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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
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Deep Learning in Quantitative Trading (Paperback)
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Cambridge University Press, Cambridge, 2025
ISBN 10: 1009707116
ISBN 13: 9781009707114
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Paperback. Condizione: 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. Codice articolo 9781009707114
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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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Cambridge University Press, 2025
ISBN 10: 1009707116
ISBN 13: 9781009707114
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Deep Learning in Quantitative Trading
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Cambridge University Press, GB, 2025
ISBN 10: 1009707116
ISBN 13: 9781009707114
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Da: Rarewaves USA, OSWEGO, IL, U.S.A.
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Paperback. Condizione: 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. Codice articolo LU-9781009707114
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Deep Learning in Quantitative Trading
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Cambridge University Press, GB, 2025
ISBN 10: 1009707116
ISBN 13: 9781009707114
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Da: Rarewaves.com USA, London, LONDO, Regno Unito
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Paperback. Condizione: 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. Codice articolo LU-9781009707114
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Deep Learning In Quantitative Trading
Editore:
Cambridge University Press, 2025
ISBN 10: 1009707116
ISBN 13: 9781009707114
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Paperback. Condizione: Brand New. 75 pages. 6.00x0.39x9.00 inches. In Stock. This item is printed on demand. Codice articolo __1009707116
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Deep Learning in Quantitative Trading (Paperback or Softback)
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Cambridge University Press 10/30/2025, 2025
ISBN 10: 1009707116
ISBN 13: 9781009707114
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Paperback or Softback. Condizione: New. Deep Learning in Quantitative Trading. Book. Codice articolo BBS-9781009707114
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Deep Learning in Quantitative Trading (Elements in Quantitative Finance)
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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Condizione: New. 2025. paperback. . . . . . Codice articolo V9781009707114
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Deep Learning In Quantitative Trading
Editore:
Cambridge University Press, 2025
ISBN 10: 1009707116
ISBN 13: 9781009707114
Nuovo
Paperback
Da: Revaluation Books, Exeter, Regno Unito
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Paperback. Condizione: Brand New. 75 pages. 6.00x0.39x9.00 inches. In Stock. Codice articolo x-1009707116
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