Foundations. Fundamentals of financial time-series
Supervised learning and canonical networks
The model training workflow
Part II: Applications. Enhancing classical quantitative trading strategies
Deep learning for risk management and portfolio optimization
Applications to market microstructure and high-frequency data
Conclusions
Acronyms
Appendix A: Different asset classes
Appendix B: Access to market data
Appendix C: Investment performance metrics
Appendix D: Code scripts