QuantPerf: Portfolio analytics and performance calculation for financial data

QuantPerf Python library that performs portfolio profiling, allowing quants and portfolio managers to understand their performance better by providing them with in-depth analytics and risk metrics.

1. quant_performance.report.perf - for calculating various performance metrics, like Sharpe ratio, Win rate, Volatility, etc.

2. quant_performance.report.total_return_chart - for calculating total_return of DataSeries

Here's an example of a simple tear sheet analyzing a strategy:

Quick Start

.. code:: python

from quant_performance.report import perf

# fetch the daily returns for a stock
 df = pd.read_csv("./test_data/test_data.csv",parse_dates=['Date'] , index_col='Date')
metrics , dataframe = perf(df['Close'])
# metrics is metrics data that calculate and return as json 
# dataframe is whole calculations for metrics for every row and date

Output:

.. code:: text

# metrics should return you json data like this :
{'annualized_downside_volatility': 3.13,
 'anualreturn_1y': 21.01,
 'anualreturn_3y': 21.95,
 'anualreturn_5y': 17.02,
 'anualreturn_si': 0.63,
 'anuualized_gain_volatility': 2.79,
 'anuualized_loss_volatility': 3.41,
 'anuualized_volatility': 3.92,
 'average_monthly_gain': 0.69,
 'average_monthly_loss': -0.72,
 'best_month': 9.39,
 'best_month_date': '2020-03-24 ',
 'burke_ratio': -0.0,
 'calmar_ratio': 0.02,
 'compounded_return': 0.09,
 'gain_loss_ratio': 1.18,
 'kurtosis': 19.43,
 'maximum_drawdown': -33.79,
 'maximum_drawdown_date': '2020-03-23 ',
 'negative_months_fraction': 44.87,
 'omega_ratio': 0.93,
 'plm': -0.97,
 'positive_months_fraction': 55.02,
 'psi': 152.16,
 'sharp_ratio': -0.08,
 'skewness': -0.67,
 'sortino_ratio': -0.03,
 'sterling_ratio': -0.33,
 'ulcer_index': 1.78,
 'worse_month': -11.98,
 'worse_month_date': '2020-03-16 ',
 'yearly_return': {'2015': 1.4,
				   '2016': 11.96,
				   '2017': 21.83,
				   '2018': -4.38,
				   '2019': 31.49,
				   '2020': 18.4,
				   '2021': 28.71,
				   '2022': -4.84
		}}

*** Full documenttion coming soon ***

In the meantime, you can get insights as to optional parameters for each method, by using Python's help method:

.. code:: python

help(qs.stats.conditional_value_at_risk)

.. code:: text

Help on function conditional_value_at_risk in module quantstats.stats:

conditional_value_at_risk(returns, sigma=1, confidence=0.99)
    calculats the conditional daily value-at-risk (aka expected shortfall)
    quantifies the amount of tail risk an investment

Installation

Install using pip:

.. code:: bash

$ pip install quant_performance --upgrade --no-cache-dir

Requirements

• Python <https://www.python.org>_ >= 3.5+
• pandas <https://github.com/pydata/pandas>_ (tested to work with >=0.24.0)
• numpy <http://www.numpy.org>_ >= 1.15.0

P.S.

Please drop me a note with any feedback you have.

Amir najafi