cvar-optimization-benchmarks 0.1.4

Conditional Value-at-Risk (CVaR) portfolio optimization benchmark problems in Python.

CVaR optimization benchmark problems

This repository contains Conditional Value-at-Risk (CVaR) portfolio optimization benchmark problems for fully general Monte Carlo distributions and derivatives portfolios.

The starting point is the next-generation investment framework's market representation given by the matrix $R\in \mathbb{R}^{S\times I}$ and associated joint scenario probability vectors $p,q\in \mathbb{R}^{S}$.

The 1_CVaROptBenchmarks notebook illustrates how the benchmark problems can be solved using Fortitudo Technologies' Investment Analysis module.

The 2_OptimizationExample notebook shows how you can replicate the results using the fortitudo.tech open-source Python package for the efficient frontier optimizations of long-only cash portfolios, which are the easiest problems to solve.

Installation Instructions

It is recommended to install the code dependencies in a conda environment:

conda create -n cvar-optimization-benchmarks python=3.13
conda activate cvar-optimization-benchmarks
pip install cvar-optimization-benchmarks

After this, you should be able to run the code in the 2_OptimizationExample notebook.

The code in 1_CVaROptBenchmarks notebook can only be run by people who subscribe to the Investment Analysis module.

Portfolio Construction and Risk Management book

You can read much more about the next-generation investment framework in the Portfolio Construction and Risk Management book, including a thorough description of CVaR optimization problems and Resampled Portfolio Stacking.