GPU-Accelerated Research in Quant Finance : Using CUDA to Speed Up Backtests and Analytics
Language: English
Published by Nobletrex Press, 2025
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 50.85
Quantity: 2 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 'GPU-Accelerated Research in Quant Finance: Using CUDA to Speed Up Backtests and Analytics'This book is for quantitative researchers, systematic portfolio managers, and technologists who want to turn GPUs from a buzzword into a practical edge. It bridges the gap between theoretical quant finance and high-performance computing, showing how to move real research workloads-backtests, risk engines, and pricing libraries-from CPU-bound prototypes to production-ready GPU pipelines.Readers will learn the mathematical and statistical foundations most relevant to GPU acceleration, then build a rigorous research and backtesting methodology that survives contact with real markets and regulators. The core chapters develop a working mental model of modern GPU architectures and the CUDA programming model, before introducing powerful patterns and libraries for Monte Carlo, PDE/FFT pricing, portfolio optimization, and risk analytics. Throughout, the focus is on trustworthy speedups: performance engineering, profiling, validation, and reproducibility.The book assumes comfort with Python and basic quantitative finance, but no prior CUDA experience. All examples are designed for implementation in a modern research stack, with LaTeX-quality formulas and code that map cleanly onto Python/CUDA tooling. The result is a practical, end-to-end guide to designing faster research loops and more ambitious models without sacrificing transparency or control. …
Seller Inventory # 9798896652281
- Title
- GPU-Accelerated Research in Quant Finance : Using CUDA to Speed Up Backtests and Analytics
- Author
- Thomas V. Trex
- Publisher
- Nobletrex Press
- Publication year
- 2025
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798896652281
- Item weight
- 728 grams
- Dimensions
- 229x152x27 mm
"GPU-Accelerated Research in Quant Finance: Using CUDA to Speed Up Backtests and Analytics"
This book is for quantitative researchers, systematic portfolio managers, and technologists who want to turn GPUs from a buzzword into a practical edge. It bridges the gap between theoretical quant finance and high-performance computing, showing how to move real research workloads—backtests, risk engines, and pricing libraries—from CPU-bound prototypes to production-ready GPU pipelines.
Readers will learn the mathematical and statistical foundations most relevant to GPU acceleration, then build a rigorous research and backtesting methodology that survives contact with real markets and regulators. The core chapters develop a working mental model of modern GPU architectures and the CUDA programming model, before introducing powerful patterns and libraries for Monte Carlo, PDE/FFT pricing, portfolio optimization, and risk analytics. Throughout, the focus is on trustworthy speedups: performance engineering, profiling, validation, and reproducibility.
The book assumes comfort with Python and basic quantitative finance, but no prior CUDA experience. All examples are designed for implementation in a modern research stack, with LaTeX-quality formulas and code that map cleanly onto Python/CUDA tooling. The result is a practical, end-to-end guide to designing faster research loops and more ambitious models without sacrificing transparency or control.
"Synopsis" may belong to another edition of this title.
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