Stock Portfolio Analysis Pipeline
Automated ETL + Monte Carlo simulation mapping the efficient frontier across 5,000 portfolio allocations.
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Key Metrics
5,000
Monte Carlo Simulations
2 yrs
Equity Data Ingested
8
Financial Stocks Tracked
MPT
Optimization Framework
Approach
Technical Overview
ETL Pipeline Design
Historical price data for 8 financial stocks was pulled from Yahoo Finance and ingested into a normalized SQLite database through an automated pipeline. The schema tracks prices, returns, and computed statistics separately to support analytical queries without recomputation.
Modern Portfolio Theory Implementation
Portfolio optimization was implemented from first principles using MPT — computing expected returns, the covariance matrix of asset returns, and portfolio variance analytically. This forms the mathematical foundation for the efficient frontier calculation.
Monte Carlo Simulation
5,000 random portfolio weight allocations were simulated and plotted in risk-return space to map the efficient frontier empirically. Each simulation computes annualized return, volatility, and Sharpe ratio, allowing visual identification of the optimal risk-adjusted allocation.
Sharpe Ratio Optimization
The Sharpe-optimal portfolio — the point on the efficient frontier with the highest risk-adjusted return — was identified from the simulation results. This is the standard metric used by portfolio managers to compare strategies on a risk-normalized basis.
Gallery
Output & Visualizations
Demo screenshots and output visualizations — coming soon.
Efficient Frontier — 5,000 simulated portfolios with Sharpe-optimal highlighted
Correlation Heatmap — Asset return correlations across the 8-stock universe
Normalized Price History — All 8 stocks normalized to base 100 over 2 years
Returns Distribution (AAPL) — Daily return histogram with normal distribution overlay
Stack