Accelerating Back-testing of Algorithmic Trading Strategies

Algorithmic trading has become ever more popular in recent years - accounting for more than half of all European and American stock trades. The strategies applied need to be regularly back-tested against historical market data for calibration and to check the expected return and risk. This is a computationally demanding process that can take hours to complete. However, back-testing and optimising the strategies frequently intra-day can significantly increase the profits for the trading institution.

This white paper surveys methods for back-testing trading strategies and highlights opportunities for acceleration. It explains how back-testing can be parallelised and explores the application of accelerator processors such as GPUs and Intel's Xeon Phi. Using a practical example, the paper demonstrates how large performance gains can be achieved.