HTTP Error: Backend error. 400 Bad Request
Hi, I'm trying to pull some data with the following:
screener_tsvx = "SCREEN(U(IN(Equity(active,public,primary))/*UNV:Public*/), IN(TR.ExchangeMarketIdCode,""TSXV"",""XTNX"",""XTSE"",""XTSX""), TR.AvgDailyValTraded20D>=GMEDIAN(ZAV(TR.AvgDailyValTraded20D),universe=""univ""), CURN=CAD)" ek.get_data([screener_tsvx],["TR.CommonName"]) # ek.get_data([screener_tsx],["TR.CommonName;TR.InstrumentType;TR.CompanyMarketCap"])
It works in excel, but in my jupyter notebook I get the following HTTPError:
---------------------------------------------------------------------------
HTTPError Traceback (most recent call last)
<ipython-input-100-fa4ae8947122> in <module>()
1 screener_tsvx = "SCREEN(U(IN(Equity(active,public,primary))/*UNV:Public*/), IN(TR.ExchangeMarketIdCode,""TSXV"",""XTNX"",""XTSE"",""XTSX""), TR.AvgDailyValTraded20D>=GMEDIAN(ZAV(TR.AvgDailyValTraded20D),universe=""univ""), CURN=CAD)"
----> 2 ek.get_data([screener_tsvx],["TR.CommonName"])
3 # ek.get_data([screener_tsx],["TR.CommonName;TR.InstrumentType;TR.CompanyMarketCap"])
~\Anaconda3\lib\site-packages\eikon\data_grid.py in get_data(instruments, fields, parameters, field_name, raw_output, debug)
149 payload = {'instruments': instruments,'fields': fields_for_request}
150 if parameters: payload.update({'parameters': parameters})
--> 151 result = eikon.json_requests.send_json_request(DataGrid_UDF_endpoint, payload, debug=debug)
152
153 if raw_output:
~\Anaconda3\lib\site-packages\eikon\json_requests.py in send_json_request(entity, payload, ID, debug)
85 if response.status_code == 200:
86 result = response.json()
---> 87 check_server_error(result)
88 return result
89 if response.status_code == 401:
~\Anaconda3\lib\site-packages\eikon\json_requests.py in check_server_error(server_response)
133 else:
134 status_code = server_response['ErrorCode']
--> 135 raise requests.HTTPError(error_message, response=server_response)
136
137 # check DataGrid error
HTTPError: Backend error. 400 Bad Request
I've seen some suggestions of queries timing out, but I'm not sure if this is the case. I've also been unable to find a detailed documentation on how the `instruments` field for the get_data function can be specified. I've been trying to use the Screener function as a work around, and now I've run into the HTTP Backend error. Not entirely sure what my options are now. Any suggestions, advice would be nice.
Thank you!
The use of equity screener through Eikon Data APIs is not supported yet. One reason for this is that currently any get_data request is automatically timed out from the server side if the server cannot return the data requested in 15 seconds. Rendering the data through screener can take much longer than 15 seconds to complete, in which case you get back "HTTPError: Backend error. 400 Bad Request". Work is being done to address this issue. Until this work is complete the screener through Eikon Data APIs can only be used for basic expressions that the server can render in less than 15 seconds.
Could you elaborate on what you expect from "detailed documentation on how the instruments field for the get_data function can be specified"? It seems you've already checked the "Eikon Data APIs for Python - Reference Guide" available from the Documentation tab on Eikon Data APIs page on this portal, which provides definition of all the methods and their signatures available in Eikon Data APIs for Python. Per this document the "instruments" argument is a string representing an identifier for a single instrument or a list of strings where each string represents an instrument identifier. For instrument identifier you can use a variety of symbol types including RIC, ISIN, CUSIP, SEDOL, Wertpapierkennnummer, PermID etc. If you have a list of instrument identifiers for which you need market or fundamental & reference data, you can use this list directly to retrieve the data using get_data method, e.g.
ek.get_data(['TRI.N','AAPL.O'],['TR.Revenue'])The screener is useful when you need to identify stocks that satisfy your criteria.
I guess I'm asking if there's a list that provides all possible types of values that the get_data `instruments` field can take. For example, I can provide `['TRI.N', 'AAPL.O']` which is a list of symbols. I could also use `'#0.SPX'`, as well as `"SCREEN()"` or `"PORTFOLIO()"`. I was wondering what other options there are and whether or not there is a documented list of allowed methods available.
Thank you!
I see. Well, you pretty much listed all of them. The only one you missed is "LISTS()", which is similar to "PORTFOLIO()". When instruments argument is a single string, it can take the same string values you can provide to the first argument of =TR function in Excel, notwithstanding the known issue with the server timeout that I described earlier.
The values the Universe argument of =TR function can take are documented in the Help, which you can access from the Thomson Reuters tab in Eikon Excel ribbon. But even this documentation is not necessarily all exhaustive. E.g. it doesn't specifically mention using chain RICs such as "0#.SPX" as universe, and we may be adding other types of universes in the future.
Hi,
Any updates getting equity screener support in the API? I'm still getting the same timeout error.
Make sure you use the latest version of Eikon Data APIs library for Python. Version 1.0.1 of the library released in May 2019 introduced a fix that greatly reduced the occurrence of "EikonError: Error code 400 | Backend error. 400 Bad Request" due to timeouts from the backend. Currently the most recent production version of Eikon Data APIs library for Python on PYPI is v1.0.2.
The 400 Bad Request error is an HTTP status code indicates that the request you sent to the webserver was malformed , in other words, the data stream sent by the client to the server didn't follow the rules. It means that the request itself has somehow incorrect or corrupted and the server couldn't understand it. There are a number of different causes for a 400: Bad Request Error . It might be a malformed request syntax, invalid request message framing, or deceptive request routing . In most cases, the problem is on the website itself, and there's not much you can do about that.