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)

Abstract

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.

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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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