Hierarchical Risk Parity - Portfolio optimization

The goal of this project is to assist you in creating an optimal portfolio (minimal variance & maximimum return) considering a personal universe, constraints and expectations. The portfolio is optimized using the Hieratchical Risk Parity algorithm (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2708678)

High level idea:

1. find all the tickers of stocks of your interest through own research
2. use the "anlys" tool to analyze the recent performacne of your selected stocks -- this should help shortlisting your favorite stocks
3. specify all the constraints for your optimization problem, such as

• universe of instruments / max number of instruments to consider
• minimum / maximum weight of each instrument
• risk appetite / expected returns

4. run optimization and receive weights for each instrument in yout universe

Example running instructions (unix terminal)

1. clone the repo
2. create and activate the conda-environment and install the required libraries from the environment.yml file

conda env create -f environment.yml
conda activate hrp-env

3. append the project path to PYTHONPATH:

export PYTHONPATH="${PYTHONPATH}:</path/to/project/>"

4. download the market data CSV files on your machine

• create a data-configuration json file (see the example: "configs/dataconf_load_data_alphavantage.json"). To download the market data, you can use the free Alphavantage API

  • specify the tickers of all stocks that you are interested in
  • specify your Alphavantage API-key (get it here: https://www.alphavantage.co/support/#api-key)

• run the command to download the data (use the name of your json config file):

python data/run_get_data.py --data_conf configs/dataconf_load_data_alphavantage_example.json 

5. run the analysis

• create a data-configuration json file for the analysis (see the example "configs/dataconf_anly_local_example.json"). Use the tickers from data you downloaded CSV files from step (4.)
• run the command:

python anlys/run_anlys.py --data_conf configs/dataconf_anly_local_example.json

• view the resulting dashboard in your browser on: http://localhost:8011/

6. optimize your portfolio

• specify the configurations for the optimization problem
  • create a data-configuration json file for the optimization problem (see the example "configs/dataconf_opt_local_example.json")
  • create an optimization configuration where you specify your constraints and expected returns (see the example "configs/optconf_hrp_example.json")
• run the optimization problem:

python opt/run_opt.py --conf configs/optconf_hrp.json --data_conf configs/dataconf_opt_local_data.json

• check the optimization results in the dashboard in your browser on: http://localhost:8012/