Deep learning for finance

• Verfasst von: Kaabar, Sofien [VerfasserIn]
• Titel: Deep learning for finance
• Titelzusatz: creating machine & deep learning models for trading in Python
• Mitwirkende: Chamberlain, Mike [ErzählerIn]
• Verf.angabe: Sofien Kaabar
• Ausgabe: [First edition].
• Verlagsort: [Place of publication not identified]
• Verlag: Ascent Audio
• Jahr: 2024
• Umfang: 1 online resource (1 sound file (10 hr., 15 min.))
• Fussnoten: Online resource; title from title details screen (O’Reilly, viewed April 29, 2024)
• ISBN: 978-1-6637-3557-7
• ISBN: 1-6637-3557-3
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Valeurs mobilières ; Commerce électronique
• Sach-SW: Apprentissage profond
• Sach-SW: Python (Langage de programmation)
• Sach-SW: Audiobooks
• Sach-SW: Livres audio
• K10plus-PPN: 1890485454
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4533724043
• Lokale URL Inst.: Zum Volltext

Abstract

Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar-financial author, trading consultant, and institutional market strategist-introduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization. This book will help you to understand and create machine learning and deep learning models; explore the details behind reinforcement learning and see how it's used in time series; understand how to interpret performance evaluation metrics; examine technical analysis and learn how it works in financial markets; create technical indicators in Python and combine them with ML models for optimization; and evaluate the models' profitability and predictability to understand their limitations and potential.