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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 |