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Most paid end-of-day data packages are available in metastock format. For analytics purposes, it will be nice if one can read the metastock data and load them into Python panda data frames. Is it possible to do this today?

The metastock format I am referring to is the legacy (pre-12.0) MetaStock file format. It is a binary file format and originated from the Computrac file format. There are four files associated with the format: MASTER, EMASTER, XMASTER, FDAT, and F.MWD.

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    $\begingroup$ Which Metastock data format are you referring to, this one stockhistoricaldata.com/historical-data-formats/… or 'The legacy (pre-12.0) MetaStock file format' en.wikipedia.org/wiki/MetaStock#File_Format ? $\endgroup$ – noob2 Mar 1 '17 at 13:10
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    $\begingroup$ It is the legacy (pre-12.0) MetaStock file format. Thanks. $\endgroup$ – curious Mar 1 '17 at 13:13
  • $\begingroup$ OK, then unfortunately I agree with Rehan's post below. $\endgroup$ – noob2 Mar 1 '17 at 13:14
  • $\begingroup$ Looks like someone has a project 'pycomputrac' to develop a way to read Metastock data in python. github.com/akapur/pycomputrac I have no idea how advanced this project is or whether it works at all. $\endgroup$ – noob2 Mar 1 '17 at 15:03
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The binary data from MetaStock cannot be directly read into a pandas dataframe, or for the matter of fact, into any python library commonly known. For this you would need to convert it into text and then import, which simply complicates the process.

The easier way to do this, would be to use Quandl - although it costs slightly higher than MetaStock, it ties in very well to Python, and you can import the requisite data in a jiffy.

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