Computer Science ›› 2022, Vol. 49 ›› Issue (7): 31-39.doi: 10.11896/jsjkx.210400304

• Database & Big Data & Data Science • Previous Articles     Next Articles

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

ZHANG Yuan1, KANG Le2,3, GONG Zhao-hui3, ZHANG Zhi-hong1   

  1. 1 School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China
    2 Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China
    3 Finance Innovation Laboratory of Zhengzhou Esunny Information,Zhengzhou 450018,China
  • Received:2021-04-29 Revised:2021-08-16 Online:2022-07-15 Published:2022-07-12
  • About author:ZHANG Yuan,born in 1996,postgra-duate,is a student member of China Computer Federation.His main research interests include data mining and financial big data.
    ZHANG Zhi-hong,born in 1965,Ph.D,professor,is a senior member of China Computer Federation.His main research interests include financial big data,blockchain,and distributed computing.
  • Supported by:
    Major Public Welfare Project of Henan Province(201300210300).

Abstract: 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.

Key words: Bi-LSTM, Futures market, Multi-feature information fusion, Multi-granularity feature extraction, Related transaction behavior

CLC Number: 

  • TP301
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