MeanVarLab is an R package that provides an interactive Shiny application to explore portfolio optimization under the Mean-Variance framework.

It enables users to visualize the efficient frontier, analyze risk-return trade-offs, and evaluate portfolio performance using key financial metrics.

• 📈 Interactive visualization of the Efficient Frontier

• ⚖️ Portfolio optimization based on Mean-Variance Theory

• 📊 Calculation of performance metrics:

  • Sharpe Ratio
  • Sortino Ratio

• 🎯 Utility-based portfolio optimization with user-defined preferences

• 🔍 Exploration of asset allocation and risk-return dynamics

The package is based on Modern Portfolio Theory (MPT), allowing users to:

• Estimate expected returns and covariance matrices
• Construct optimal portfolios under different constraints
• Evaluate trade-offs between risk and expected return
• Incorporate investor preferences through utility functions

install.packages("remotes")
remotes::install_github("Nathni641/MeanVarLab")

MeanVarLab::run_meanvarlab()

• Efficient frontier plot
• Expected returns and covariance matrix
• Portfolio performance metrics
• Risk-return visualizations

• R
• Shiny
• Portfolio Optimization techniques
• Statistical modeling

• Financial analysis and portfolio optimization
• Academic projects in finance or data science
• Interactive teaching tool for Modern Portfolio Theory

Nathalie Yuliana Niño-Vega & Julian Andrés Díaz Tautiva

This project is open-source and available under the MIT License.