Metadata-Version: 2.1
Name: margot
Version: 0.4
Summary: An algorithmic trading framework for PyData.
Home-page: https://github.com/atkinson/margot
Author: Rich Atkinson
Author-email: rich@airteam.com.au
License: apache-2.0
Keywords: quant,trading,systematic
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scipy
Requires-Dist: pyfolio
Requires-Dist: trading-calendars
Requires-Dist: m2r
Requires-Dist: versioneer



.. image:: https://img.shields.io/pypi/v/margot
   :target: https://pypi.org/project/margot/
   :alt: version


.. image:: https://img.shields.io/pypi/pyversions/margot
   :target: https://img.shields.io/pypi/pyversions/margot
   :alt: python


.. image:: https://img.shields.io/pypi/wheel/margot
   :target: https://img.shields.io/pypi/wheel/margot
   :alt: wheel


.. image:: https://img.shields.io/github/license/atkinson/margot
   :target: https://github.com/atkinson/margot/blob/master/LICENSE
   :alt: license


.. image:: https://img.shields.io/travis/com/atkinson/margot
   :target: https://travis-ci.com/github/atkinson/margot
   :alt: build


.. image:: https://readthedocs.org/projects/margot/badge/?version=latest
   :target: https://margot.readthedocs.io/en/latest/?badge=latest
   :alt: Documentation Status


.. image:: https://codecov.io/gh/atkinson/margot/branch/master/graph/badge.svg
   :target: https://codecov.io/gh/atkinson/margot
   :alt: codecov


An algorithmic trading framework for pydata.
============================================

Margot is a library of components that may be used together or separately. The first
major component is now availble for public preview. It should be considered early-beta.


* margot.data

Margot Data
===========

Margot data makes it easy to create neat and tidy dataframes.

Margot manages data collection, caching, cleaning, time-series feature generation and
Pandas Dataframe organisaiton and management using a clean, declarative API. If you've
ever used Django you'll find this approach similar to the Django ORM.

Columns
-------

The heart of a time-series dataframe is the original data. Margot can retreive time series
data from external sources (currently AlphaVantage). To add a time series such as
"closing_price" or "volume", we declare a Column.

e.g. to get closing_price and volume from AlphaVantage:

.. code-block::

   adjusted_close = av.Column(function='historical_daily_adjusted', 
                              column='adjusted_close')

   daily_volume = av.Column(function='historical_daily_adjusted',
                            column='volume')


Features
--------

Columns are useful, but we usually want to derive new time series from them, such 
as "log_returns" or "SMA20". Margot does this for you; we've called these derived
time-series, Features.

.. code-block::

   simple_returns = feature.SimpleReturns(column='adjusted_close')
   log_returns = feature.LogReturns(column='adjusted_close')
   sma20 = feature.SimpleMovingAverage(column='adjusted_close', window=20)


Features can be piled on top of one another. For example, to create a time series of
realised volatility based on log_returns with a lookback of 30 trading days, simply
add the following feature:

.. code-block::

   realised_vol = feature.RealisedVolatility(column='log_returns', window=30)


Margot includes many common financial Features, and we'll be adding more soon. It's 
also very easy to add your own.

Symbols
-------

Often, you want to make a dataframe combining a number of columns and features.
Margot makes this very easy by providing the Symbol class e.g.

.. code-block::

   class MyEquity(Symbol):

       adjusted_close = av.Column(function='historical_daily_adjusted', 
                                  column='adjusted_close')
       log_returns = feature.LogReturns(column='adjusted_close')
       realised_vol = feature.RealisedVolatility(column='log_returns', 
                                                 window=30)
       upper_band = feature.UpperBollingerBand(column='adjusted_close', 
                                               window=20, 
                                               width=2.0)
       sma20 = feature.SimpleMovingAverage(column='adjusted_close', 
                                           window=20)
       lower_band = feature.LowerBollingerBand(column='adjusted_close', 
                                               window=20, 
                                               width=2.0)

   spy = MyEquity(symbol='SPY')


MargotDataFrames
----------------

You usually you want to look at more than one symbol. That's where
ensembles come in. MargotDataFrame really brings power to margot.data.

.. code-block::

   class MyEnsemble(MargotDataFrame):
       spy = Equity(symbol='SPY')
       iwm = Equity(symbol='IWM')
       spy_iwm_ratio = Ratio(numerator=spy.adjusted_close, 
                             denominator=iwm.adjusted_close,
                             label='spy_iwm_ratio')

   my_df = MyEnsemble().to_pandas() 


The above code creates a Pandas DataFrame of both equities, and an additional
feature that calculates a time-series of the ratio of their respective
adjusted close prices.

Margot's other parts
====================

**not yet released.**

Margot also provides a simple framework for writing and backtesting trading
signal generation algorithms using margot.data.

Results from margot's trading algorithms can be analysed with pyfolio.

Getting Started
---------------

.. code-block::

   pip install margot


Next you need to make sure you have a couple of environment variables set:

.. code-block::

   export ALPHAVANTAGE_API_KEY=YOUR_API_KEY
   export DATA_CACHE=PATH_TO_FOLDER_TO_STORE_HDF5_FILES


Once you've done that, try running the code in the `notebook <https://github.com/atkinson/margot/blob/master/notebooks/margot.ipynb>`_.

Status
------

This is still an early stage software project, and should not be used for live trading.

Documentation
-------------

in progress - for examples see the `notebook <https://github.com/atkinson/margot/blob/master/notebooks/margot.ipynb>`_.

Contributing
------------

Feel free to make a pull request or chat about your idea first using `issues <https://github.com/atkinson/margot/issues>`_.

Dependencies are kept to a minimum. Generally if there's a way to do something in the standard library (or numpy / Pandas), let's do it that way rather than add another library. 

License
-------

Margot is licensed for use under Apache 2.0. For details see `the License <https://github.com/atkinson/margot/blob/master/LICENSE>`_.


