面向限价指令簿趋势分析的网络集成模型

doi:10.16451/j.cnki.issn1003-6059.202108008

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摘要为了更好地分析限价指令簿(LOBs)的趋势,文中提出面向LOBs趋势分析的网络集成模型(NEM-LOB).模型融合2个长短期记忆(LSTM)子模型和1个卷积神经网络(CNN)子模型.一个LSTM子模型可通过LOBs的分布信息捕捉全局时间依赖性,另一个LSTM子模型可通过LOBs和订单流的动态信息捕捉全局动态性.CNN子模型通过LOBs的事实信息提取局部特征.最后,结合3个子模型,提取特征以获得预测结果.在FI-2010数据集上的实验表明NEM-LOB通过引入订单流信息,能对LOBs进行更好的趋势分析.

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关键词 ：订单流, 限价指令簿(LOBs), 集成模型, 卷积神经网络(CNN), 长短期记忆(LSTM)

Abstract：To analyze the trend of limit order books(LOBs) better, a network ensemble model for trend analysis of LOBs(NEM-LOB) is proposed. Two long short-term memory(LSTM) sub-models and one convolutional neural network sub-model are integrated in NEM-LOB. One LSTM sub-model captures the global temporal dependence through the distribution information of LOBs. The other LSTM sub-model captures the global dynamics through the dynamic information of LOBs and order streams. The local features are extracted through the factual information of LOBs. Finally, three sub-models are combined to extract features to obtain prediction results. Experiments on FI-2010 dataset show that NEM-LOB makes a better trend analysis for LOBs by combining order streams.

Key words： Order Streams Limit Order Books(LOBs) Ensemble Model Convolutional Neural Network(CNN) Long Short-Term Memory(LSTM)

收稿日期: 2021-04-28

ZTFLH:

TP 183

基金资助:江苏省高校自然科学研究项目(No.19KJA550002)、江苏省六大人才高峰项目(No.XYDXX-054)、江苏高校优势学科建设工程项目资助

通讯作者: 张莉,博士,教授,主要研究方向为机器学习、模式识别、图像处理.E-mail:zhangliml@suda.edu.cn.

作者简介: 吕雪瑞,硕士研究生,主要研究方向为机器学习、深度学习、金融时间序列分析.E-mail:20194227038@stu.suda.edu.cn.

引用本文:

吕雪瑞, 张莉. 面向限价指令簿趋势分析的网络集成模型[J]. 模式识别与人工智能, 2021, 34(8): 751-759. LÜ Xuerui, ZHANG Li. Network Ensemble Model for Trend Analysis of Limit Order Books. , 2021, 34(8): 751-759.

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http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.202108008 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2021/V34/I8/751

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