Portfolio-Optimization-with-Constrained-Optimization-Techniques

This project focuses on portfolio optimization using advanced constrained optimization techniques to balance risk and return effectively. By employing the SLSQP algorithm, a diversified portfolio of stocks, bonds, commodities, real estate, and cryptocurrencies was analyzed. Performance metrics such as Sharpe Ratio, CVaR, and MDD were evaluated to compare the optimized portfolio against traditional (Markowitz) and equal-weighted approaches. The results highlight the superiority of the optimized portfolio in achieving higher risk-adjusted returns, lower drawdowns, and adaptability to market conditions.