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"SDate" and "Period" parameters of get_data function

Hi everyone,

Thanks to the insights from another user's thread on this forum, I was able to create a function that retrieves the historical constituents of the SP1500 at a specific point in time (specifically, at the end of each year). My ultimate goal is to get a list of firms which where constantly part of the SP1500 between 2004-12-31 and 2006-12-31.

While the function works as intended, I'm having trouble understanding the purpose of the "SDate" and "Period" parameters. I would really appreciate some help in clarifying how these parameters function.

From what I gather, "FY0" likely refers to Fiscal Year 0, but I'm unsure of its exact role within the function. Additionally, the date appears in two places—once after "SPSUP" and again as a parameter. Could someone explain the difference between these two date references?

Thanks in advance for your help!

dates = ['2004-12-31', '2005-12-31', '2006-12-31']

#Create empty dataframe
SP1500_Complete = pd.DataFrame(columns=['Instrument', ' Company Common Name', 'Organization PermID', 'Date'])

# get S&P1500 constituents at the end of each year
for date in dates:
    SP1500, er = ek.get_data(f'0#.SPSUP({date})', fields=['TR.CommonName', 'TR.OrganizationID'], parameters={'SDate': f'{date}', 'Period': 'FY0'})
    SP1500['Date'] = date
    print(SP1500)
    SP1500_Complete = SP1500_Complete.append(SP1500, ignore_index=True) #Append dataframe
python apiindexconstituents
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Hi @flammers ,


You can find more information about the params from Workspace DIB app. If you select one of fields, select parameters on the right and click the question mark of the respective params, a box with explanations will appear. Including the screenshots below:

Sdate:

screenshot-2024-08-14-at-104940.png

Period:

screenshot-2024-08-14-at-105051.png

If you have further questions about the params, the best help can we received from our content team by raising a content query via my.refinitiv.com or Help&Support section in Workspace.

Additionally, please have a look into the following article which introduces a python object to get historical constituents easily:

Building historical index constituents | Devportal (lseg.com)


Hope this helps.


Best regards,

Haykaz


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Hi @aramyan.h

Thank you very much for your assistance and the helpful article. Greatly appreciated!

thank you, happy to hear it helped!

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