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https://github.com/quantmind/dynts

Python package for timeseries analysis and manipulation
https://github.com/quantmind/dynts

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Python package for timeseries analysis and manipulation

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README 

        
A statistic package for python with enphasis on timeseries analysis.

Built around numpy_, it provides several back-end timeseries classes including R-based objects via rpy2_.

It is shipped with a domain specific language for timeseries analysis

and manipulation built on to of ply_.



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:Badges: |license|  |pyversions| |status| |pypiversion|

:Master CI: |master-build| |coverage-master|

:Documentation: http://quantmind.github.io/dynts/

:Dowloads: http://pypi.python.org/pypi/dynts/

:Source: http://github.com/quantmind/dynts

:Platforms: Linux, OSX, Windows. Python 3.5 and above

:Keywords: timeseries, quantitative, finance, statistics, numpy, R, web



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.. contents::

    :local:



Timeseries

========================



To create a timeseries object directly::



	>>> from dynts import timeseries

	>>> ts = timeseries('test')

	>>> ts.type

	'numpy'

	>>> ts.name

	'test'

	>>> ts

	TimeSeries:numpy:test

	>>> str(ts)

	'test'



DSL

=======

The package comes with a Domain-Specific-Language (DSL_) dedicated

to timeserie analysis and manipulation.

This is a simple multiplication::



	>>> from dynts import api

	>>> e = api.parse('2*GOOG')

	>>> e

	2.0 * goog

	>>> len(e)

	2

	>>> list(e)

	[2.0, goog]

	>>> ts = api.evaluate(e).unwind()

	>>> ts

	TimeSeries:numpy:2.0 * goog

	>>> len(ts)

	251



Requirements

=====================

There are few requirements that must be met:



* python_ 2.6 up to python 3.3.

* numpy_ version 1.5.1 or higher for arrays and matrices.

* ply_ version 3.3 or higher, the building block of the DSL_.

* ccy_ for date and currency manipulation.



R backend

===============================

Depending on the back-end used, additional dependencies need to be met.

For example, there are back-ends depending on the following R packages:



* rpy2_ if an R_ TimeSeries back-end is used (default).

* zoo_ and PerformanceAnlytics_ for the ``zoo`` back-end (currently the default one)

* timeSeries_ for the ``rmetrics`` back-end



Installing rpy2_ on Linux is straightforward, on windows it requires the

`python for windows`__ extension library.



Optional Requirements

===============================



* cython_ for performance. The library is not strictly dependent on cython, however its usage

  is highly recommended. If available several python modules will be replaced by more efficient compiled C code.

* xlwt_ to create spreadsheet from timeseries.

* matplotlib_ for plotting.

* djpcms_ for the ``web.views`` module.



__ http://sourceforge.net/projects/pywin32/files/



.. _running-tests:



Running Tests

=================

There are three types of tests available:



* ``regression`` for unit and regression tests.

* ``profile`` for analysing performance of different backends and impact of cython_.

* ``bench`` same as ``profile`` but geared towards speed rather than profiling.



From the distribution directory type::



	python setup.py test



This will run by default the regression tests. To run a profile test

type::



	python runtests.py -t profile 



where ```` is the name of a profile test.

To obtain a list of available tests for each test type, run::



	python setup.py test -l



for unit tests, or::



	python runtests.py -t profile --list



for profile, or::



	python runtests.py -t bench --list



from benchmarks.



It is needed since during tests some data is fetched from google finance.



To access coverage of tests you need to install the coverage_ package and run the tests using::



	coverage run runtests.py



and to check out the coverage report::



	coverage report -m



.. |pypiversion| image:: https://badge.fury.io/py/dynts.svg

    :target: https://pypi.python.org/pypi/dynts

.. |pyversions| image:: https://img.shields.io/pypi/pyversions/dynts.svg

  :target: https://pypi.python.org/pypi/dynts

.. |license| image:: https://img.shields.io/pypi/l/dynts.svg

  :target: https://pypi.python.org/pypi/dynts

.. |status| image:: https://img.shields.io/pypi/status/dynts.svg

  :target: https://pypi.python.org/pypi/dynts

.. |master-build| image:: https://travis-ci.org/quantmind/dynts.svg?branch=master

  :target: https://travis-ci.org/quantmind/dynts

.. |coverage-master| image:: https://coveralls.io/repos/github/quantmind/dynts/badge.svg?branch=master

  :target: https://coveralls.io/github/quantmind/dynts?branch=master

.. _numpy: http://numpy.scipy.org/

.. _ply: http://www.dabeaz.com/ply/

.. _rpy2: http://rpy.sourceforge.net/rpy2.html

.. _DSL: http://en.wikipedia.org/wiki/Domain-specific_language

.. _R: http://www.r-project.org/

.. _ccy: http://code.google.com/p/ccy/

.. _zoo: http://cran.r-project.org/web/packages/zoo/index.html

.. _PerformanceAnlytics: http://cran.r-project.org/web/packages/PerformanceAnalytics/index.html

.. _timeSeries: http://cran.r-project.org/web/packages/timeSeries/index.html

.. _Python: http://www.python.org/

.. _xlwt: http://pypi.python.org/pypi/xlwt

.. _matplotlib: http://matplotlib.sourceforge.net/

.. _djpcms: http://djpcms.com

.. _coverage: http://nedbatchelder.com/code/coverage/

.. _cython: http://www.cython.org/

.. _flot: http://code.google.com/p/flot/

.. _Sparklines: http://www.omnipotent.net/jquery.sparkline/