Wow! eBook
Deep Learning in Quantitative Trading
Author(s): Zihao Zhang (Author), Stefan Zohren (Author)
- Publisher: Cambridge University Press
- Publication Date: October 30, 2025
- Language: English
- Print length: 184 pages
- ISBN-10: 1009707124
- ISBN-13: 9781009707121
Book Description
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.
Editorial Reviews
Book Description
Provides a comprehensive guide to deep learning in quantitative trading, merging foundational theory with hands-on applications.
