When Does Order Flow Matter? State-Dependent L2 Liquidity-State Transitions in Crypto Futures
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摘要
This paper investigates event-conditioned microstructure in Binance BTCUSDT and ETHUSDT futures (2023–2026), defining a supervised discrete L2 liquidity-state transition task (calm/mixed/stressed) using pre-event state and top-20 L2 features, and evaluating a staged model sequence under rolling-month out-of-sample folds and robust controls. The main finding is a state-first design: the pre-event L2 state is the primary predictor of post-event liquidity regimes; linear continuous L2 features do not outperform the coarse state baseline; a shallow nonlinear L2-shape model yields a comparable additive gain; order flow adds incremental value only when layered atop the L2 model, and this gain is ETH-dominant and stress-amplified, while BTC shows no robust cross-horizon regime. The cycle concludes with a practical evaluation protocol and a baseline for future reinforcement-learning or context-layer extensions to exceed before crediting extra context signals [page::1]
