GPU-Accelerated Research in Quant Finance

<p>GPU-Accelerated Research in Quant Finance: Using CUDA to Speed Up Backtests and Analytics<br /><br />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.<br /><br />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.<br /><br />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.</p>