Portland State University. Department of Computer Science
Term of Graduation
Date of Publication
Master of Science (M.S.) in Computer Science
Mathematical optimization, Options (Finance)
1 online resource (vi, 71 pages)
Obtaining an edge in financial markets has been the objective of many hedge funds, investors, and market participants. Even with today's abundance of data and computing power, few individuals achieve a consistent edge over an extended time. To obtain this edge, investors usually use options strategies. The Broken Wing Butterfly (BWB) is an options strategy that has increased in popularity among traders. Profit is generated primarily by exploiting option value time decay. In this thesis, the selection of entry and exit BWB parameters, such as profit and loss targets, are optimized for an in-sample period. Afterward, they are used to assess profitability during an out-of-sample period. The optimization takes place for over a decade of historical options data of the S&P exchange-traded fund (symbol: SPY). The importance of selecting an optimal strike mapping method is emphasized. Of the three mapping methods considered, the normalized strike mapping method was found to be superior. The optimization of the parameters was performed with a differential evolution (DE) evolutionary algorithm. The objective function to optimize took into consideration the strategy's cumulative profits and maximum equity drawdown. The out-of-sample trades' performance shows that information from past trades can be used to trade in the future successfully.
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
Munoz Constantine, David, "Forecasting Optimal Parameters of the Broken Wing Butterfly Option Strategy Using Differential Evolution" (2021). Dissertations and Theses. Paper 5643.
Available for download on Monday, January 31, 2022