02256nam a2200217 4500 20260325150934.0 9781009707114 English 332.6 ZHA/D Colon Classification Zhang, Zihao 12522 Deep Learning in Quantitative Trading /By Zihao Zhang and Stefan Zohren 1 UK: Cambridge University Press, 2025. 174p. 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 Artificial intelligence/ Financial applications- Deep learning (Machine learning) /Economic aspects 12523 Zohren, Stefan 12524 ddc BK 756977 756977 0 0 ddc 0 0 NFIC ECN ECN PROC 2026-02-23 MBC/0763/2025,13/02/2026 2222.00 0 332.6 ZHA/D ECN16986 2026-02-24 10:27:20 2026-02-23 BK