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dynts-0.4.1
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Quantitative financial timeseries analysis
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dynts-0.4.1 |
| نام | dynts |
| نسخه کتابخانه | 0.4.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Luca Sbardella |
| ایمیل نویسنده | luca@quantmind.com |
| آدرس صفحه اصلی | http://github.com/quantmind/dynts/ |
| آدرس اینترنتی | https://pypi.org/project/dynts/ |
| مجوز | BSD |
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_.
It requires Python 2.6 and up, including Python 3 versions.
--
:Documentation: http://packages.python.org/dynts/
:Dowloads: http://pypi.python.org/pypi/dynts/
:Source: http://github.com/quantmind/dynts
:Keywords: timeseries, quantitative, finance, statistics, numpy, R, web
--
.. contents::
:local:
Timeserie Object
========================
To create a timeseries object directly::
>>> from dynts import timeseries
>>> ts = timeseries('test')
>>> ts.type
'zoo'
>>> ts.name
'test'
>>> ts
TimeSeries:zoo:test
>>> str(ts)
'test'
DSL
=======
At the core of the library there is a Domain-Specific-Language (DSL_) dedicated
to timeserie analysis and manipulation. DynTS makes timeserie manipulation easy and fun.
This is a simple multiplication::
>>> import dynts
>>> e = dynts.parse('2*GOOG')
>>> e
2.0 * goog
>>> len(e)
2
>>> list(e)
[2.0, goog]
>>> ts = dynts.evaluate(e).unwind()
>>> ts
TimeSeries:zoo:2.0 * goog
>>> len(ts)
251
Requirements
=====================
There are few requirements that must be met:
* python_ 2.6 up to python 3.2.
* 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 runtests.py
This will run by default the regression tests. To run a profile test
type::
python runtests.py -t profile <test-name>
where ``<test-name>`` is the name of a profile test.
To obtain a list of available tests for each test type, run::
python runtests.py --list
for regression, or::
python runtests.py -t profile --list
for profile, or::
python runtests.py -t bench --list
from benchmarks.
If you access the internet behind a proxy server, pass the ``-p`` option, for example::
python runtests.py -p http://myproxy.com:80
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
Kudos
===========
* numpy_ developers.
Community
=================
Trying to use an IRC channel **#dynts** on ``irc.freenode.net``
(you can use the webchat at http://webchat.freenode.net/).
If you find a bug or would like to request a feature, please `submit an issue`__.
__ http://github.com/quantmind/dynts/issues
.. _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/
نحوه نصب
نصب پکیج whl dynts-0.4.1:
pip install dynts-0.4.1.whl
نصب پکیج tar.gz dynts-0.4.1:
pip install dynts-0.4.1.tar.gz