LiT:限量订单簿变压器
Yue Xiao1, Carmine Ventre1, Yuhan Wang2
1Finance Hub, Department of Informatics, King's College London, London, United Kingdom.
Frontiers in artificial intelligence
|October 29, 2025
概括
我们介绍了极限订单簿转换器 (LiT),这是一个用于金融市场预测的新型深度学习模型. LiT有效地捕捉了市场微观结构的动态,在限量订单簿分析中表现优于现有的方法.
科学领域:
- 人工智能的人工智能
- 计算金融是指计算金融.
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 变压器架构在NLP和计算机视觉方面取得了成功.
- 变压器对于极限订单簿 (LOB) 预测的有限应用.
- 需要在LOB数据中捕捉空间和时间依赖性的模型.
研究的目的:
- 介绍极限订单簿转换器 (LiT),一种新的深度学习架构.
- 使用高频限量订单簿数据预测短期市场变动.
- 在LOB预测中捕捉空间和时间依赖性的地址限制.
主要方法:
- 利用结构化补丁和基于变压器的自我注意力.
- 在市场微观结构动态中模拟空间和时间特征.
- 使用高频限量订单簿数据进行预测.
主要成果:
- LiT的表现始终优于传统的机器学习方法.
- LiT超越了最先进的深度学习基线.
- 通过微调,LiT在分布变化下表现强.
结论:
- LiT是一种用于LOB预测的新且有效的深度学习架构.
- 对于动态的金融环境,LiT提供了一个实用的解决方案.
- 该模型成功地捕捉了复杂的市场微观结构动态.
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