9781009707121 - deep learning in quantitative trading di zhang, zihao; zohren, stefan (15 risultati)
Deep Learning In Quantitative Trading
Zhang, Zihao (university Of Oxford);zohren, Stefan (university Of Oxford)
Lingua: Inglese
Editore: Cambridge University Press, 2025
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Deep Learning In Quantitative Trading
Zhang, Zihao (university Of Oxford);zohren, Stefan (university Of Oxford)
Lingua: Inglese
Editore: Cambridge University Press, 2025
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Deep Learning In Quantitative Trading
Zhang, Zihao (university Of Oxford);zohren, Stefan (university Of Oxford)
Lingua: Inglese
Editore: Cambridge University Press, 2025
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Deep Learning In Quantitative Trading
Zhang, Zihao (university Of Oxford);zohren, Stefan (university Of Oxford)
Lingua: Inglese
Editore: Cambridge University Press, 2025
- Rilegato
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Deep Learning In Quantitative Trading
Zhang, Zihao (University Of Oxford) Zohren, Stefan (University Of Oxford)
Lingua: Inglese
Editore: Cambridge University Press, 2025
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 75 pages. 6.00x0.50x9.00 inches. In Stock.
Lingua: Inglese
Editore: Cambridge University Press, 2025
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 supervis…ed 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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Editore: Cambridge University Press, Cambridge, 2025
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Hardcover. Condizione: new. Hardcover. 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 networ…k 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.
Lingua: Inglese
Editore: Cambridge University Press, 2025
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Lingua: Inglese
Editore: Cambridge University Press, 2025
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Hardcover. Condizione: new. Hardcover. 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 networ…k 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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2025
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Hardcover. Condizione: new. Hardcover. 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 networ…k 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Lingua: Inglese
Editore: Cambridge University Press, 2025
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Buch. Condizione: Neu. Deep Learning in Quantitative Trading | Zihao Zhang (u. a.) | Buch | Englisch | 2025 | Cambridge University Press | EAN 9781009707121 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
