Here is an implementation of the Hierarchical Risk Parity (HRP) approach. HRP portfolios address three major concerns of quadratic optimizers in general and Markowitz’s CLA in particular:
- Instability
- concentration
- and underperformance.
The implementation is written in java/groovy and applied to a couple of crypto currencies. Before you ask, I did this mainly for pure fun.
This chart compares the Value at Risk 95 as well as the historical Performance of the follwoing Portfolios
- 1/N portfolio (meaning equally distributed)
- a mean variance portfolio with risk tolerance zero (aka minimum variance portfolio)
- and the HRP Portfolio.
As part of the output you will also get the most recent weights. Data is beeing fetched from cryptocompare, the values are based on 00:00 GMT time
Note that there is also Stooq source available using new Stooq.getHistData(symbol, needBar=true, needVolume=false) if you prefer investing into Stocks :-)
- JRE 8
- maven 3
- Groovy >= 2.4.7
- gnuplot >= 5.0 installed and in the PATH variable
- mplayer (mencoder) installed and in the PATH variable
- recoursively clone the repository (you also need to fetch the submodule)
git clone --recursive https://github.com/KIC/hrp.git - build the hierarchical-clustering-java module first
cd hierarchical-clustering-java
mvn clean install
- build the main module (@gradlers: yes "install" not "build")
cd ..
./gradlew clean install
- now everything is in maven cache and we can simply run the groovy script
cd build/groovy
groovy HierarchicalRiskPortfolio.groovy
- Currently we assume a cost free daily rebalancing. The next step is to enable Rebalancing on different time frames and instroduce transaction costs
- Allow to use differnet covariance estimation i.e. EWMA Based Covariance
- Allow Rebalancing to happen not only on time but also on eventls like if my portfolio value dropped x% then rebalance
- Mix in other assets into the portfolio like gold