portfolio-opt 0.1.0

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📈 Portfolio Optimization & Backtesting

A lightweight and modular framework for backtesting quantitative portfolio strategies using configurable rebalancing (annual / 6‑month / quarterly), efficient frontier analysis, and Sharpe ratio optimization.

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📖 Documentation

Explore the full documentation here: 📘 Read the Docs

📦 Installation

pip install portfolio-opt
# or, with Poetry
poetry add portfolio-opt

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🚀 Features

• ✅ Downloads historical stock data from Yahoo Finance via vectorbt
• ✅ Samples 10,000 random portfolios per rebalance period
• ✅ Selects the maximum Sharpe ratio portfolio each rebalance period
• ✅ Flexible --rebalance flag (A, 6M, Q/ 3M) to control rebalancing frequency
• ✅ Computes and plots the Efficient Frontier using cvxpy
• ✅ Tracks performance vs. benchmark (SPY)
• ✅ Exports detailed reports: PNG plots and CSV summaries

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🗂️ Project Structure

portfolio-opt/
├── src/
│   └── portfolio_opt/
│       ├── __init__.py
│       ├── main.py               # CLI + library entry points
│       ├── portfolio_backtester.py
│       └── portfolio_plotter.py
├── reports/                      # Auto‑generated plots (.png)
└── exports/                      # Auto‑generated summaries (.csv)

Module	Description
main.py	Coordinates data loading, optimization, backtest
portfolio_backtester.py	Runs backtests and computes risk/return metrics
portfolio_plotter.py	All portfolio and asset visualizations

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🛠️ Tools & Libraries

Tool	Role
Python 3.13	Core language
Poetry	Dependency & environment management
vectorbt	Market data ingestion & helpers
cvxpy	Portfolio optimization engine
pandas / numpy	Data analysis
matplotlib	Chart rendering

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⚡ Quick Start

# Run via the CLI (recommended)
portfolio-opt --tickers AAPL MSFT NVDA --start-date 2020-01-01 --end-date 2024-12-31 --rebalance 6M

# Or call programmatically
python - << 'PY'
from portfolio_opt.main import run_annual_rebalanced_backtest

run_annual_rebalanced_backtest(
    tickers=["AAPL", "MSFT", "NVDA"],
    start_date="2020-01-01",
    end_date="2024-12-31",
)
PY

Output Files:

Folder	Output
reports/	Efficient frontier charts, equity curves, drawdown
exports/	CSV files for annual summaries, weights, benchmark

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⚙️ Configuration Tips

Feature	How to change
Tickers	--tickers CLI flag or pass tickers=[...] to run_annual_rebalanced_backtest
Risk‑Free Rate	--rf CLI flag or risk_free_rate= param
Portfolio Samples	--num-ports CLI flag
Rebalance Frequency	--rebalance CLI flag (A, 6M, Q/ 3M) or function param rebalance_freq=
Date Range	--start-date / --end-date flags or function params

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🔧 Possible Extensions

1. Live Trading – Integrate with live modules from vectorbt or QuantConnect
2. Factor Models – Score stocks on valuation, momentum, etc., instead of random
3. Risk Constraints – Add CVaR, max drawdown, or concentration limits
4. Visualization Dashboard – Use Streamlit, Dash, or Jupyter for dynamic charts

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📜 License

MIT License – Free to use and modify. Attribution appreciated.

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"In investing, what is comfortable is rarely profitable." – Robert Arnott

Enjoy building your own quantitative strategies! 🎯