面向限价指令簿趋势分析的网络集成模型

Network Ensemble Model for Trend Analysis of Limit Order Books

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.

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.