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Deep Learning in Quantitative Trading (Elements in Quantitative Finance) - Softcover
Book 7 of 7: Elements in Quantitative Finance
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Provides a comprehensive guide to deep learning in quantitative trading, merging foundational theory with hands-on applications.
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Deep Learning In Quantitative Trading
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Deep Learning in Quantitative Trading (Paperback or Softback)
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Deep Learning in Quantitative Trading (Elements in Quantitative Finance)
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Deep Learning in Quantitative Trading
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Paperback. Condition: New. 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. Seller Inventory # LU-9781009707114
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Deep Learning in Quantitative Trading
Published by
Cambridge University Press, GB, 2025
ISBN 10: 1009707116
ISBN 13: 9781009707114
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Paperback. Condition: New. 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. Seller Inventory # LU-9781009707114
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Deep Learning In Quantitative Trading
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Deep Learning in Quantitative Trading (Elements in Quantitative Finance)
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Deep Learning in Quantitative Trading (Paperback)
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Paperback. Condition: new. Paperback. 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. This Element provides a comprehensive guide to deep learning in quantitative trading, merging foundational theory with hands-on applications. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781009707114
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Deep Learning In Quantitative Trading
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Deep Learning in Quantitative Trading (Elements in Quantitative Finance)
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