Metadata-Version: 1.0
Name: dynts
Version: 0.4.1
Summary: Quantitative financial timeseries analysis
Home-page: http://github.com/quantmind/dynts/
Author: Luca Sbardella
Author-email: luca@quantmind.com
License: BSD
Description: 
        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/
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Plugins
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: JavaScript
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.1
Classifier: Programming Language :: Python :: 3.2
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Office/Business :: Financial
