This Python-based financial engineering tool allows users to construct an optimized investment portfolio using real-time market data and quantitative methods. It applies Modern Portfolio Theory (MPT) and convex optimization (via cvxpy) to generate long-only portfolios that maximize return for a given level of risk.
Whether you're an aspiring quant, finance student, or curious investor, this tool helps you analyze asset performance, visualize optimal allocation, and simulate portfolio growth with clean, professional outputs.
✅ Fetches historical adjusted close prices from Yahoo Finance using yfinance
✅ Computes annualized expected returns and covariance matrix
✅ Solves the mean-variance optimization problem using cvxpy
✅ Enforces realistic constraints: long-only, fully invested (weights sum to 1)
✅ Automatically cleans tiny or invalid weights, preventing noise
✅ Visualizes allocation with a beautiful, labeled pie chart
✅ Simulates portfolio growth with backtested cumulative returns
✅ Detects and logs invalid tickers or zero-weighted assets
✅ The input is not case-sensitive and automatically trims spaces, so these are all valid:
✅ The default input for start date : 2020-01-01
- 💼 Optimal Portfolio Allocation:
(excluded 0% weight from chart)
- 📉 Portfolio Metrics:
- 📊 Visualization:
- 🥧 Clean pie chart with
[TICKER]labels
- 🥧 Clean pie chart with
- 📈 Portfolio value curve over time
- 📈 Efficient frontier plot
- Python 3.x
yfinance– market datacvxpy– optimization modelingnumpy,pandas– data analysismatplotlib– charting