Deep Learning in Hilbert Spaces: New Frontiers in Algorithmic Trading - Brossura

Flux, Jamie

Brossura

ISBN 13: 9798340304148

Casa editrice: Independently published, 2024

Riassunto

This book delves into the fusion of advanced mathematical concepts and cutting-edge deep learning techniques to transform algorithmic trading. By extending deep learning models into Hilbert spaces—complete infinite-dimensional spaces endowed with inner products—the book presents a novel framework for handling the complex, high-dimensional data inherent in financial markets. This approach opens new avenues for modeling and predicting market behaviors with greater accuracy and computational efficiency.

Main Topics:

Foundations of Hilbert Spaces in Financial Modeling: This section introduces the core principles of Hilbert spaces and their applicability to finance, explaining how infinite-dimensional spaces can model complex financial phenomena more effectively than traditional finite-dimensional methods.

Extending Deep Learning Architectures to Hilbert Spaces: Exploring how standard deep learning models like neural networks can be generalized to operate within Hilbert spaces, enabling the processing of functional data and continuous-time signals crucial for high-frequency trading.

Kernel Methods and Reproducing Kernel Hilbert Spaces (RKHS): Discussing the role of RKHS in enhancing machine learning models, particularly in capturing nonlinear relationships in financial data through kernel functions that map inputs into higher-dimensional Hilbert spaces.

Dettagli dell'edizione

• Editore: Independently published
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 13: 9798340304148
• Rilegatura: Copertina flessibile
• Numero di pagine: 351
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile