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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.

Publisher ‏ : ‎ Cambridge University Press
Publication date ‏ : ‎ October 30, 2025
Language ‏ : ‎ English
Print length ‏ : ‎ 184 pages
ISBN-10 ‏ : ‎ 1009707116
ISBN-13 ‏ : ‎ 978-1009707114
Item Weight ‏ : ‎ 9.8 ounces
Dimensions ‏ : ‎ 6 x 0.42 x 9 inches
Part of series ‏ : ‎ Elements in Quantitative Finance
Best Sellers Rank: #1,612,216 in Books (See Top 100 in Books) #689 in Business Finance #2,219 in Economics (Books) #2,403 in Finance (Books)
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