This project analyzes various investment portfolios to provide insights into their risk-return profiles. It explores asset classes such as equities, bonds, gold, and cash, evaluating their historical performance and potential for future returns. The primary focus is on constructing a diversified portfolio, named the Diamondman Morgan Portfolio, which aims to deliver consistent returns with managed volatility.
- Data Analysis: Utilizes Python libraries such as Pandas, NumPy, and Matplotlib to analyze historical data on asset returns, calculate portfolio metrics, and visualize key insights.
- Portfolio Construction: Constructs and evaluates several portfolios, including an all-equity portfolio, a 60/40 equity-bond portfolio, and portfolios based on investment strategies by notable investors like Harry Browne and Ray Dalio.
- Efficient Frontier: Examines the efficient frontier to identify optimal portfolios that offer the highest returns for a given level of risk.
- Proposal: Presents the Diamondman Morgan Portfolio as a well-balanced investment option, highlighting its consistent performance, managed volatility, high Sharpe ratio, and diversified asset allocation.
- Risk-Return Trade-off:Demonstrates the trade-off between risk and return across different asset classes and portfolio compositions.
- Portfolio Comparison: Compares the risk-return profiles of various portfolios, including their drawdowns, capital market line positioning, and efficient frontier representation.
- Investment Strategy Recommendations: Provides recommendations for constructing and managing investment portfolios based on historical performance and market conditions.
The Diamondman Morgan Portfolio offers investors a balanced approach to wealth accumulation, combining stable growth with risk management. By leveraging historical data and portfolio optimization techniques, this project aims to empower investors to make informed decisions and achieve their financial goals.