Deep Learning in Hilbert Spaces: New Frontiers in Algorithmic Trading

By: Flux, Jamie

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.

• Format: Paperback
• Language: English
• ISBN: B0DHYLCQ4J
• ISBN13: 9798340304148
• Release Date: September 2024
• Publisher: Independently Published
• Length: 352 Pages
• Weight: 1.04 lbs.
• Dimensions: 0.7" x 6.0" x 9.0"