用新分词法让大模型读懂订单簿消息，预测更准且更快。
LOBERT: Generative AI Foundation Model for Limit Order Book Messages
- 将多维订单消息当作单一令牌处理，保留价格、成交量和时间的连续表示
- 在预测中间价波动和下一消息任务上达到领先性能，上下文长度更短
- 通用编码器架构可微调，适合多种订单簿下游任务
在消息层面建模金融限价订单簿（LOB）动态极具挑战，源于事件发生时间不规则、市场状态快速切换，以及高频交易者对可见订单流的反应。现有订单簿模型依赖繁琐的数据表示，且在原始任务外适应性差。为此，我们提出 LOBERT——一种适用于下游微调的通用编码器型基础模型。通过将原始 BERT 架构适配至订单簿数据，LOBERT 采用新颖的分词方案，将完整的多维消息视为单一令牌，同时保留价格、成交量和时间的连续表示。该方法在预测中间价变动和下一消息等任务上表现优异，相较以往方法显著缩短所需上下文长度。
原文摘要 · Abstract (English)
Modeling the dynamics of financial Limit Order Books (LOB) at the message level is challenging due to irregular event timing, rapid regime shifts, and the reactions of high-frequency traders to visible order flow. Previous LOB models require cumbersome data representations and lack adaptability outside their original tasks, leading us to introduce LOBERT, a general-purpose encoder-only foundation model for LOB data suitable for downstream fine-tuning. LOBERT adapts the original BERT architecture for LOB data by using a novel tokenization scheme that treats complete multi-dimensional messages as single tokens while retaining continuous representations of price, volume, and time. With these methods, LOBERT achieves leading performance in tasks such as predicting mid-price movements and next messages, while reducing the required context length compared to previous methods.
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