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    • Publisher:
      Cambridge University Press
      Publication date:
      03 October 2025
      30 October 2025
      ISBN:
      9781009707091
      9781009707121
      9781009707114
      Dimensions:
      (229 x 152 mm)
      Weight & Pages:
      0.416kg, 184 Pages
      Dimensions:
      (229 x 152 mm)
      Weight & Pages:
      0.307kg, 184 Pages
    • Series:
      Elements in Quantitative Finance
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    Series:
    Elements in Quantitative Finance

    Book 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.

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