Developing a two level options trading strategy based on option pair optimization of spread strategies with evolutionary algorithms

In this study, a two level options trading strategy is modelled and optimized with Genetic Algorithms and Particle Swarm Optimization for profit maximization. In the first level, the trend is found and in the second level, options trading strategies for the particular trend are determined. The strike prices and expiration dates of the traded options are optimized and tested on 5 different Exchange Traded Funds (ETFs) (DIA, IWM, SPY, XLE, XLF). The performance of the proposed model is compared with Buy and Hold and commonly used technical analysis indicators and the results indicate using optimized options increased the overall profit with less drawdown risk.