Deep Learning in Quantitative Trading

Zihao Zhang, Stefan Zohren

ISBN 10: 1009707116 / ISBN 13: 9781009707114

Published by Cambridge University Press, GB, 2025

About this Item

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.

Seller Inventory # LU-9781009707114

Bibliographic Details

• Title: Deep Learning in Quantitative Trading
• Publisher: Cambridge University Press, GB
• Publication Date: 2025
• Language: English
• ISBN 10: 1009707116
• ISBN 13: 9781009707114
• Binding: Paperback
• Condition: New
• Dimensions: 15.24W x 1.07H x 22.86D centimeters 15.24 centimeters width by 1.07 centimeters height by 22.86 centimeters depth
• Weight: 307 grams

About this title

Synopsis

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.