Fr. 31.90

Deep Learning in Quantitative Trading

English · Paperback / Softback

Will be released 31.10.2025

Description

Read more










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.

List of contents










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.

Product details

Authors Zihao Zhang, Stefan Zohren
Publisher Cambridge Academic
 
Languages English
Product format Paperback / Softback
Release 31.10.2025
 
EAN 9781009707114
ISBN 978-1-009-70711-4
Illustrations Worked examples or Exercises
Series Elements in Quantitative Finance
Subjects Social sciences, law, business > Business > Advertising, marketing

BUSINESS & ECONOMICS / Finance / General, Information technology: general issues, Investment & securities, Investment and securities, Applied computing

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.