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https://github.com/ranaroussi/quantstats

Portfolio analytics for quants, written in Python
https://github.com/ranaroussi/quantstats

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Portfolio analytics for quants, written in Python

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README 

        
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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('META')



    # 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', show=True)



    # can also be called via:

    # stock.plot_snapshot(title='Facebook Performance', show=True)



Output:



.. image:: https://github.com/ranaroussi/quantstats/blob/main/docs/snapshot.jpg?raw=true

    :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://github.com/ranaroussi/quantstats/blob/main/docs/report.jpg?raw=true

    :alt: HTML tearsheet



(`view original html file `_)



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',

	 'adjusted_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 `_ >= 3.5+

* `pandas `_ (tested to work with >=0.24.0)

* `numpy `_ >= 1.15.0

* `scipy `_ >= 1.2.0

* `matplotlib `_ >= 3.0.0

* `seaborn `_ >= 0.9.0

* `tabulate `_ >= 0.8.0

* `yfinance `_ >= 0.1.38

* `plotly `_ >= 3.4.1 (optional, for using ``plots.to_plotly()``)



Questions?

----------



This is a new library... If you find a bug, please

`open an issue `_

in this repository.



If you'd like to contribute, a great place to look is the

`issues marked with help-wanted `_.



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**