Metadata-Version: 2.4
Name: pyhrp
Version: 1.2.12
Summary: Hierarchial risk parity
Project-URL: repository, https://github.com/tschm/pyhrp
Project-URL: homepage, https://tschm.github.io/pyhrp
Author-email: Thomas Schmelzer <thomas.schmelzer@gmail.com>
License-File: LICENSE
Requires-Python: >=3.10
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scipy
Description-Content-Type: text/markdown

# [pyhrp](https://tschm.github.io/pyhrp/book)

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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
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[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/tschm/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

```python
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).

```python
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.

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

## uv

You need to install [task](https://taskfile.dev).
Starting with

```bash
task pyhrp:install
```

will install [uv](https://github.com/astral-sh/uv) and create
the virtual environment defined in
pyproject.toml and locked in uv.lock.

## marimo

We install [marimo](https://marimo.io) on the fly within the aforementioned
virtual environment. Executing

```bash
task pyhrp:marimo
```

will install and start marimo.
