深限订单簿预测:一个微观结构指南
Antonio Briola1, Silvia Bartolucci2, Tomaso Aste1,2
1Department of Computer Science, University College London, London, WC1E 6EA, UK.
Quantitative finance
|August 4, 2025
概括
深度学习可以使用Limit Order Book数据预测股票中价变化,但高准确度不能保证利的交易信号. 需要新的指标来评估这个领域的实际预测.
科学领域:
- 量化金融 量化金融
- 机器学习 机器学习
- 金融市场微观结构 金融市场微观结构
背景情况:
- 高频交易依赖于准确的价格预测.
- 限量订单簿 (LOB) 数据为市场动态提供了细致的见解.
- 在LOB数据中评估深度学习模型的性能需要专门的指标.
研究的目的:
- 通过深度学习探索高频限制订单簿中期价格变化的可预测性.
- 推出LOBFrame,这是一个开源工具,用于处理LOB数据和评估深度学习模型.
- 提出一个创新的框架来评估LOB价格预测的实际实用性.
主要方法:
- 利用了尖端的深度学习方法.
- 开发并发布了用于大规模LOB数据处理的LOBFrame.
- 提出了一个新的运营框架,重点关注交易完成概率.
主要成果:
- 深度学习模型的有效性受到库存微观结构特征的影响.
- 高预测能力并不直接转化为可操作的交易信号.
- 传统的机器学习指标对于LOB预测评估是不够的.
结论:
- 深度学习显示了LOB中价预测的潜力,但实际应用需要仔细评估.
- 拟议的框架增强了超越标准指标的预测实用性的评估.
- 学术界和从业人员可以利用这些发现,在LOB分析中对深度学习做出明智决策.
关键词:
在C32中,C32是指C32和C32.在C53的基础上.在C5555中,它是C55的.深度学习是一种深度学习.经济物理 经济物理G1414 一个人的生活高频交易是一种高频交易.限量订单簿 限量订单簿 限量订单簿市场微观结构 市场微观结构更多相关视频
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