PortAn

portan is a set of portfolio analytics and reporting toolset in Python. The package is written to be comprehensive and flexible, while emphasizing ease of use.

• Free software: MIT license
• Documentation: https://portan.readthedocs.io/en/latest/

Installation

Preferred method to install portan is through Python's package installer pip. To install portan, run this command in your terminal

pip install portan

Alternatively, you can install the package directly from GitHub:

git clone -b development https://github.com/draktr/portan.git
cd portan
python setup.py install

User Guides

5 Minute Guide

Simplest way to initiate analysis is to pass tickers and weights arguments to Analytics class.

from portan import Analytics

portfolio = Analytics(
    tickers=["XOM", "GOOG", "T"],
    weights=[0.3, 0.3, 0.4],
)

Initiation of the Analytics object automatically calculates some basic portfolio attributes such as return time-series, mean return (simple, geometric and arithmetic), return volatility, AUM statistics, etc. For example, to get compounded annual mean return call

print(f"Compounded mean return: {portfolio.geometric_mean}")
# Compounded mean return: 0.12978880349610256

However, most of the analytics can be obtained by calling the relevant method. Here are a few examples:

premium = portfolio.excess_mar(annual_mar=0.05)
results = portfolio.distribution_test(test="kolomogorov-smirnov", distribution="norm")
ulcer_index = portfolio.ulcer()
var = portfolio.parametric_var()

This yields

print(f"Premium: {premium}")
# Premium: 0.07978880349610255
print(f"KS Test p-value: {results[1]}")
# KS Test p-value: 0.0
print(f"Ulcer Index: {ulcer_index}")
# Ulcer Index: 15.025392174313993
print(f"Parametric Value-at-Risk: {var}")
# Parametric Value-at-Risk: -0.02050514669206502

IMPORTANT: Since we didn't provide benchmark details, we cannot use any analytics that require benchmark such as portfolio $\beta$, information ratio, up capture, etc. To utilize full set of portan.Analytics features the user should specify benchmark details. Recommended way to do this is to provide benchmark details (benchmark weights and benchmark tickers or prices) at object instantiation:

from portan import Analytics

portfolio = Analytics(
    tickers=["XOM", "GOOG", "T"],
    weights=[0.3, 0.3, 0.4],
    benchmark_tickers=["ITOT", "IEF"],
    benchmark_weights=[0.6, 0.4],
)

In this example, we took standard 60/40 equity-fixed income portfolio as benchmark. This allows us to calculate other analytics. For example:

alpha, beta, epsilon, r_squared = portfolio.capm()
ir = portfolio.information_ratio()
treynor = portfolio.treynor()
downside_risk = portfolio.downside_risk()

This yields

print(f"Portfolio beta: {beta}")
# Portfolio beta: 1.4821305983730502
print(f"Information ratio: {ir}")
# Information ratio: 0.3873682857378387
print(f"Treynor: {treynor}")
# Treynor: 0.06732794488749151
print(f"Downside risk: {downside_risk}")
# Downside risk: 0.008819039607513948

Alternatively, benchmark can be set and reset after initiation of Analytics object. More on this is discussed in (Re)setting Benchmark section.

Features explored here are only to demonstrate basic functionality of Analytics class and complete list of portan features is listed in Features section.