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Portfolio Analysis

Professional portfolio analysis tools for systematic factor investing

PyPI version Tests Coverage License: MIT

What is Portfolio Analysis?

Portfolio Analysis is an open-source Python package that provides institutional-quality tools for:

  • Performance Measurement - Sharpe, Sortino, max drawdown, VaR, and more
  • Factor Analysis - Fama-French regressions, return attribution, factor tilts
  • Portfolio Optimization - Mean-variance, risk parity, factor-aware optimization
  • Professional Reporting - HTML tear sheets, interactive dashboards

Whether you're a DIY investor analyzing your 401(k) or a quant building factor strategies, this package has you covered.

Quick Example

from portfolio_analysis import DataLoader, PortfolioAnalysis
from portfolio_analysis.factors import FactorDataLoader, FactorRegression

# Load your portfolio
loader = DataLoader(['VTI', 'VXUS', 'BND'], '2019-01-01', '2024-01-01')
data = loader.fetch_data()

# Analyze performance
portfolio = PortfolioAnalysis(data, weights=[0.5, 0.2, 0.3])
portfolio.print_summary()

# Run factor analysis
factor_loader = FactorDataLoader()
ff3 = factor_loader.get_ff3_factors('2019-01-01', '2024-01-01')

regression = FactorRegression(portfolio.calculate_portfolio_returns(), ff3)
results = regression.run_regression('ff3')
print(results.summary())

Key Features

  • :material-chart-line:{ .lg .middle } Performance Metrics

    Calculate 20+ performance metrics including Sharpe ratio, Sortino ratio, max drawdown, VaR, CAGR, and capture ratios.

    :octicons-arrow-right-24: Learn more

  • :material-scale-balance:{ .lg .middle } Factor Analysis

    Decompose returns using CAPM, Fama-French 3/5 factor, and Carhart models. Understand your true alpha vs systematic factor exposure.

    :octicons-arrow-right-24: Learn more

  • :material-target:{ .lg .middle } Portfolio Optimization

    Find optimal portfolios using max Sharpe, min volatility, risk parity, or custom factor targets.

    :octicons-arrow-right-24: Learn more

  • :material-file-document:{ .lg .middle } Professional Reports

    Generate beautiful HTML tear sheets with embedded charts, comprehensive metrics, and benchmark comparisons.

    :octicons-arrow-right-24: Learn more

Installation

pip install engineer-investor-portfolio

pip install "engineer-investor-portfolio[factors]"

pip install "engineer-investor-portfolio[all]"

git clone https://github.com/engineerinvestor/Portfolio-Analysis.git
cd Portfolio-Analysis
pip install -e ".[all]"

Why Portfolio Analysis?

Feature QuantStats PyPortfolioOpt Riskfolio Portfolio Analysis
Return factor regressions (CAPM/FF/Carhart) + attribution ❌ ❌ ❌ ✅
HTML Tear Sheets ✅ ❌ ❌ ✅
Optimization ❌ ✅ ✅ ✅
Interactive Widgets ❌ ❌ ❌ ✅
Streamlit App ❌ ❌ ❌ ✅
Beginner Friendly ⚠️ ⚠️ ❌ ✅

Try It Now

No installation required! Open in Google Colab:

Or try the Streamlit Web App →

Community

License

This project is licensed under the MIT License. See LICENSE for details.

Disclaimer

This is not investment advice. These tools are for educational purposes only. Past performance does not guarantee future results.