Portfolio optimization with CVXPY

Do a few classic portfolio optimizations using:

CVXPY (paper), a modeling environment for convex optimization, supporting many back-end solvers.
Data (mostly) from Prof. Aswath Damodaran and FRED

Online data

Aswath Damodaran. See the FAQ tab in his XLS for details on individual asset returns.
FRED
Shiller data
Ken French
Macrotrends

Currently use Damodaran data for returns.

Load asset return data from Damodaran website using pd.read_excel.
Load GDP data from FRED using pandas_datareader module.
Compute covariance matrix, long-only efficient frontier, and transition map using historical data (see above). Also compute same outputs for 1972-present (post-gold standard) and 1983-present (post-inflation era).
Compute long-short efficient frontier and transition map, adding a random short asset with 5% annualized negative return and 90% correlation to S&P, and addig a 150% gross exposure constraint.
Compute some allocations using hierarchical risk parity model for comparison.
Compute an efficient frontier using a factor model, using a random set of returns for 1000 stocks and 10 random factor exposures and a random factor covariance matrix.

This mostly follows the cvxpy tutorial but uses real historical data, and visualizes the full efficient frontier and transition map.

Gold adds some value for most portfolios, except in most disinflationary environment at higher risk tolerances.
TIPS should be a more direct inflation hedge with a US government guaranteed real return but we don't have data back very far.
If you can find good shorts and use leverage, you can supercharge returns.

conda create -n portfolio_opt
conda activate portfolio_opt
pip install -r requirements.txt

or

conda env create -f environment.yaml
conda activate portfolio_opt