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\
Fork of Original QuantStats by Ran Aroussi
==========================================
This is a forked version of the original QuantStats library by Ran Aroussi. The original library can be found at https://github.com/ranaroussi/quantstats
This forked version was created because it seems that the original library is no longer being maintained. The original library has a number of issues and pull requests that have been open for a long time and have not been addressed. This forked version aims to address some of these issues and pull requests.
This forked version is created and maintained by the Lumiwealth team. We are a team of data scientists and software engineers who are passionate about quantitative finance and algorithmic trading. We use QuantStats in our daily work with the Lumibot library and we want to make sure that QuantStats is a reliable and well-maintained library.
If you're interested in learning how to make your own trading algorithms, check out our Lumibot library at https://github.com/Lumiwealth/lumibot and check out our courses at https://lumiwealth.com
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
===========
Install QuantStats Lumi using pip:
.. code:: bash
$ pip install quantstats-lumi
.. code:: python
%matplotlib inline
import quantstats_lumi 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\nFork of Original QuantStats by Ran Aroussi\n==========================================\n\nThis is a forked version of the original QuantStats library by Ran Aroussi. The original library can be found at https://github.com/ranaroussi/quantstats\n\nThis forked version was created because it seems that the original library is no longer being maintained. The original library has a number of issues and pull requests that have been open for a long time and have not been addressed. This forked version aims to address some of these issues and pull requests.\n\nThis forked version is created and maintained by the Lumiwealth team. We are a team of data scientists and software engineers who are passionate about quantitative finance and algorithmic trading. We use QuantStats in our daily work with the Lumibot library and we want to make sure that QuantStats is a reliable and well-maintained library.\n\nIf you're interested in learning how to make your own trading algorithms, check out our Lumibot library at https://github.com/Lumiwealth/lumibot and check out our courses at https://lumiwealth.com\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\nInstall QuantStats Lumi using pip:\n\n.. code:: bash\n\n\t$ pip install quantstats-lumi\n\n.. code:: python\n\n %matplotlib inline\n import quantstats_lumi 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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