Optimizing Option Short Strangle Strategies Through Genetic Algorithm

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.