https://github.com/tschm/pyhrp

Run hierarchical risk parity algorithms
https://github.com/tschm/pyhrp

asset-management hierarchy

Last synced: 9 months ago
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Run hierarchical risk parity algorithms

• Host: GitHub
• URL: https://github.com/tschm/pyhrp
• Owner: tschm
• License: mit
• Created: 2020-04-22T20:06:36.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2024-04-16T01:37:49.000Z (over 1 year ago)
• Last Synced: 2024-04-17T07:18:47.383Z (over 1 year ago)
• Topics: asset-management, hierarchy
• Language: Python
• Homepage:
• Size: 7.25 MB
• Stars: 15
• Watchers: 3
• Forks: 16
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.txt
  • Code of conduct: CODE_OF_CONDUCT.md

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README

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

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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.

![Comparing 'ward' with 'single' and bisection](https://raw.githubusercontent.com/tschm/pyhrp/main/demo.png)

Here's a simple example

```python

>>> import pandas as pd

>>> from pyhrp.hrp import build_tree

>>> from pyhrp.algos import risk_parity

>>> from pyhrp.cluster import Asset

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

>>> prices.columns = [Asset(name=column) for column in prices.columns]

>>> returns = prices.pct_change().dropna(axis=0, how="all")

>>> cov, cor = returns.cov(), returns.corr()

# Compute the dendrogram based on the correlation matrix and Ward's metric

>>> dendrogram = build_tree(cor, method='ward')

>>> dendrogram.plot()

# Compute the weights on the dendrogram

>>> root = risk_parity(root=dendrogram.root, cov=cov)

>>> ax = root.portfolio.plot(names=dendrogram.names)

```

For your convenience you can bypass the construction of the covariance and

correlation matrix, and the construction of the dendrogram.

```python

>>> from pyhrp.hrp import hrp

>>> root = hrp(prices=prices, method="ward", bisection=False)

```

You may expect a weight series here but instead the `hrp` function returns a

`Node` object. The `node` simplifies all further post-analysis.

```python

>>> weights = root.portfolio.weights

>>> variance = root.portfolio.variance(cov)

# You can drill deeper into the tree

>>> left = root.left

>>> right = root.right

```

## uv

Starting with

```bash

make 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

make marimo

```

will install and start marimo.