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An intelligent market making strategy in algorithmic trading

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Abstract

Market making (MM) strategies have played an important role in the electronic stock market. However, the MM strategies without any forecasting power are not safe while trading. In this paper, we design and implement a twotier framework, which includes a trading signal generator based on a supervised learning approach and an event-driven MM strategy. The proposed generator incorporates the information within order book microstructure and market news to provide directional predictions. The MM strategy in the second tier trades on the signals and prevents itself from profit loss led by market trending. Using half a year price tick data from Tokyo Stock Exchange (TSE) and Shanghai Stock Exchange (SSE), and corresponding Thomson Reuters news of the same time period, we conduct the back-testing and simulation on an industrial near-to-reality simulator. From the empirical results, we find that 1) strategies with signals perform better than strategies without any signal in terms of average daily profit and loss (PnL) and sharpe ratio (SR), and 2) correct predictions do help MM strategies readjust their quoting along with market trending, which avoids the strategies triggering stop loss procedure that further realizes the paper loss.

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Author information

Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Hong Kong, China

    Xiaodong Li

  2. AIMS Lab, Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, 200240, China

    Xiaotie Deng

  3. Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, 200433, China

    Xiaotie Deng & Shanfeng Zhu

  4. School of Computer Science, Fudan University, Shanghai, 200433, China

    Shanfeng Zhu

  5. State Key Lab of Software Engineering, School of Computer Science, Wuhan University, Wuhan, 430072, China

    Feng Wang

  6. Department of Computer Science, Hong Kong Baptist University, Hong Kong, China

    Haoran Xie

Authors
  1. Xiaodong Li
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  2. Xiaotie Deng
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  3. Shanfeng Zhu
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  4. Feng Wang
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  5. Haoran Xie
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Corresponding author

Correspondence to Feng Wang.

Additional information

Xiaodong Li received the BSc degree in computer science from Nanjing University, China. He is currently a PhD student at City University of Hong Kong, China. His research interests include data mining and algorithmic trading.

Xiaotie Deng is a chair professor in the Department of Computer Science, Shanghai Jiaotong University, China. His research focus is on algorithmic game theory, which deals with computational issues on fundamental economic problems such as Nash equilibrium, resource pricing and allocation protocols such as auction and market equilibrium, as well as theory and practice in internet market design.

Shanfeng Zhu received the BS and MPhil degrees in computer science from Wuhan University, China in 1996 and 1999, respectively, and the PhD in computer science from the City University of Hong Kong, China in 2003. He is currently an associate professor of School of Computer Science, and Shanghai Key Lab of Intelligent Information Processing, Fudan University, China. Before joining Fudan University in July 2008, he was a postdoctoral fellow at Kyoto University, Japan. His research focuses on developing and applying machine learning, data mining and algorithmic methods for information retrieval, algorithmic trading, and bioinformatics. He is a member of the CCF and the ACM.

Feng Wang received the MS and PhD degrees in computer science in 2005 and 2008, respectively, both from Wuhan University, China. She is currently an associate professor of School of Computer Science, and State Key Lab of Software Engineering ofWuhan University, China. Her research interests include machine learning, intelligent information retrieval, and algorithmic trading. She serves as a reviewer for several IEEE transactions, other international journals and conferences. She is a member of IEEE and ACM, and a senior member of CCF.

Haoran Xie received the BEng degree in software engineering from Beijing University of Technology, China and the MSc and PhD degrees in computer science from City University of Hong Kong, China. He is currently a senior research assistant at Hong Kong Baptist University, China. His research interests include user modeling, personalization, social media, recommender systems, and financial data mining.

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Li, X., Deng, X., Zhu, S. et al. An intelligent market making strategy in algorithmic trading. Front. Comput. Sci. 8, 596–608 (2014). https://doi.org/10.1007/s11704-014-3312-6

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  • DOI: https://doi.org/10.1007/s11704-014-3312-6

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