When Does Order Flow Matter? State-Dependent L2 Liquidity-State Transitions in Crypto Futures
2026-07-10 • Machine Learning
Machine Learning
AI summaryⓘ
The authors analyze trading data from Binance futures for Bitcoin and Ethereum to understand how short-term market states before big events predict liquidity changes afterward. They find that the immediate liquidity state prior to an event is the strongest indicator of how liquidity behaves post-event, more so than traditional models or knowing the event type. Adding information about trade orders helps only when combined with the liquidity state, and this effect varies between Bitcoin and Ethereum. Their work suggests that modeling market microstructure should first focus on capturing these liquidity states before adding more complex features.
limit order bookliquiditymarket microstructurefutures tradingevent studypredictive modelingsupervised learningorder flowBTCUSDTETHUSDT
Authors
Joohyoung Jeon
Abstract
Building event-conditioned market models requires separating macro-event labels from persistent microstructure state. We study this distinction in Binance BTCUSDT and ETHUSDT futures from 2023-2026, combining top-20 L2 order book data, trade-flow records, and macro-event windows. We define a supervised discrete L2 liquidity-state transition task, distinct from latent-regime detection and price-direction prediction, and evaluate models in rolling monthly out-of-sample folds with event-clustered validation and blocked permutation tests, admitting each feature layer only if it improves on the layer below it on the same panel. Within these event windows, the first-order predictive signal is the pre-event L2 liquidity state: a coarse pre-event state baseline strongly predicts post-event liquidity regimes, interpretable logit models over continuous L2 features fail to improve on it, and a shallow nonlinear L2 model adds a robust further gain of comparable size to the state baseline's own. The macro-event calendar enters only by locating the windows and supplying matched non-event controls; we use event timing but not the event's label content, so pre-event state competes against an uninformed within-window baseline, not against the event type. Order flow adds further value only when layered on top of the L2 state model, not as a replacement. This value is not robustly cross-symbol: for ETH it is present across calm, mixed, and stressed regimes and largest under stressed pre-event liquidity, whereas BTC shows only isolated five-minute passes and no regime that clears at both horizons. These findings motivate a state-first design principle for market microstructure models. We provide a liquidity-state transition baseline and evaluation protocol that reinforcement-learning, execution-policy, or LLM-based context layers should exceed before their added value is credited.