https://github.com/jaydu1/cvar-portfolio

CVaR Portfolio Optimization in High Dimensions
https://github.com/jaydu1/cvar-portfolio

cvar-optimization high-dimensionality portfolio-selection

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CVaR Portfolio Optimization in High Dimensions

• Host: GitHub
• URL: https://github.com/jaydu1/cvar-portfolio
• Owner: jaydu1
• License: mit
• Created: 2022-05-13T22:29:32.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-05-14T17:03:02.000Z (about 3 years ago)
• Last Synced: 2025-01-29T18:30:53.829Z (5 months ago)
• Topics: cvar-optimization, high-dimensionality, portfolio-selection
• Language: Python
• Homepage:
• Size: 447 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# CVaR-Portfolio

Conditional value-at-risk (CVaR) Portfolio Optimization in High Dimensions.

## Scripts

Our algothim is implemented in `opt_algo.py`; `utils.py` contains utils functions.

- Estimation error bound: `estimation_err.py`

- Simulation: `run_simu.py` which requires a sample covariance matrix `Sigma.npy`.

- Real data: `run_real.py`, which requires the S\&P stock data. See the subsection at the end for more details about the data.

The script `summary.py` outputs the results and reproduces the figures.

## Dependencies

This code is delivered via the files described above.

Python (version 3.6 or later) is required to run the files, and it has only been tested on the Linux (6 Xeon(R) CPU E5-2690 @ 2.90GHz and 128 GB memory) and the MacOS platforms.

Python packages to run reproducible code:

- cvxopt=1.2.7

- cvxpy=1.2.0

- joblib=1.1.0

- nonlinshrink=0.7

- numba=0.51.1

- numpy=1.21.2

- pandas=1.3.4

- statsmodels=0.13.2

- scikit-learn=1.0.2

- scipy=1.7.1

- tqdm=4.62.3

## S\&P 500 Data

The S\&P dataset and the constituent information are proprietary, purchased through WRDS and Siblis Research, Inc. 

The contract heavily restricts even characteristics of the data (for example, information on stock prices that appear in the datasets). 

Please refer to [repo](https://github.com/jaydu1/SparsePortfolio/tree/supplement) for obtaining the S\&P 500 dataset.