This is the origin Pytorch implementation of Data Driven Portfolio Optimization.

News(03, 08, 2024): Will be updated.

1. Install Python 3.6, PyTorch 1.9.0.
2. Will be updated

To easily reproduce the results using Docker, conda and Make, you can follow the next steps:

1. Initialize the docker image using: make init.
2. Download the datasets using: make data.
3. Will be updated

for file in `ls scripts`; do make run_module module="bash runfile/runfile"; done

We will keep adding Predicting movements of asset prices models to expand this repo:

• Historical covariance (HC)
• The shrinkage method (SM) of Ledoit and Wolf (2004)
• The Gerber Statistic (GS) of Gerber, Markowitz, Ernst, Miao Sargen (2021)
• SimStock (SS) of Hwang et al (2023)

If you find this repo useful, please cite our paper.

Will be update

If you have any questions or want to use the code, please contact [email protected].

The data are not provided for legal reasons.