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Quantitative financial timeseries analysis

Project description

A Domain Specific Language for Timeseries Analysis.

Keywords:timeserie, quantitative, finance, statistics, web

Timeseries analysis and a timeseries domain specific language written in Python.

Timeserie Object

To create a timeseries object directly:

>>> from dynts import timeseries
>>> ts = timeseries('test')
>>> ts.type
>>> ts
>>> str(ts)


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)
>>> list(e)
[2.0, goog]
>>> ts = dynts.evaluate(e).unwind()
>>> ts
TimeSeries:zoo:2.0 * goog
>>> len(ts)


There are several requirements that must be met:

  • python 2.5 or later. Note that Python 3 series are not supported yet.
  • numpy for arrays and matrices.
  • ply the building block of the DSL.
  • rpy2 if an R TimeSeries back-end is used (default).
  • ccy for date and currency manipulation.

Depending on the back-end used, additional dependencies need to be met. For example, there are back-ends depending on the following R packages:

Installing rpy2 on Linux is straightforward, on windows it requires the python for windows extension library.

Optional Requirements

Running Tests

Form the package directory:


or, once installed:

from dynts import runtests

If you are behind a proxy, some tests will fail unless you write a little script which looks like this:

from dynts.conf import settings
from dynts import runtests
settings.proxies['http'] = ''

if __name__ == '__main__':


Trying to use an IRC channel #dynts on (you can use the webchat at

If you find a bug or would like to request a feature, please submit an issue.

Project details

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