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.

Dettagli sul prodotto

• Autori: Stefan Zohren, Zihao Zhang, Zihao (University of Oxford) Zohren Zhang
• Editore: Cambridge University Press ELT
• Contenuto: Libro
• Forma del prodotto: Tascabile
• Data pubblicazione: 31.10.2025
• Categoria: Scienze sociali, diritto, economia > Economia > Pubblicità, marketing; Guide e manuali > Diritto, professione, finanze > Diritto di famiglia
• EAN: 9781009707114
• ISBN: 978-1-0-0970711-4
• Numero di pagine: 184
• Serie: Elements in Quantitative Finance
• Categorie: BUSINESS & ECONOMICS / Finance / General; Information technology: general issues; Investment & securities; Investment and securities; Applied computing