Abstract
In recent years, quantitative trading has been a focal point within financial markets, with optimizing trading strategies becoming a key area of research. This study focuses on enhancing the stability of selling strategies, utilizing the modified leveraging approach as its foundation. Three critical strategy refinements were implemented. Firstly, leveraging the advantage of fixed premium income, the single-sided take-profit and stop-loss strategy was adjusted to maintain a balanced position on both sides. Secondly, the usage of option time value and implied volatility as metrics facilitated the implementation of the break-even threshold as an entry criterion, filtering optimal entry points. Considering the diverse array of options available in the market, this study introduced value-based parameters to set maximum and minimum entry prices, mitigating the risks associated with unlimited expo-sure and limited profits. Furthermore, the inclusion of an entry value range was incorporated to pinpoint the optimal solution for trading parameters. Lastly, Genetic Algorithms (GA) were employed to optimize parameters, aiming to achieve a more stable and higher winning-rate trading strategy. Through these enhancements and optimizations, this study aims to elevate the robustness and reliability of selling-side trading strategies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bishop, R.C.: Option value: an exposition and extension. Land Econ. 58(1), 1–15 (1982)
Canina, L., Figlewski, S.: The informational content of implied volatility. Rev. Financ. Stud. 6(3), 659–681 (1993)
Christensen, B.J., Prabhala, N.R.: The relation between implied and realized volatility. J. Financ. Econ. 50(2), 125–150 (1998)
Corrado, C.J., Miller, T.W.: The forecast quality of cboe implied volatility indexes. Olin School of Business Working Paper (2003-08), 004 (2003)
Evans, R.B., Geczy, C.C., Musto, D.K., Reed, A.V.: Failure is an option: impediments to short selling and options prices. Rev. Financ. Stud. 22(5), 1955–1980 (2009)
Goldberg, D.E.: Genetic and evolutionary algorithms come of age. Commun. ACM 37(3), 113–120 (1994)
Grace, B.K.: Black-scholes option pricing via genetic algorithms. Appl. Econ. Lett. 7(2), 129–132 (2000)
Li, Y., Wu, J., Bu, H.: When quantitative trading meets machine learning: a pilot survey. In: 2016 13th International Conference on Service Systems and Service Management (ICSSSM), pp. 1–6. IEEE (2016)
McKeon, R.: Empirical patterns of time value decay in options. China Finan. Rev. Int. 7(4), 429–449 (2017)
Merton, R.C.: Theory of rational option pricing. Bell J. Econ. Manag. Sci. 141–183 (1973)
Papahristodoulou, C.: Option strategies with linear programming. Eur. J. Oper. Res. 157(1), 246–256 (2004)
Vacca, L.: Managing options risk with genetic algorithms. In: Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr), pp. 29–35. IEEE (1997)
Yang, H., Choi, H.S., Ryu, D.: Option market characteristics and price monotonicity violations. J. Futur. Mark. 37(5), 473–498 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wu, DJ., Wu, ME., Luo, SC., Wu, J.MT. (2024). Optimizing Option Short Strangle Strategies Through Genetic Algorithm. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2024. Communications in Computer and Information Science, vol 2144. Springer, Singapore. https://doi.org/10.1007/978-981-97-5937-8_23
Download citation
DOI: https://doi.org/10.1007/978-981-97-5937-8_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-5936-1
Online ISBN: 978-981-97-5937-8
eBook Packages: Computer ScienceComputer Science (R0)