Skip to main content

Portfolio of Global Futures Algorithmic Trading Strategies for Best Out-of-Sample Performance

Part of the Lecture Notes in Business Information Processing book series (LNBIP,volume 255)

Abstract

We investigate two different portfolio construction methods for two different sets of algorithmic trading strategies that trade global futures. The problem becomes complex if we consider the out-of-sample performance. The Comgen method blindly optimizes the Sharpe ratio, and Comsha does the same but gives priority to strategies that individually have the better Sharpe ratio. It has been shown in the past that high Sharpe ratio strategies tend to perform better in out-of-sample periods. As the benchmark method, we use an equally weighted (1/N, naïve) portfolio. The analysis is performed on two years of out-of-sample data using a walk forward approach in 24 independent periods. We use the mean reversion and trend following datasets consisting of 22,702 and 36,466 trading models (time series), respectively. We conclude that Comsha produces better results with trend-following methods, and Comsha performs the same as Comgen with other type of strategies.

Keywords

  • Portfolio construction
  • Algorithmic trading
  • Sharpe ratio
  • Optimization
  • Comgen
  • Comsha

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-39426-8_33
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   59.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-39426-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   79.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

References

  1. Narang, R.K.: Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading. Wiley, New York (2013)

    CrossRef  Google Scholar 

  2. Markowitz, H.: Portfolio selection. J. Finan. 7(1), 77–91 (1952)

    Google Scholar 

  3. Raudys, S., Raudys, A., Pabarskaite, Z.: Sustainable economy inspired large-scale feed-forward portfolio construction. Technol. Econ. Dev. Econ. 20(1), 79–96 (2014)

    CrossRef  Google Scholar 

  4. McNelis, P.D.: Neural Networks in Finance: Gaining Predictive Edge in the Market. Academic Press, London (2005)

    Google Scholar 

  5. Ustun, O., Kasimbeyli, R.: Combined forecasts in portfolio optimization: a generalized approach. Comput. Oper. Res. 39(4), 805–819 (2012)

    MathSciNet  CrossRef  MATH  Google Scholar 

  6. Yamamoto, R., Ishibashi, T., Konno, H.: Portfolio optimization under transfer coefficient constraint. J. Asset Manag. 13(1), 51–57 (2011)

    CrossRef  Google Scholar 

  7. Sharpe, W.F.: Mutual fund performance. J. Bus. 39(1), 119–138 (1966)

    CrossRef  Google Scholar 

  8. Hung, K., Cheung, Y., Xu, L.: An extended ASLD trading system to enhance portfolio management. IEEE Trans. Neural Netw. 14(2), 413–425 (2003)

    CrossRef  Google Scholar 

  9. Freitas, F.D., De Souza, A.F., de Almeida, A.R.: Prediction-based portfolio optimization model using neural networks. Neurocomputing 72(10–12), 2155–2170 (2009)

    CrossRef  Google Scholar 

  10. Wang, J., Qiu, G., Cao, X.: Application of genetic algorithm based on dual mutation in the optimal portfolio selection. J. Nanchang Hangkong Univ. (Nat. Sci.) 4, 006 (2009)

    Google Scholar 

  11. Jivendra, K.: Portfolio optimization using the quadratic optimization system and publicly available information on the WWW. Manag. Finan. 35(5), 439–450 (2009)

    Google Scholar 

  12. Bailey, D.H., Borwein, J.M., de Prado, M.L., Zhu, Q.J.: Pseudomathematics and financial charlatanism: the effects of backtest over fitting on out-of-sample performance. Not. AMS 61(5), 458–471 (2014)

    MATH  Google Scholar 

  13. Stein, M., Branke, J., Schmeck, H.: Efficient implementation of an active set algorithm for large-scale portfolio selection. Comput. Oper. Res. 35(12), 3945–3961 (2008)

    CrossRef  MATH  Google Scholar 

  14. Raudys, A., Pabarskaite, Z.: Discrete portfolio optimisation for large scale systematic trading applications. In: 2012 5th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE (2012)

    Google Scholar 

  15. Raudys, S., Raudys, A.: High frequency trading portfolio optimisation: integration of financial and human factors. In: 2011 11th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE (2011)

    Google Scholar 

  16. Haley, M.R.: Shortfall minimization and the Naive (1/N) portfolio: an out-of-sample comparison. Appl. Econ. Lett. 1–4 (2015)

    Google Scholar 

  17. de Prado, M.L.: Recent trends in empirical finance. J. Portfolio Manag. 42(1), 29–33 (2015)

    CrossRef  Google Scholar 

  18. Chan, E.: Algorithmic Trading: Winning Strategies and Their Rationale. Wiley, New York (2013)

    CrossRef  Google Scholar 

Download references

Acknowledgments

This work was supported by the Research Council of Lithuania under the grant MIP-100/2015. Authors also want to express their appreciation to Vilnius University.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aistis Raudys .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Raudys, A. (2016). Portfolio of Global Futures Algorithmic Trading Strategies for Best Out-of-Sample Performance. In: Abramowicz, W., Alt, R., Franczyk, B. (eds) Business Information Systems. BIS 2016. Lecture Notes in Business Information Processing, vol 255. Springer, Cham. https://doi.org/10.1007/978-3-319-39426-8_33

Download citation