Deep Learning in Quantitative Trading

Name: Deep Learning in Quantitative Trading
Price: 145.57 AUD
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ISBN: 9781009707121

Zihao Zhang (University of Oxford), Stefan Zohren (University of Oxford)

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Hardback

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English

Cambridge University Press
30 October 2025

Investment & securities; Computing: general

Series: Elements in Quantitative Finance

Summary
Details

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.

By: Zihao Zhang (University of Oxford), Stefan Zohren (University of Oxford)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Weight: 416g
ISBN: 9781009707121
ISBN 10: 1009707124
Series: Elements in Quantitative Finance
Pages: 184
Publication Date: 30 October 2025
Audience: General/trade , ELT Advanced
Format: Hardback
Publisher's Status: Active