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An Efficient Implementation of the Backtesting of Trading Strategies

  • Conference paper
Parallel and Distributed Processing and Applications (ISPA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3758))

Abstract

Some trading strategies are becoming more and more complicated and utilize a large amount of data, which makes the backtesting of these strategies very time consuming. This paper presents an efficient implementation of the backtesting of such a trading strategy using a parallel genetic algorithm (PGA) which is fine tuned based on thorough analysis of the trading strategy. The reuse of intermediate results is very important for such backtesting problems. Our implementation can perform the backtesting within a reasonable time range so that the tested trading strategy can be properly deployed in time.

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References

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, University of Technology, Sydney, GPO Box 123, Broadway, NSW 2007, Australia

    Jiarui Ni & Chengqi Zhang

Authors
  1. Jiarui Ni
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  2. Chengqi Zhang
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Editor information

Editors and Affiliations

  1. Dept. of CS, Georgia State University, 30302, Atlanta, GA, USA

    Yi Pan

  2. State Key Laboratory for Novel Software Technology, Nanjing University, 210093, Nanjing, Jiangsu, China

    Daoxu Chen

  3. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 200030, Shanghai, China

    Minyi Guo

  4. Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong, China

    Jiannong Cao

  5. Computer Science Department, University of Tennessee, 37996-3450, Knoxville, TN, USA

    Jack Dongarra

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© 2005 Springer-Verlag Berlin Heidelberg

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Cite this paper

Ni, J., Zhang, C. (2005). An Efficient Implementation of the Backtesting of Trading Strategies. In: Pan, Y., Chen, D., Guo, M., Cao, J., Dongarra, J. (eds) Parallel and Distributed Processing and Applications. ISPA 2005. Lecture Notes in Computer Science, vol 3758. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11576235_17

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  • DOI: https://doi.org/10.1007/11576235_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29769-7

  • Online ISBN: 978-3-540-32100-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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