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QuantStats: Portfolio analytics for quants

QuantStats 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.

Changelog » <./CHANGELOG.rst>__

QuantStats is comprised of 3 main modules:

1. ``quantstats.stats`` - for calculating various performance metrics, like Sharpe ratio, Win rate, Volatility, etc.
2. ``quantstats.plots`` - for visualizing performance, drawdowns, rolling statistics, monthly returns, etc.
3. ``quantstats.reports`` - for generating metrics reports, batch plotting, and creating tear sheets that can be saved as an HTML file.

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

Quick Start
===========

.. code:: python

    %matplotlib inline
    import quantstats as qs

    # extend pandas functionality with metrics, etc.
    qs.extend_pandas()

    # fetch the daily returns for a stock
    stock = qs.utils.download_returns('FB')

    # show sharpe ratio
    qs.stats.sharpe(stock)

    # or using extend_pandas() :)
    stock.sharpe()

Output:

.. code:: text

    0.8135304438803402


Visualize stock performance
~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: python

    qs.plots.snapshot(stock, title='Facebook Performance')

    # can also be called via:
    # stock.plot_snapshot(title='Facebook Performance')

Output:

.. image:: https://raw.githubusercontent.com/ranaroussi/quantstats/dev/docs/snapshot.jpg
    :alt: Snapshot plot


Creating a report
~~~~~~~~~~~~~~~~~

You can create 7 different report tearsheets:

1. ``qs.reports.metrics(mode='basic|full", ...)`` - shows basic/full metrics
2. ``qs.reports.plots(mode='basic|full", ...)`` - shows basic/full plots
3. ``qs.reports.basic(...)`` - shows basic metrics and plots
4. ``qs.reports.full(...)`` - shows full metrics and plots
5. ``qs.reports.html(...)`` - generates a complete report as html

Let' create an html tearsheet

.. code:: python

    (benchmark can be a pandas Series or ticker)
    qs.reports.html(stock, "SPY")

Output will generate something like this:

.. image:: https://raw.githubusercontent.com/ranaroussi/quantstats/dev/docs/report.jpg
    :alt: HTML tearsheet

(`view original html file <https://rawcdn.githack.com/ranaroussi/quantstats/bd0e70bd284798a58c569363bc7e1c3c8c6e3fd9/docs/tearsheet.html>`_)


To view a complete list of available methods, run

.. code:: python

[f for f in dir(qs.stats) if f[0] != '_']

.. code:: text

['avg_loss',
 'avg_return',
 'avg_win',
 'best',
 'cagr',
 'calmar',
 'common_sense_ratio',
 'comp',
 'compare',
 'compsum',
 'conditional_value_at_risk',
 'consecutive_losses',
 'consecutive_wins',
 'cpc_index',
 'cvar',
 'drawdown_details',
 'expected_return',
 'expected_shortfall',
 'exposure',
 'gain_to_pain_ratio',
 'geometric_mean',
 'ghpr',
 'greeks',
 'implied_volatility',
 'information_ratio',
 'kelly_criterion',
 'kurtosis',
 'max_drawdown',
 'monthly_returns',
 'outlier_loss_ratio',
 'outlier_win_ratio',
 'outliers',
 'payoff_ratio',
 'profit_factor',
 'profit_ratio',
 'r2',
 'r_squared',
 'rar',
 'recovery_factor',
 'remove_outliers',
 'risk_of_ruin',
 'risk_return_ratio',
 'rolling_greeks',
 'ror',
 'sharpe',
 'skew',
 'sortino',
 'tail_ratio',
 'to_drawdown_series',
 'ulcer_index',
 'ulcer_performance_index',
 'upi',
 'utils',
 'value_at_risk',
 'var',
 'volatility',
 'win_loss_ratio',
 'win_rate',
 'worst']

.. code:: python

[f for f in dir(qs.plots) if f[0] != '_']

.. code:: text

['daily_returns',
 'distribution',
 'drawdown',
 'drawdowns_periods',
 'earnings',
 'histogram',
 'log_returns',
 'monthly_heatmap',
 'returns',
 'rolling_beta',
 'rolling_sharpe',
 'rolling_sortino',
 'rolling_volatility',
 'snapshot',
 'yearly_returns']

*** 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 quantstats --upgrade --no-cache-dir

Install using conda:

.. code:: bash

$ conda install -c ranaroussi quantstats

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
• scipy <https://www.scipy.org>_ >= 1.2.0
• matplotlib <https://matplotlib.org>_ >= 3.0.0
• seaborn <https://seaborn.pydata.org>_ >= 0.9.0
• tabulate <https://bitbucket.org/astanin/python-tabulate>_ >= 0.8.0
• yfinance <https://github.com/ranaroussi/yfinance>_ >= 0.1.38
• plotly <https://plot.ly/>_ >= 3.4.1 (optional, for using plots.to_plotly())

Questions?

This is a new library... If you find a bug, please open an issue <https://github.com/ranaroussi/quantstats/issues>_ in this repository.

If you'd like to contribute, a great place to look is the issues marked with help-wanted <https://github.com/ranaroussi/quantstats/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22>_.

Known Issues

For some reason, I couldn't find a way to tell seaborn not to return the monthly returns heatmap when instructed to save - so even if you save the plot (by passing savefig={...}) it will still show the plot.

Legal Stuff

QuantStats is distributed under the Apache Software License. See the LICENSE.txt <./LICENSE.txt>_ file in the release for details.

P.S.

Please drop me a note with any feedback you have.

Ran Aroussi