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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.
ASIN : B0DHV2GRLG
Accessibility : Learn more
Publication date : September 24, 2024
Language : English
File size : 9.2 MB
Enhanced typesetting : Not Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 351 pages
Format : Print Replica
Page Flip : Not Enabled
Part of series : The Artificial Edge: Quantitative Trading Strategies with Python
Best Sellers Rank: #1,640,960 in Kindle Store (See Top 100 in Kindle Store) #444 in Options Trading (Kindle Store) #790 in Options Trading (Books) #1,283 in Stock Market Investing (Kindle Store)
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