Error handling for get_timeseries()

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Question by oik22 · Aug 04, 2020 at 08:15 AM · eikon python eikon-data-api workspace workspace-data-api refinitiv-dataplatform-eikon error

Error handling for get_timeseries()

I am using a get_timeseries() function in a loop over a long list of stocks and get different errors depending on context (I am aware of the reasons for the errors):

- No data available for the requested data range.

- Client Error: Too many requests, please try again later

Is there a way to handle these errors differently? For example, I want skip the iteration that returns "no data available" and completely stop the loop execution if "too many requests" error shows, something like this

for ticker in ticker_list:         
        try:
            prices = ek.get_timeseries(ticker, interval = 'daily)         
        except NoDataAvailableError:
            continue
        except ClientError:
            break

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Answer 1 · 2020-08-04T18:59:38Z

REFINITIV

Best Answer

Answer by chavalit.jintamalit · Aug 04, 2020 at 06:59 PM

Hi @oik22

Eikon Data API raises EikonError error.

This is the sample code to catch it: (I simulated 2 cases, too many ric and invalid ric)

Under the except ek.EikonError, you can handle it according to your requirement.

To either break or continue your loop.

ahs.png (44.5 KiB)

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Answer 2 · 2020-08-04T08:47:16Z

REFINITIV

Answer by chavalit.jintamalit · Aug 04, 2020 at 08:47 AM

Hi @oik22

I think your handles make sense.

Maybe add a few delays between each loop too.

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Answer 3 · 2020-08-04T09:06:14Z

Answer by oik22 · Aug 04, 2020 at 09:06 AM

Thanks @chavalit.jintamalit

I am actually looking for the specific handles or codes for the errors that Eikon API throws in these two cases: (1) "no data available for the data range" (2) "too many requests", so that I can distinguish between them. The exception handles "NoDataAvailableError" and "ClientError" are just some random names I came up with as an example for illustrative purposes. I do not know what the actual Eikon error handles are for these two cases. Like the different Python build-in exceptions such as ValueError or ZeroDivisionError, that's what I am looking for.

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Error handling for get_timeseries()

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