GPU-Accelerated Research in Quant Finance (Paperback)

Paperback. "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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …