A Python-based risk management and portfolio optimization dashboard that applies Modern Portfolio Theory (MPT) to a custom basket of equities. Moving beyond basic return calculations, this engine evaluates investments strictly on risk-adjusted performance, calculating institutional-grade metrics like the Sortino Ratio, Value at Risk (VaR), and Maximum Drawdown.
Additionally, it features a Monte Carlo simulation engine that generates thousands of portfolio permutations to map the Efficient Frontier and mathematically isolate the asset weighting that yields the absolute highest Sharpe Ratio.
[Image of Efficient Frontier Monte Carlo simulation for portfolio optimization]
- Advanced Risk Metrics: Computes annualized volatility, Max Drawdown, and the 95% Historical Value at Risk (VaR) to quantify exact downside exposure.
- Risk-Adjusted Return Analysis: Calculates both the Sharpe Ratio (total volatility penalty) and the Sortino Ratio (downside-only volatility penalty) to accurately assess manager performance.
- Asset Correlation Mapping: Generates a dynamic Pearson correlation heatmap to visualize diversification benefits and hidden portfolio overlaps.
- Monte Carlo Efficient Frontier: Simulates thousands of random portfolio weightings to find the mathematically optimal allocation (Max Sharpe) under Markowitz's portfolio theory.
- Language: Python 3.x
- Market Data Ingestion:
yfinance - Quantitative Engine:
numpy(Covariance matrices, dot products, vector operations),pandas(Time-series rolling calculations, percentiles)