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Deep Learning in Quantitative Trading

By Zihao Zhang

Deep Learning in Quantitative Trading

By Zihao Zhang

0 - Default Title
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.
Product details
Number of Pages:
184
Release Date:
2025-10-31
Publication Date:
2025-10-06
Publisher:
Cambridge University Press
Languages:
Original: English
ISBN10:
1009707124
ISBN13:
9781009707121
GPSR Manufacturer Reference:
[email protected]
Weight:
426 g
Height:
157 cm
Width:
235 cm
Thickness:
15 cm
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