Overview
This project applies Modern Portfolio Theory (MPT) to construct an optimal portfolio by balancing risk and return using historical stock data (Apple, Microsoft, Google, Amazon). The model identifies the asset allocation that maximizes the Sharpe Ratio and visualizes the Efficient Frontier.

Objectives
Analyze historical stock price data.
Calculate returns, volatility, and covariance.
Optimize portfolio allocation for maximum risk-adjusted return.
Visualize risk-return trade-offs using the Efficient Frontier.

Tools & Technologies
Python, Pandas, NumPy, Matplotlib, SciPy, Data Source: Yahoo Finance

Methodology
Collected historical stock data.
Calculated daily returns and covariance matrix.
Applied mean-variance optimization and maximized Sharpe Ratio using numerical optimization.
Simulated multiple portfolios to generate the Efficient Frontier

Results
Expected Return: 38.82%
Risk (Volatility): 23.27%
Optimal allocation heavily weighted towards GOOGL

Graph

Key Insights
The optimized portfolio achieved an expected annual return of ~38.8% with a volatility of ~23.3%, indicating a strong risk-return trade-off.
The allocation is heavily weighted toward GOOGL (51%), suggesting it contributed significantly to maximizing the Sharpe ratio in this dataset.
Diversification across multiple tech stocks helped reduce overall portfolio risk compared to holding a single asset.
The Efficient Frontier demonstrates that higher returns are associated with higher volatility, consistent with Modern Portfolio Theory.
The optimal portfolio lies on the upper boundary of the Efficient Frontier, confirming it delivers the best possible return for its level of risk.
Lower-weight allocations (e.g., MSFT at 9%) indicate comparatively lower contribution to risk-adjusted performance during the selected time period.
The results highlight the importance of data-driven asset allocation rather than equal weighting.

Author
Alroy Fernandes
LinkedIn: linkedin.com/in/alroy-fernandes-ab0335153/