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Artículo: AMZ-B0DHZ5BTTQ
Deep Learning in Hilbert Spaces: New Frontiers in Algorithmic Trading (The Artificial Edge: Quantitative Trading Strategies with Python)
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Hardcover
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0.67 kg
0.67 kg
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Amazon
Amazon
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USA
USA
Sobre este producto
- 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.
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