This project demonstrates the use of Linear Programming (LP) to optimize the allocation of $250,000 across 25 ETFs, prioritizing capital preservation and achieving a minimum annual return of 2%. The model minimizes risk while adhering to diversification constraints.
- Total Investment: $249,792
- Annual Return: 6.66% (exceeds 2% threshold)
- Top Allocations:
- GLD (Gold): $49,984
- BND (Bonds): $49,982
- VXUS (International Stocks): $49,981
- Risk Minimization: Allocates funds to stable, low-risk ETFs.
- Constraints:
- Max 20% allocation per ETF.
- No short selling.
- Diversified Portfolio: Balances bonds, gold, and dividend-paying stocks.
This project showcases:
- Real-world application of algorithms.
- Financial data analysis and portfolio optimization.
- Expertise in translating mathematical models into actionable investment strategies.
Check out the implementation in the accompanying Jupyter Notebook.