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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 <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 investmentInstall using pip:
.. code:: bash
$ pip install quantstats --upgrade --no-cache-dirInstall using conda:
.. code:: bash
$ conda install -c ranaroussi quantstatsPython <https://www.python.org>_ >= 3.5+pandas <https://github.com/pydata/pandas>_ (tested to work with >=0.24.0)numpy <http://www.numpy.org>_ >= 1.15.0scipy <https://www.scipy.org>_ >= 1.2.0matplotlib <https://matplotlib.org>_ >= 3.0.0seaborn <https://seaborn.pydata.org>_ >= 0.9.0tabulate <https://bitbucket.org/astanin/python-tabulate>_ >= 0.8.0yfinance <https://github.com/ranaroussi/yfinance>_ >= 0.1.38plotly <https://plot.ly/>_ >= 3.4.1 (optional, for using plots.to_plotly())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>_.
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.
QuantStats is distributed under the Apache Software License. See the LICENSE.txt <./LICENSE.txt>_ file in the release for details.
Please drop me a note with any feedback you have.
Ran Aroussi