Run-time Reconfigurable Acceleration for Genetic Programming Fitness Evaluation in Trading Strategies

Abstract

Genetic programming can be used to identify complex patterns in financial markets which may lead to more advanced trading strategies. However, the computationally intensive nature of genetic programming makes it difficult to apply to real world problems, particularly in realtime constrained scenarios. In this work we propose the use of Field Programmable Gate Array technology to accelerate the fitness evaluation step, one of the most computationally demanding operations in genetic programming. We propose to develop a fully-pipelined, mixed precision design using run-time reconfiguration to accelerate fitness evaluation. We show that run-time reconfiguration can reduce resource consumption by a factor of 2 compared to previous solutions on certain configurations. The proposed design is up to 22 times faster than an optimised, multithreaded software implementation while achieving comparable financial returns.

Flexible task‐DAG management in PHAST library: Data‐parallel tasks and orchestration support for heterogeneous systems

“…From a technological point of view, these devices are characterized by the presence of multiple specialized computational resources: multi‐core CPUs, GPUs, FPGAs, and ad‐hoc modules (e.g., AI‐related TPUs, DSPs) 2,3 . The most common combination of resources in the same system is a multi‐core CPU with a many‐cores GPU 4‐6 .…”