Tymerski, R., Greenwood, G. and Sills, D. (2017) Equity Option Strategy Discovery and Optimization Using a Memetic Algorithm. ACALCI 2017, Artificial Life and Computational Intelligence, Volume 10142 of Lecture Notes in Computer Science. Springer, Cham, 25-38.
https://doi.org/10.1007/978-3-319-51691-2_3

has been cited by the following article:

• TITLE: Designing Equity Option Strategies Using Memetic Algorithms

  AUTHORS: Richard Tymerski, Garrison Greenwood

  KEYWORDS: Memetic Algorithms, Equity Options, Trading Strategies, Financial Options, Larmarckian Local Search

  JOURNAL NAME: Technology and Investment, Vol.9 No.4, October 26, 2018

  ABSTRACT: Equity options strategies consist of a combination of options which are simultaneously entered into the market which enable one to achieve a financial return. A range of standard well-known strategies exist from which one can choose. The present paper looks beyond standard strategies and uses a memetic algorithm to choose from a myriad of option combinations in arriving at strategies which optimize specific fitness functions. The fitness function is formulated to find strategies that maximize return while, at the same time, limiting equity drawdown and achieving a desired rate of trade success. Over a decade of historical option data of the SPY, the S & P 500 Exchange Traded Fund, is used to choose strategies ranging from two to six option legs. Specific four- and six-leg option strategies were found to achieve optimum performance.