Hardware acceleration of high-frequency trading strategies with memristor crossbars
PI: Dr. Can Li, HKU
The proposed research aims to accelerate high-frequency trading with new in-memory computing hardware based on emerging memristors.
High-frequency trading is a set of strategies to trade securities in the financial markets based on quantitative models that run on a computer. Usually, the one who can execute the orders faster can make more profit than the others. The requirement has limited the available model complexity that can be used for high-frequency trading. Therefore, financial firms are actively building more powerful computing hardware, to expedite the speed of processing market information, but the conventional hardware is ill-suited to perform data-intensive tasks.
Memristor-based compute-in-memory hardware has been proposed to accelerate various data-intensive computing workloads, owing to its massive parallelism and co-located computing and memory units. With the hardware, the latency to process the market information and execute the order is independent of model size, promising at least three orders of magnitude improvement compared to conventional hardware.
The research in our group has suggested the hardware can predict time-series data with minimum latency. In this research, we plan to apply the technology to process market information. We expect the hardware enables more capable models for high-frequency trading, leading to more profit.