Algorithmic trading strategy optimization based on mutual information entropy based clustering

Authors:
Feng Wang;Keren Dong;Xiaotie Deng
Affiliations:
State Key Lab. of Software Engineering, Wuhan University, Wuhan, China;Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong;Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
Venue:
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Year:
2010

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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.