  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.
