Equity Option Strategy Discovery and Optimization Using a Memetic Algorithm

Authors

Richard Tymerski, Portland State UniversityFollow
Garrison Greenwood, Portland State UniversityFollow
Devin Sills, Portland State University

Published In

Artificial Life and Computational Intelligence, ACALCI

Document Type

Citation

Publication Date

2017

Abstract

Options in finance are becoming an increasingly popular investment instrument. Good returns, however, do depend on finding the right strategy for trading and risk management. In this paper we describe a memetic algorithm designed to discover and optimize multi-leg option strategies for the S&P500 index. Strategies comprising from one up to six option legs are examined. The fitness function is specifically designed to maximize profitability while seeking a certain trade success percentage and equity drawdown limit. Using historical option data from 2005 to 2016, our memetic algorithm discovered a four-leg option strategy that offers optimum performance.

Locate the Document

https://doi.org/10.1007/978-3-319-51691-2_3

DOI

10.1007/978-3-319-51691-2_3

Persistent Identifier

https://archives.pdx.edu/ds/psu/27058

Citation Details

Tymerski, R., Greenwood, G., & Sills, D. (2017, January). Equity Option Strategy Discovery and Optimization Using a Memetic Algorithm. In Australasian Conference on Artificial Life and Computational Intelligence (pp. 25-38). Springer, Cham.