基于Bi-LSTM的期货市场关联交易行为检测方法  

Related Transaction Behavior Detection in Futures Market Based on Bi-LSTM

作　　者：张源 康乐[2,3] 宫朝辉 张志鸿 ZHANG Yuan;KANG Le;GONG Zhao-hui;ZHANG Zhi-hong(School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China;Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China;Finance Innovation Laboratory of Zhengzhou Esunny Information,Zhengzhou 450018,China)

机构地区：[1]郑州大学信息工程学院,郑州450001 [2]清华大学计算机科学与技术系,北京100084 [3]郑州易盛信息金融创新实验室,郑州450018

出　　处：《计算机科学》2022年第7期31-39,共9页Computer Science

基　　金：河南省重大公益专项(201300210300)。

摘　　要：随着期货市场的不断发展,其交易量屡创新高,但在海量交易的背后,一些交易者利用关联交易行为对市场进行操纵,扰乱了交易秩序,给市场监管和风险控制带来了严峻考验。因此,如何从海量交易中挖掘潜在关联交易行为成为维护期货市场公平交易的重要任务。针对该问题,提出了一种多特征信息融合的双向长短期记忆(Bi-LSTM)网络模型,从原始数据中提取交易时间、交易量、持仓变化、期货品种等多种维度的浅层特征信息,通过Bi-LSTM网络模型从时间序列上向前、向后两个方向的上下文关系学习深层特征,实现关联交易行为检测。针对浅层特征提取提出了一种基于交易行为的多粒度窗口特征提取方法,从日、小时、分钟、秒等级别捕捉账户间交易的关联性,从而解决了原始交易数据维度高、数据量大、关联性弱的问题。模型引入了Dropout策略,缓解了收敛速度慢和过拟合的问题。在郑州商品交易所真实数据上的实验结果表明,与一些传统的分类模型以及RNN和LSTM网络相比,所提方法在分类的准确率和召回率上有明显提升,同时,对特征中各个维度信息的消解实验证明了多特征融合方法和多粒度窗口策略的有效性。另外,抽取了两种期货品种的交易数据进行测试,结果表明所提模型具有良好的泛化能力。With the continuous development of the futures market,its transaction volume keeps increasing.Behind the massive transactions,some traders use related transaction behaviors to manipulate the futures market and disrupt the order of transactions,which has brought severe challenges to market supervision and risk control.How to mining potential related transaction behaviors from massive transaction data is an important task for maintaining fair transactions in the futures market.In response to this problem,this paper proposes a bidirectional long short-term memory(Bi-LSTM)network model with multi-feature information fusion,which extracts multiple dimensions of shallow feature information such as trading time,trading volume,position changes,and futures varieties from the original transaction data.The Bi-LSTM network model learns deep features from the contextualrelationship in the forward and backward directions of the time series and realizes the detection of related transaction behavior.For shallow features extraction,a multi-granularity window feature extraction method based on transaction behavior is proposed,to captures the correlation of transactions between accounts from the levels of day,hour,minute,second,etc.It solves the problems of high data dimension,large amount of data,and weak correlation of original transaction data.The model introduces the Dropout strategy to alleviate problems of slow convergence and over-fitting.Experimental results on the real data of Zhengzhou Commodity Exchange show that the proposed method evidently improves the classification precision and recall compared with some traditional classification models and RNN and LSTM network.At the same time,the ablation experiment of each dimension information proves the effectiveness of the multi-feature fusion method and the multi-granularity window strategy.In addition,the transaction data of two futures varieties are extracted for testing,and the results show that the proposed model has good generalization ability.

关 键 词：关联交易行为 多特征信息融合 多粒度特征提取 Bi-LSTM 期货市场 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]