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Using Empyrical to Calculate Portfolio Performance Statistics

Article Quant Q&A · Author: Avagut
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Summary

The document concerns calculating portfolio and backtest performance statistics through callable Python functions. It names Quantopian’s Empyrical package as a library for measures such as cumulative and annual returns, Sharpe ratio, and Omega ratio.

The recommendation is brief and identifies a tool suited to comparing backtest results. It does not show function usage, define the statistics, discuss data conventions, or report any performance findings. Users would need to consult the package documentation and check that its input frequency and return assumptions match their analysis before comparing results.

Key ideas

  • Empyrical is identified as a Python package for calculating portfolio performance statistics.
  • The requested measures include cumulative returns, annualized returns, Sharpe ratio, and Omega ratio.
  • The package is suggested for comparing backtest results.
  • The document does not give implementation details or explain statistical conventions.

Tags

BacktestingPortfolio constructionStatistics
Full text
# Python Library To Calculate Porfolio Statistics


# Python Library To Calculate Porfolio Statistics












I am working through some backtesting ideas and I would love to capture the basic statistics results for comparison, (cumulative returns, annual returns, sharpe, omega etc.) Is there a python library that provides the calculation of the stats as callable functions similar to the ta-lib library, Python's TA-Lib port?

## Answer by Avagut (score 4, accepted)

https://quant.stackexchange.com/a/35831

Turns out Quantopian's empyrical package handles this exact use case Quantopian Empyrical Package

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.