LOBIN: in-network machine learning for limit order books - Hong, X; Zheng, C; Zohren, S; Zilberman, N (IEEE, 2023)
Machine learning is driving the evolution of algorithmic trading, but the demands for fast execution speed remain. Although both aim to increase profitability, embedding more powerful machine learning approaches and lowering trading latencies are hard to achieve simultaneously. Offloading machine learning inference to programmable network devices, also referred to as in-network machine learning, provides a delicate balance between the two ends of this trade-off. In this paper, we present LOBIN, providing machine learning based market prediction using high-frequency market data feeds. LOBIN builds limit order books and conducts inference within programmable switches. Compared with server-based solutions, LOBIN predicts future stock price movements with lower latency, higher throughput, and a minor impact on machine learning performance.