Algorithmic Trading Strategy Optimization Based on Mutual Information Entropy Based Clustering

  • Feng Wang
  • Keren Dong
  • Xiaotie Deng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6382)


Algorithmic trading strategies are automated defining a sequence of instructions executed by a computer. A good strategy should be profitable which includes identification of what to trade and how to trade. In this paper, we focus on the study of algorithmic trading strategy optimization and propose a strategy optimization model based on an initialized strategy pool. In order to get a better strategy, a mutual information entropy based clustering algorithm is employed to analyze the correlations among the stocks and a reward and punishment scheme is also set up for updating the latest transaction data in the strategy optimization process. Experimental results on several different groups of stocks showed that in most cases, this optimization model can find a profitable strategy swiftly.


Trading Strategy Punishment Scheme Frequency Matrix Algorithmic Trading High Trading Frequency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    van Bunningen, A.H.: Augmented Trading: from news articls to stock price prediction using syntactic analysis. Master’s thesis, University of Twente (2004)Google Scholar
  2. 2.
    Chen, S., Navet, N.: Pretests for genetic-programming evolved trading programs: “zero-intelligence” strategies and lottery trading. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4234, pp. 450–460. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Choice, A.S.: Market risk and algorithmic trading. Tech. rep., AMD White Paper (2008)Google Scholar
  4. 4.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory, 2nd edn. Wiley Series in Telecommunications and Signal Processing. Wiley-Interscience, Hoboken (2006)zbMATHGoogle Scholar
  5. 5.
    Crammer, K., Kearns, M., Wortman, J.: Learning from multiple sources. Journal of Machine Learning Research 9, 1757–1774 (2008)MathSciNetGoogle Scholar
  6. 6.
    He, H., Chen, J., Jin, H., Chen, S.: Stock trend analysis and trading strategy. In: JCIS. Atlantis Press (2006)Google Scholar
  7. 7.
    Kakade, S., Kearns, M.J., Mansour, Y., Ortiz, L.E., Competitive, L.E.: algorithms for vwap and limit order trading. In: ACM Conference on Electronic Commerce, pp. 189–198 (2004)Google Scholar
  8. 8.
    Nevmyvaka, Y., Feng, Y., Kearns, M.: Reinforcement learning for optimized trade execution. In: ICML 2006, pp. 673–680 (2006)Google Scholar
  9. 9.
    Pranav, P., Eamonn, K., Jessica, L., Stefano, L.: Mining motifs in massive time series databases. In: Proceedings of IEEE International conference on data mining, pp. 370–377 (2002)Google Scholar
  10. 10.
    Rakesh, A., Christos, F., Arun, S.: Efficient similarity serach in sequence databases. In: Proceedings of 4th International Conference on Foundations of Data Organization and Algorithms, pp. 13–15 (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Feng Wang
    • 1
    • 2
  • Keren Dong
    • 2
  • Xiaotie Deng
    • 2
  1. 1.State Key Lab. of Software EngineeringWuhan UniversityWuhanChina
  2. 2.Department of Computer ScienceCity University of Hong KongKowloonHong Kong

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