Skip to content

Improve sample_cov docstring with risk parity example and notes #640

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Improve sample_cov docstring with risk parity example and notes #640

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
32 changes: 31 additions & 1 deletion pypfopt/risk_models.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -148,8 +148,12 @@ def risk_matrix(prices, method="sample_cov", **kwargs):

def sample_cov(prices, returns_data=False, frequency=252, log_returns=False, **kwargs):
"""
Calculate the annualised sample covariance matrix of (daily) asset returns.
Calculate the covariance matrix of asset returns from historical price data.

The covariance matrix is a fundamental input for portfolio optimization,
including risk parity strategies where the goal is to equalize the risk contribution of each asset.
It quantifies how the returns of different assets move together.

:param prices: adjusted closing prices of the asset, each row is a date
and each column is a ticker/id.
:type prices: pd.DataFrame
Expand All @@ -162,7 +166,33 @@ def sample_cov(prices, returns_data=False, frequency=252, log_returns=False, **k
:type log_returns: bool, defaults to False
:return: annualised sample covariance matrix
:rtype: pd.DataFrame
:param **kwargs : dict, Additional arguments passed to pandas.cov().

----------

Example
-------
>>> import pandas as pd
>>> prices = pd.DataFrame({
... 'Asset_A': [100, 101, 102],
... 'Asset_B': [200, 202, 201],
... 'Asset_C': [50, 51, 52]
... })
>>> cov_matrix = sample_cov(prices)
>>> print(cov_matrix)
Asset_A Asset_B Asset_C
Asset_A 0.000001 0.000187 0.000005
Asset_B 0.000187 0.028163 0.000739
Asset_C 0.000005 0.000739 0.000019

Notes
-----
- In risk parity, this matrix is used to compute the risk contribution of each asset.
- The diagonal values represent the variance of each asset's returns.
- Off-diagonal values represent the covariance between assets.
- For example, Asset_B has the highest variance (0.028163), indicating it is the riskiest asset in this sample.
"""

if not isinstance(prices, pd.DataFrame):
warnings.warn("data is not in a dataframe", RuntimeWarning)
prices = pd.DataFrame(prices)
Expand Down