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Beschreibung
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. 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.
Inhaltsverzeichnis
Preface; 1. Introduction; Part I. Foundations: 2. Fundamentals of Financial Time-Series; 3. Supervised Learning and Canonical Networks; 4. The Model Training Workflow; Part II. Applications: 5. Enhancing Classical Quantitative Trading Strategies with Deep Learning; 6. Deep Learning for Risk Management and Portfolio Optimization; 7. Applications to Market Microstructure and High-Frequency Data; 8. Conclusions; List of Acronyms; Appendix A: Different Asset Classes; Appendix B: Access to Market Data; Appendix C: Investment Performance Metrics; Appendix D: Code Scripts.
Details
Erscheinungsjahr: 2025
Fachbereich: Rechtsratgeber
Genre: Importe, Recht
Produktart: Nachschlagewerke
Rubrik: Recht & Wirtschaft
Medium: Taschenbuch
ISBN-13: 9781009707114
ISBN-10: 1009707116
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Zhang, Zihao
Zohren, Stefan
Hersteller: Cambridge University Press
Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße: 229 x 152 x 11 mm
Von/Mit: Zihao Zhang (u. a.)
Erscheinungsdatum: 06.10.2025
Gewicht: 0,276 kg
Artikel-ID: 134146786

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