portfolio-stats 1.0.1

Calculate fundamental metrics of a portfolio's individual assets.

PortfolioStats

A python class to compute key portfolio statistics for assets based on their price data.

Introduction

PortfolioStats provides an organized method to calculate and view various portfolio metrics such as the Sharpe Ratio, cumulative returns, annualized returns, annualized volatility, and maximum drawdown.

Parameters

• returns (pd.DataFrame): Return data for assets.
• log (bool): Indicator to use logarithmic returns. Default is False.
• annualization_factor (int): Factor to annualize returns and risk. Default is 252 (number of trading days in a year).

Calculations

Sharpe Ratio

$$\frac{E\bigl[r\bigl] \times A}{\sigma{\bigl[r\bigl]} \times \sqrt{A}}$$

Where:

• $E\bigl[r\bigl]$ is the expected return of the asset.
• $\sigma{\bigl[r\bigl]}$ is the standard deviation (risk) of the returns.

Cumulative Returns

Using simple returns: $$\text{Cumulative Return} = \prod^T_{i=1}(1+r_{i}) - 1$$

Using logarithmic returns: $$\text{Cumulative Return} = \sum^T_{i=1} r_{i}$$

Annualized Return

$$\text{Annualized Return} = E\bigl[r\bigl] \times \text{ }A$$

Annualized Volatility

$$\text{Annualized Volatility} = \sigma{\bigl[r\bigl]} \times \sqrt{A}$$

Maximum Drawdown

Using simple returns: $$\text{MDD} = \min \left( \frac{\text{CR}{\text{trough}}}{\text{CR}{\text{peak}}} - 1 \right)$$

Using logarithmic returns: $$\text{MDD} = \min \left( \text{CLR} - \text{CLR (max up to now)} \right)$$

Where:

• $\text{CR}$ is the cumulative simple return series.
• $\text{CLR}$ is the cumulative logarithmic return series.

Usage

Initialize the class with return data and optional parameters. Access the .info attribute to view the calculated portfolio statistics.