Python package for timeseries analysis and manipulation
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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.

Badges:license pyversions status pypiversion
Master CI:master-build coverage-master
Platforms:Linux, OSX, Windows. Python 3.5 and above
Keywords:timeseries, quantitative, finance, statistics, numpy, R, web


To create a timeseries object directly:

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


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


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:

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.

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 test

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

python -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 test -l

for unit tests, or:

python -t profile --list

for profile, or:

python -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

and to check out the coverage report:

coverage report -m