Overview

A simple python tool for calculating ratios used to measure portfolio performance. Ratios include alpha, beta, sharpe, volatility, upside capture, downside capture, sortino ratio, treynor ratio, drawdown etc.

It also can be used to calculating portfolio returns like XIRR. (twirr, holding period return etc. will be added).

The tool is largely based on pandas and numpy and is capable of giving continuous (rolling) values of ratios wherever required in the form of a pandas dataframe. All data (portfolio/ navs/ market) needs to be passed in arguments based on the function getting called.

For example - XIRR can be calculated from portfolio cashflows [(date, amount)]. - Sharpe ratio will need scheme/portfolio nav [(date, nav)]. - Alpha will need both scheme nav as well as benchmark nav.

For definitions of above terms, check Investopedia. You can find the examples of few of these ratios here. https://www.valueresearchonline.com/funds/197/sbi-large-and-midcap-fund

• Free software: BSD 2-Clause License

pip install finance-calculator

pip install https://github.com/sprksh/finance-calculator/archive/master.zip

tox

set PYTEST_ADDOPTS=--cov-append
tox

PYTEST_ADDOPTS=--cov-append tox

Usage

To use finance_calculator in a project:

import finance_calculator as fc

drawdown = fc.get_drawdown(scheme_data, 250, 22)
volatility = fc.get_volatility(scheme_data, 250, 22)
sharpe = fc.get_sharpe(scheme_data, 250, 22)
sortino = fc.get_sortino(scheme_data, 250, 22)
treynor = fc.get_treynor(scheme_data, benchmark_data, 250, 22)
alpha = fc.get_alpha(scheme_data, benchmark_data, 250, 22)
beta = fc.get_beta(scheme_data, benchmark_data, 250, 22)
upside_capture = fc.get_upside_capture(scheme_data, benchmark_data, 250, 22)
downside_capture = fc.get_downside_capture(scheme_data, benchmark_data, 250, 22)

If you want only current value of a given ratio, you can use tail=True as a keyword argument in all of these functions. With tail=False it will give a pandas dataframe with values in a rolling window fashion.

The scheme data and the benchmark data can either be a pandas dataframe or list of tuples: (date, nav).

Also you can use it to calculate xirr:

>>> import finance_calculator as fc
>>> cashflow_data = [
    (datetime.date(2020, 3, 1), 10000),
    (datetime.date(2020, 4, 1), 10000),
    (datetime.date(2020, 5, 1), 10000),
    (datetime.date(2020, 6, 1), 10000),
    (datetime.date(2020, 7, 1), 10000),
    (datetime.date(2020, 8, 1), -60000),
]
>>> xirr = fc.get_xirr(cashflow_data)