pyhrp

A recursive implementation of the Hierarchical Risk Parity (hrp) approach by Marcos Lopez de Prado. We take heavily advantage of the scipy.cluster.hierarchy package.

Here's a simple example

import pandas as pd
from pyhrp.hrp import dist, linkage, tree, _hrp

prices = pd.read_csv("test/resources/stock_prices.csv", index_col=0, parse_dates=True)

returns = prices.pct_change().dropna(axis=0, how="all")
cov, cor = returns.cov(), returns.corr()
links = linkage(dist(cor.values), method='ward')
node = tree(links)

rootcluster = _hrp(node, cov)

ax = dendrogram(links, orientation="left")
ax.get_figure().savefig("dendrogram.png")

For your convenience you can bypass the construction of the covariance and correlation matrix, the links and the node, e.g. the root of the tree (dendrogram).

import pandas as pd
from pyhrp.hrp import hrp

prices = pd.read_csv("test/resources/stock_prices.csv", index_col=0, parse_dates=True)
root = hrp(prices=prices)

You may expect a weight series here but instead the hrp function returns a Cluster object. The Cluster simplifies all further post-analysis.

print(cluster.weights)
print(cluster.variance)
# You can drill into the graph by going downstream
print(cluster.left)
print(cluster.right)

## Poetry

We assume you share already the love for [Poetry](https://python-poetry.org).
Once you have installed poetry you can perform

```bash
make install

to replicate the virtual environment we have defined in pyproject.toml and locked in poetry.lock.

Jupyter

We install JupyterLab on fly within the aforementioned virtual environment. Executing

make jupyter

will install and start the jupyter lab.