TRTH Python API - Queuing Time

Hi,

I received a question "I see that to generate the intraday summary report with 5sec interval for 1 RIC for 1 month takes long time (more than 30min excluding queuing time). Is it normal? Is there any way to quicken the process?"

I could not attach .py File though the code from it is copied below, I have removed the Username and Password from the Python script.

Best regards,

Gareth

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# coding: utf-8 # In[4]: #Step 1: token request import requests import json import time requestUrl = "https://hosted.datascopeapi.reuters.com/RestApi/v1/Authentication/RequestToken"; requestHeaders={ "Prefer":"respond-async", "Content-Type":"application/json" } requestBody={ "Credentials": { "Username": , "Password": "" } } proxies = {'http': 'http://webproxy.ssmb.com:8080', 'https': 'http://webproxy.ssmb.com:8080'} r1 = requests.post(requestUrl, json=requestBody, headers=requestHeaders, proxies=proxies) if r1.status_code == 200 : jsonResponse = json.loads(r1.text.encode('ascii', 'ignore')) token = jsonResponse["value"] print ('Authentication token (valid 24 hours):') print (token) else: print ('Please replace myUserName and myPassword with valid credentials, then repeat the request') # In[5]: #Step 2: send an on demand extraction request using the received token requestUrl='https://hosted.datascopeapi.reuters.com/RestApi/v1/Extractions/ExtractRaw' requestHeaders={ "Prefer":"respond-async", "Content-Type":"application/json", "Authorization": "token " + token } requestBody={ "ExtractionRequest": { "@odata.type": "#ThomsonReuters.Dss.Api.Extractions.ExtractionRequests.TickHistoryIntradaySummariesExtractionRequest", "ContentFieldNames": [ # "Close Ask", # "Close Bid", # "High", # "High Ask", # "High Bid", "Last", # "Low", # "Low Ask", # "Low Bid", # "No. Asks", # "No. Bids", "No. Trades", "Open", # "Open Ask", # "Open Bid", "Volume" ], # "@odata.type": "#ThomsonReuters.Dss.Api.Extractions.ExtractionRequests.TickHistoryTimeAndSalesExtractionRequest", # "ContentFieldNames": [ # "Trade - Price", # "Trade - Volume" # ], "IdentifierList": { "@odata.type": "#ThomsonReuters.Dss.Api.Extractions.ExtractionRequests.InstrumentIdentifierList", "InstrumentIdentifiers": [{ "Identifier": "ESU7", "IdentifierType": "Ric" }, ], "UseUserPreferencesForValidationOptions":"false" }, "Condition": { "MessageTimeStampIn": "GmtUtc", "ReportDateRangeType": "Range", "QueryStartDate": "2017-06-28T00:00:00.000Z", "QueryEndDate": "2017-06-29T00:00:00.000Z", "SummaryInterval": "FiveSeconds", "TimebarPersistence":"false", "DisplaySourceRIC":"true" } } } r2 = requests.post(requestUrl, json=requestBody, headers=requestHeaders, proxies=proxies) #displaying the response status, and the location url to use to get the status of the extraction request #initial response status (after approximately 30 seconds wait) will be 202 print (r2.status_code) print (r2.headers["location"]) # In[6]: #Step 3: poll the status of the request using the received location URL, and getting the jobId and extraction notes requestUrl = r2.headers["location"] requestHeaders={ "Prefer":"respond-async", "Content-Type":"application/json", "Authorization":"token " + token } while True: r3 = requests.get(requestUrl, headers=requestHeaders, proxies=proxies) if r3.status_code == 200: break else: print('Failed...Re-request in 30 secs...') time.sleep(30) #when the status of the request is 200 the extraction is complete, we display the jobId and the extraction notes print ('response status = ' + str(r3.status_code)) if r3.status_code == 200 : r3Json = json.loads(r3.text.encode('ascii', 'ignore')) jobId = r3Json["JobId"] print ('jobId: ' + jobId + '\n') notes = r3Json["Notes"] print ('Extraction notes:\n' + notes[0]) else: print ('execute the cell again, until it returns a response status of 200') # In[7]: #Step 4: get the extraction results, using the receive jobId requestUrl = "https://hosted.datascopeapi.reuters.com/RestApi/v1/Extractions/RawExtractionResults"; + "('" + jobId + "')" + "/$value" requestHeaders={ "Prefer":"respond-async", "Content-Type":"text/plain", "Accept-Encoding":"gzip", "Authorization": "token " + token } r4 = requests.get(requestUrl, headers=requestHeaders, proxies=proxies) # print (r4.text) # In[8]: #Step 5 (cosmetic): formating the response using a panda dataframe from io import StringIO import pandas as pd timeSeries = pd.read_csv(StringIO(r4.text)) timeSeries # In[ ]: