We design and train machine learning models to capture the nonlinear
interactions between financial market dynamics and high-frequency trading (HFT)
activity. In doing so, we introduce new metrics to identify liquidity-demanding
and -supplying HFT strategies. Both types of HFT strategies increase activity
in response to information events and decrease it when trading speed is
restricted, with liquidity-supplying strategies demonstrating greater
responsiveness. Liquidity-demanding HFT is positively linked with latency
arbitrage opportunities, whereas liquidity-supplying HFT is negatively related,
aligning with theoretical expectations. Our metrics have implications for
understanding the information production process in financial markets.

利用机器学习模型，我们研究金融市场动态与高频交易活动之间的非线性相互作用，并引入新的指标来识别需求和供应流动性的高频交易策略。这些策略在信息事件发生时增加交易活动，在交易速度受限时降低交易活动，其中供应流动性策略表现出更高的反应性。需求流动性的高频交易与延迟套利机会呈正相关，而供应流动性的高频交易呈负相关，与理论预期一致。我们的指标对于理解金融市场中的信息生产过程具有重要意义。