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)Installation:
pip install pyhpr