This element provides a comprehensive guide to deep learning in quantitative trading, combining fundamental theory with practical applications. It is organized into two parts.

The first part introduces the fundamentals of financial time series and supervised learning, exploring various neural network architectures, from feedforward to the most modern ones. To ensure robustness and reduce overfitting on complex real-world data, a complete workflow is presented, from initial data analysis to cross-validation techniques tailored for financial data.

The second part applies deep learning methods to a range of financial tasks. The authors demonstrate how deep learning models can enhance trend forecasting strategies and cross-sectional strategies, generate predictive signals, and be formulated as a comprehensive process for portfolio optimization. Applications include a mix of data from daily data to high-frequency microstructure data across various asset classes.

Throughout, code examples are included for better understanding, along with a dedicated repository on GitHub with detailed implementations.

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