发表机构
Korea University（韩国大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究 2023 年至 2026 年币安加密期货，定义离散 L2 流动性状态转换任务，用事件聚类验证等评估模型。发现事件前 L2 流动性状态是重要预测信号，订单流在 L2 状态模型上分层才有价值，且不同符号表现不同，提出状态优先设计原则及评估协议。
AI 中文摘要
构建事件条件市场模型需要将宏观事件标签与持久的微观结构状态分开。我们研究了 2023 年至 2026 年币安 BTCUSDT 和 ETHUSDT 期货中的这种区别，结合了前 20 的 L2 订单簿数据、交易流记录和宏观事件窗口。我们定义了一个有监督的离散 L2 流动性状态转换任务，与潜在状态检测和价格方向预测不同，并在滚动月度样本外折叠中使用事件聚类验证和阻塞排列测试来评估模型，只有当每个特征层在同一面板上比其下一层有所改进时才允许使用。在这些事件窗口内，一阶预测信号是事件前的 L2 流动性状态：一个粗略的事件前状态基线强烈预测事件后的流动性状态，基于连续 L2 特征的可解释逻辑模型未能在此基础上改进，而一个浅层非线性 L2 模型为状态基线本身增加了相当规模的稳健进一步收益。宏观事件日历仅通过定位窗口并提供匹配的非事件控制来进入；我们使用事件时间但不使用事件的标签内容，因此事件前状态与一个不知情的窗口内基线竞争，而不是与事件类型竞争。订单流只有在 L2 状态模型之上分层时才会增加进一步的价值，而不是作为替代品。这种价值在不同符号之间并不稳健：对于 ETH，它在平静、混合和压力状态下都存在，在压力事件前流动性下最大，而 BTC 只显示孤立的五分钟时间段，没有在两个时间范围内都清晰的状态。这些发现激发了市场微观结构模型的状态优先设计原则。我们提供了一个流动性状态转换基线和评估协议，强化学习、执行策略或基于大语言模型的上下文层在其附加值得到认可之前应超过该协议。
英文摘要
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.
Comments8 pages, 2 figures, 1 table
