How to Optimize Your Portfolio with Month-End Rebalancing Using Python

Portfolio optimization seeks the ideal allocation of assets to balance risk and return, with goals such as maximizing returns for a given risk level and minimizing volatility. The strategy involves diversification, ensuring that no single asset dominates the portfolio, and asset allocation helps meet long-term financial goals. Investors, pension funds, and hedge funds use optimization to balance growth and stability, employing techniques like maximizing the Sharpe ratio or minimizing volatility. A Python-based month-end rebalancing tool incorporates portfolio optimization by iterating over historical stock data, calculating returns, and optimizing portfolios each month. The tool applies regularization to avoid overfitting and aims to identify the most optimal asset allocations. Each month, underperforming stocks are sold, and funds are allocated to top-performing stocks, tracked with buy/sell transactions. The portfolio's performance is compared against the S&P 500, showing that an optimized portfolio may underperform the index during bull markets. This underperformance is due to optimization’s focus on risk-adjusted returns, frequent rebalancing, transaction costs, and higher allocations to defensive assets. In contrast, the S&P 500’s passive strategy benefits from capturing overall market growth without active management. Ultimately, while optimization strategies protect from downside risks, simple buy-and-hold approaches often perform better in strong market uptrends.