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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 ยป <./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 <https://rawcdn.githack.com/ranaroussi/quantstats/main/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',
'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 <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**
Raw data
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"description": ".. image:: https://img.shields.io/badge/python-3.6+-blue.svg?style=flat\n :target: https://pypi.python.org/pypi/quantstats\n :alt: Python version\n\n.. image:: https://img.shields.io/pypi/v/quantstats.svg?maxAge=60\n :target: https://pypi.python.org/pypi/quantstats\n :alt: PyPi version\n\n.. image:: https://img.shields.io/pypi/status/quantstats.svg?maxAge=60\n :target: https://pypi.python.org/pypi/quantstats\n :alt: PyPi status\n\n.. image:: https://img.shields.io/pypi/dm/quantstats.svg?maxAge=2592000&label=installs&color=%2327B1FF\n :target: https://pypi.python.org/pypi/quantstats\n :alt: PyPi downloads\n\n.. image:: https://www.codefactor.io/repository/github/ranaroussi/quantstats/badge\n :target: https://www.codefactor.io/repository/github/ranaroussi/quantstats\n :alt: CodeFactor\n\n.. image:: https://img.shields.io/github/stars/ranaroussi/quantstats.svg?style=social&label=Star&maxAge=60\n :target: https://github.com/ranaroussi/quantstats\n :alt: Star this repo\n\n.. image:: https://img.shields.io/twitter/follow/aroussi.svg?style=social&label=Follow&maxAge=60\n :target: https://twitter.com/aroussi\n :alt: Follow me on twitter\n\n\\\n\nQuantStats: Portfolio analytics for quants\n==========================================\n\n**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.\n\n`Changelog \u00bb <./CHANGELOG.rst>`__\n\nQuantStats is comprised of 3 main modules:\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n1. ``quantstats.stats`` - for calculating various performance metrics, like Sharpe ratio, Win rate, Volatility, etc.\n2. ``quantstats.plots`` - for visualizing performance, drawdowns, rolling statistics, monthly returns, etc.\n3. ``quantstats.reports`` - for generating metrics reports, batch plotting, and creating tear sheets that can be saved as an HTML file.\n\nHere's an example of a simple tear sheet analyzing a strategy:\n\nQuick Start\n===========\n\n.. code:: python\n\n %matplotlib inline\n import quantstats as qs\n\n # extend pandas functionality with metrics, etc.\n qs.extend_pandas()\n\n # fetch the daily returns for a stock\n stock = qs.utils.download_returns('META')\n\n # show sharpe ratio\n qs.stats.sharpe(stock)\n\n # or using extend_pandas() :)\n stock.sharpe()\n\nOutput:\n\n.. code:: text\n\n 0.8135304438803402\n\n\nVisualize stock performance\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: python\n\n qs.plots.snapshot(stock, title='Facebook Performance', show=True)\n\n # can also be called via:\n # stock.plot_snapshot(title='Facebook Performance', show=True)\n\nOutput:\n\n.. image:: https://github.com/ranaroussi/quantstats/blob/main/docs/snapshot.jpg?raw=true\n :alt: Snapshot plot\n\n\nCreating a report\n~~~~~~~~~~~~~~~~~\n\nYou can create 7 different report tearsheets:\n\n1. ``qs.reports.metrics(mode='basic|full\", ...)`` - shows basic/full metrics\n2. ``qs.reports.plots(mode='basic|full\", ...)`` - shows basic/full plots\n3. ``qs.reports.basic(...)`` - shows basic metrics and plots\n4. ``qs.reports.full(...)`` - shows full metrics and plots\n5. ``qs.reports.html(...)`` - generates a complete report as html\n\nLet' create an html tearsheet\n\n.. code:: python\n\n (benchmark can be a pandas Series or ticker)\n qs.reports.html(stock, \"SPY\")\n\nOutput will generate something like this:\n\n.. image:: https://github.com/ranaroussi/quantstats/blob/main/docs/report.jpg?raw=true\n :alt: HTML tearsheet\n\n(`view original html file <https://rawcdn.githack.com/ranaroussi/quantstats/main/docs/tearsheet.html>`_)\n\n\nTo view a complete list of available methods, run\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: python\n\n [f for f in dir(qs.stats) if f[0] != '_']\n\n\n.. code:: text\n\n\t['avg_loss',\n\t 'avg_return',\n\t 'avg_win',\n\t 'best',\n\t 'cagr',\n\t 'calmar',\n\t 'common_sense_ratio',\n\t 'comp',\n\t 'compare',\n\t 'compsum',\n\t 'conditional_value_at_risk',\n\t 'consecutive_losses',\n\t 'consecutive_wins',\n\t 'cpc_index',\n\t 'cvar',\n\t 'drawdown_details',\n\t 'expected_return',\n\t 'expected_shortfall',\n\t 'exposure',\n\t 'gain_to_pain_ratio',\n\t 'geometric_mean',\n\t 'ghpr',\n\t 'greeks',\n\t 'implied_volatility',\n\t 'information_ratio',\n\t 'kelly_criterion',\n\t 'kurtosis',\n\t 'max_drawdown',\n\t 'monthly_returns',\n\t 'outlier_loss_ratio',\n\t 'outlier_win_ratio',\n\t 'outliers',\n\t 'payoff_ratio',\n\t 'profit_factor',\n\t 'profit_ratio',\n\t 'r2',\n\t 'r_squared',\n\t 'rar',\n\t 'recovery_factor',\n\t 'remove_outliers',\n\t 'risk_of_ruin',\n\t 'risk_return_ratio',\n\t 'rolling_greeks',\n\t 'ror',\n\t 'sharpe',\n\t 'skew',\n\t 'sortino',\n\t 'adjusted_sortino',\n\t 'tail_ratio',\n\t 'to_drawdown_series',\n\t 'ulcer_index',\n\t 'ulcer_performance_index',\n\t 'upi',\n\t 'utils',\n\t 'value_at_risk',\n\t 'var',\n\t 'volatility',\n\t 'win_loss_ratio',\n\t 'win_rate',\n\t 'worst']\n\n.. code:: python\n\n [f for f in dir(qs.plots) if f[0] != '_']\n\n.. code:: text\n\n\t['daily_returns',\n\t 'distribution',\n\t 'drawdown',\n\t 'drawdowns_periods',\n\t 'earnings',\n\t 'histogram',\n\t 'log_returns',\n\t 'monthly_heatmap',\n\t 'returns',\n\t 'rolling_beta',\n\t 'rolling_sharpe',\n\t 'rolling_sortino',\n\t 'rolling_volatility',\n\t 'snapshot',\n\t 'yearly_returns']\n\n\n**\\*\\*\\* Full documenttion coming soon \\*\\*\\***\n\nIn the meantime, you can get insights as to optional parameters for each method, by using Python's ``help`` method:\n\n.. code:: python\n\n help(qs.stats.conditional_value_at_risk)\n\n.. code:: text\n\n\tHelp on function conditional_value_at_risk in module quantstats.stats:\n\n\tconditional_value_at_risk(returns, sigma=1, confidence=0.99)\n\t calculats the conditional daily value-at-risk (aka expected shortfall)\n\t quantifies the amount of tail risk an investment\n\n\nInstallation\n------------\n\nInstall using ``pip``:\n\n.. code:: bash\n\n $ pip install quantstats --upgrade --no-cache-dir\n\n\nInstall using ``conda``:\n\n.. code:: bash\n\n $ conda install -c ranaroussi quantstats\n\n\nRequirements\n------------\n\n* `Python <https://www.python.org>`_ >= 3.5+\n* `pandas <https://github.com/pydata/pandas>`_ (tested to work with >=0.24.0)\n* `numpy <http://www.numpy.org>`_ >= 1.15.0\n* `scipy <https://www.scipy.org>`_ >= 1.2.0\n* `matplotlib <https://matplotlib.org>`_ >= 3.0.0\n* `seaborn <https://seaborn.pydata.org>`_ >= 0.9.0\n* `tabulate <https://bitbucket.org/astanin/python-tabulate>`_ >= 0.8.0\n* `yfinance <https://github.com/ranaroussi/yfinance>`_ >= 0.1.38\n* `plotly <https://plot.ly/>`_ >= 3.4.1 (optional, for using ``plots.to_plotly()``)\n\nQuestions?\n----------\n\nThis is a new library... If you find a bug, please\n`open an issue <https://github.com/ranaroussi/quantstats/issues>`_\nin this repository.\n\nIf you'd like to contribute, a great place to look is the\n`issues marked with help-wanted <https://github.com/ranaroussi/quantstats/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22>`_.\n\n\nKnown Issues\n------------\n\nFor some reason, I couldn't find a way to tell seaborn not to return the\nmonthly returns heatmap when instructed to save - so even if you save the plot (by passing ``savefig={...}``) it will still show the plot.\n\n\nLegal Stuff\n------------\n\n**QuantStats** is distributed under the **Apache Software License**. See the `LICENSE.txt <./LICENSE.txt>`_ file in the release for details.\n\n\nP.S.\n------------\n\nPlease drop me a note with any feedback you have.\n\n**Ran Aroussi**\n",
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