Deep Learning in Quantitative Trading

Zihao Zhang, Stefan Zohren (Autoren)

Buch | Hardcover

184 Seiten

2025
Cambridge University Press (Verlag)
978-1-009-70712-1 (ISBN)

Artikel merken

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.

Preface; 1. Introduction; Part I. Foundations: 2. Fundamentals of Financial Time-Series; 3. Supervised Learning and Canonical Networks; 4. The Model Training Workflow; Part II. Applications: 5. Enhancing Classical Quantitative Trading Strategies with Deep Learning; 6. Deep Learning for Risk Management and Portfolio Optimization; 7. Applications to Market Microstructure and High-Frequency Data; 8. Conclusions; List of Acronyms; Appendix A: Different Asset Classes; Appendix B: Access to Market Data; Appendix C: Investment Performance Metrics; Appendix D: Code Scripts.

Erscheinungsdatum	21.10.2025
Reihe/Serie	Elements in Quantitative Finance
Zusatzinfo	Worked examples or Exercises
Verlagsort	Cambridge
Sprache	englisch
Gewicht	416 g
Themenwelt	Mathematik / Informatik ► Informatik
Themenwelt	Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung
ISBN-10	1-009-70712-4 / 1009707124
ISBN-13	978-1-009-70712-1 / 9781009707121
Zustand	Neuware