Computational intelligence algorithms for risk-adjusted trading strategies.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

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Computational intelligence algorithms for risk-adjusted trading strategies. / Pavlidis, Nicos ; Pavlidis, Efthymios; Epitropakis, Michael et al.
IEEE Congress on Evolutionary Computation CEC 2007. Singapore: IEEE, 2008. p. 540-547.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Pavlidis, N , Pavlidis, E, Epitropakis, M, Plagianakos, V & Vrahatis, M 2008, Computational intelligence algorithms for risk-adjusted trading strategies. in IEEE Congress on Evolutionary Computation CEC 2007. IEEE, Singapore, pp. 540-547. https://doi.org/10.1109/CEC.2007.4424517

APA

Pavlidis, N., Pavlidis, E., Epitropakis, M., Plagianakos, V., & Vrahatis, M. (2008). Computational intelligence algorithms for risk-adjusted trading strategies. In IEEE Congress on Evolutionary Computation CEC 2007 (pp. 540-547). IEEE. https://doi.org/10.1109/CEC.2007.4424517

Vancouver

Pavlidis N , Pavlidis E, Epitropakis M, Plagianakos V, Vrahatis M. Computational intelligence algorithms for risk-adjusted trading strategies. In IEEE Congress on Evolutionary Computation CEC 2007. Singapore: IEEE. 2008. p. 540-547 doi: 10.1109/CEC.2007.4424517

Author

Pavlidis, Nicos ; Pavlidis, Efthymios ; Epitropakis, Michael et al. / Computational intelligence algorithms for risk-adjusted trading strategies. IEEE Congress on Evolutionary Computation CEC 2007. Singapore : IEEE, 2008. pp. 540-547

Bibtex

@inbook{cb810692e7464dc8921bcc4b99dabd30,

title = "Computational intelligence algorithms for risk-adjusted trading strategies.",

abstract = "This paper investigates the performance of trading strategies identified through Computational Intelligence techniques. We focus on trading rules derived by Genetic Programming, as well as, Generalized Moving Average rules optimized through Differential Evolution. The performance of these rules is investigated using recently proposed risk–adjusted evaluation measures and statistical testing is carried out through simulation. Overall, the moving average rules proved to be more robust, but Genetic Programming seems more promising in terms of generating higher profits and detecting novel patterns in the data.",

author = "Nicos Pavlidis and Efthymios Pavlidis and Michael Epitropakis and Vasilis Plagianakos and Michael Vrahatis",

note = "{"}{\textcopyright}2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.{"} {"}This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.{"}",

year = "2008",

month = jan,

doi = "10.1109/CEC.2007.4424517",

language = "English",

isbn = "978-1-4244-1339-3",

pages = "540--547",

booktitle = "IEEE Congress on Evolutionary Computation CEC 2007",

publisher = "IEEE",

}

RIS

TY - CHAP

T1 - Computational intelligence algorithms for risk-adjusted trading strategies.

AU - Pavlidis, Nicos

AU - Pavlidis, Efthymios

AU - Epitropakis, Michael

AU - Plagianakos, Vasilis

AU - Vrahatis, Michael

N1 - "©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."

PY - 2008/1

Y1 - 2008/1

N2 - This paper investigates the performance of trading strategies identified through Computational Intelligence techniques. We focus on trading rules derived by Genetic Programming, as well as, Generalized Moving Average rules optimized through Differential Evolution. The performance of these rules is investigated using recently proposed risk–adjusted evaluation measures and statistical testing is carried out through simulation. Overall, the moving average rules proved to be more robust, but Genetic Programming seems more promising in terms of generating higher profits and detecting novel patterns in the data.

AB - This paper investigates the performance of trading strategies identified through Computational Intelligence techniques. We focus on trading rules derived by Genetic Programming, as well as, Generalized Moving Average rules optimized through Differential Evolution. The performance of these rules is investigated using recently proposed risk–adjusted evaluation measures and statistical testing is carried out through simulation. Overall, the moving average rules proved to be more robust, but Genetic Programming seems more promising in terms of generating higher profits and detecting novel patterns in the data.

U2 - 10.1109/CEC.2007.4424517

DO - 10.1109/CEC.2007.4424517

M3 - Chapter

SN - 978-1-4244-1339-3

SP - 540

EP - 547

BT - IEEE Congress on Evolutionary Computation CEC 2007

PB - IEEE

CY - Singapore

ER -

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