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Deep Learning in Quantitative Trading
Elements in Quantitative Finance
09707091First published online: September 2025
Zihao Zhang
University of Oxford
Stefan Zohren
University of Oxford
Author for correspondence: Zihao Zhang, zhangzihao@hotmail.co.uk
Abstract: 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.
Keywords: deep learning, machine learning, reinforcement learning,
time-series, neural networks, quantitative trading, portfolio optimization,
market microstructure, momentum trading, volatility scalings
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