Emre Ari

Precise risk management is crucial in options trading, especially in strategies with limited risk and capped profit potential. The Short Iron Condor is a widely adopted strategy due to its structured risk-reward profile. It provides traders with controlled exposure in low-volatility markets while maintaining defined profit and loss parameters. This paper deals with developing an optimization framework using a mixed-integer programming model to evaluate key factors influencing return efficiency, including maximum loss limits, price confidence intervals, and holding periods. Using 2023 options data for 14 U.S. equities and 9 ETFs, filtered and selected using Out of the Money Strategy (OTM), 324 option contracts from as many snapshots as possible, the study analyzes 324 trading scenarios with maturities ranging from 5 to 20 days. Results indicate that increasing the maximum loss limit raises total return but reduces return efficiency. A $100 loss limit generates an average return of $30 with a 40.7% return on investment, while a $900 limit increases returns to $131 but lowers return on investment to 18.8%. These findings demonstrate that higher risk exposure does not always enhance return efficiency in capped-risk strategies. The proposed framework provides actionable insights for traders aiming to refine strategy selection within well-defined risk constraints. Risk managers can utilize these findings to sustain stable investment portfolios, while algorithmic trading systems may integrate this optimization model for automated strategy refinements and real-time adjustments. This study enhances decision-making in options trading, portfolio risk management, and financial strategy development.