I requested market depths with below code. Although there are no error msg, the file is empty. I tried different time but no luck.
#Step 2: send an on demand extraction request using the received token
requestUrl='https://selectapi.datascope.refinitiv.com/RestApi/v1/Extractions/ExtractRaw'
requestHeaders={
"Prefer":"respond-async",
"Content-Type":"application/json",
"Authorization": "token " + token
}
requestBody={
"ExtractionRequest": {
"@odata.type": "#DataScope.Select.Api.Extractions.ExtractionRequests.TickHistoryMarketDepthExtractionRequest",
"ContentFieldNames": [
"Ask Price",
"Ask Size",
"Bid Price",
"Bid Size",
"Domain",
"History End",
"History Start",
"Instrument ID",
"Instrument ID Type",
"Number of Buyers",
"Number of Sellers",
"Sample Data"
],
"IdentifierList": {
"@odata.type": "#DataScope.Select.Api.Extractions.ExtractionRequests.InstrumentIdentifierList",
"InstrumentIdentifiers": [
{
"Identifier": "6501.T",
"IdentifierType": "Ric"
}
]
},
"Condition": {
"View": "NormalizedLL2",
"NumberOfLevels": 10,
"MessageTimeStampIn": "GmtUtc",
"ReportDateRangeType": "Range",
"QueryStartDate": "2022-06-28T05:00:00.000Z",
"QueryEndDate": "2022-06-28T05:35:00.000Z",
"DisplaySourceRIC": True
}
}
}
r2 = requests.post(requestUrl, json=requestBody,headers=requestHeaders)
#Display the HTTP status of the response
#Initial response status (after approximately 30 seconds wait) is usually 202
status_code = r2.status_code
print ("HTTP status of the response: " + str(status_code))
#Step 3: if required, poll the status of the request using the received location URL.
#Once the request has completed, retrieve the jobId and extraction notes.
#If status is 202, display the location url we received, and will use to poll the status of the extraction request:
if status_code == 202 :
requestUrl = r2.headers["location"]
print ('Extraction is not complete, we shall poll the location URL:')
print (str(requestUrl))
requestHeaders={
"Prefer":"respond-async",
"Content-Type":"application/json",
"Authorization":"token " + token
}
#As long as the status of the request is 202, the extraction is not finished;
#we must wait, and poll the status until it is no longer 202:
while (status_code == 202):
print ('As we received a 202, we wait 30 seconds, then poll again (until we receive a 200)')
time.sleep(30)
r3 = requests.get(requestUrl,headers=requestHeaders)
status_code = r3.status_code
print ('HTTP status of the response: ' + str(status_code))
#When the status of the request is 200 the extraction is complete;
#we retrieve and display the jobId and the extraction notes (it is recommended to analyse their content)):
if status_code == 200 :
r3Json = json.loads(r3.text.encode('ascii', 'ignore'))
jobId = r3Json["JobId"]
print ('\njobId: ' + jobId + '\n')
notes = r3Json["Notes"]
print ('Extraction notes:\n' + notes[0])
#If instead of a status 200 we receive a different status, there was an error:
if status_code != 200 :
print ('An error occured. Try to run this cell again. If it fails, re-run the previous cell.\n')
#Step 4: get the extraction results, using the received jobId.
#Decompress the data and display it on screen.
#Skip this step if you asked for a large data set, and go directly to step 5 !
#We also save the data to disk; but note that if you use AWS it will be saved as a GZIP,
#otherwise it will be saved as a CSV !
#This discrepancy occurs because we allow automatic decompression to happen when retrieving
#from RTH, so we end up saving the decompressed contents.
#IMPORTANT NOTE:
#The code in this step is only for demo, to display some data on screen.
#Avoid using this code in production, it will fail for large data sets !
#See step 5 for production code.
requestUrl = "https://selectapi.datascope.refinitiv.com/RestApi/v1/Extractions/RawExtractionResults" + "('" + jobId + "')" + "/$value"
#AWS requires an additional header: X-Direct-Download
if useAws:
requestHeaders={
"Prefer":"respond-async",
"Content-Type":"text/plain",
"Accept-Encoding":"gzip",
"X-Direct-Download":"true",
"Authorization": "token " + token
}
else:
requestHeaders={
"Prefer":"respond-async",
"Content-Type":"text/plain",
"Accept-Encoding":"gzip",
"Authorization": "token " + token
}
r4 = requests.get(requestUrl,headers=requestHeaders)
if useAws:
print ('Content response headers (AWS server): type: ' + r4.headers["Content-Type"] + '\n')
#AWS does not set header Content-Encoding="gzip", so the requests call does not decompress.
#We therefore decompress using a separate call (to the gzip library).
uncompressedData = gzip.decompress(r4.content).decode("utf-8")
#We save the original compressed data (to save space):
fileName = filePath + fileNameRoot + ".step4.csv.gz"
print ('Saving compressed data to file: ' + fileName + ' ... please be patient')
else:
print ('Content response headers (TRTH server): type: ' + r4.headers["Content-Type"] + ' - encoding: ' + r4.headers["Content-Encoding"] + '\n')
#The requests call automatically decompresses the data, if header Content-Encoding="gzip".
uncompressedData = r4.text
#We save the uncompressed data (because it was automatically decompressed):
fileName = filePath + fileNameRoot + ".step4.csv"
print ('Saving uncompressed data to file: ' + fileName + ' ... please be patient')
#Save to file:
#with open(fileName, 'wb') as fd:
# for chunk in r4.iter_content(chunk_size=1024):
# fd.write(chunk)
#fd.close
#print ('Finished saving data to file:' + fileName + '\n')
#Display data:
print ('Decompressed data:\n' + uncompressedData)
#Note: variable uncompressedData stores all the data.
#This is not a good practice, that can lead to issues with large data sets.
#We only use it here as a convenience for the demo, to keep the code very simple.
#Step 5: get the extraction results, using the received jobId.
#We also save the compressed data to disk, as a GZIP.
#We only display a few lines of the data.
#IMPORTANT NOTE:
#This code is much better than that of step 4; it should not fail even with large data sets.
#If you need to manipulate the data, read and decompress the file, instead of decompressing
#data from the server on the fly.
#This is the recommended way to proceed, to avoid data loss issues.
#For more information, see the related document:
# Advisory: avoid incomplete output - decompress then download
requestUrl = "https://selectapi.datascope.refinitiv.com/RestApi/v1/Extractions/RawExtractionResults" + "('" + jobId + "')" + "/$value"
#AWS requires an additional header: X-Direct-Download
if useAws:
requestHeaders={
"Prefer":"respond-async",
"Content-Type":"text/plain",
"Accept-Encoding":"gzip",
"X-Direct-Download":"true",
"Authorization": "token " + token
}
else:
requestHeaders={
"Prefer":"respond-async",
"Content-Type":"text/plain",
"Accept-Encoding":"gzip",
"Authorization": "token " + token
}
r5 = requests.get(requestUrl,headers=requestHeaders,stream=True)
#Ensure we do not automatically decompress the data on the fly:
r5.raw.decode_content = False
if useAws:
print ('Content response headers (AWS server): type: ' + r5.headers["Content-Type"] + '\n')
#AWS does not set header Content-Encoding="gzip".
else:
print ('Content response headers (TRTH server): type: ' + r5.headers["Content-Type"] + ' - encoding: ' + r5.headers["Content-Encoding"] + '\n')
#Next 2 lines display some of the compressed data, but if you uncomment them save to file fails
#print ('20 bytes of compressed data:')
#print (r5.raw.read(20))
print ('Saving compressed data to file:' + fileName + ' ... please be patient')
fileName = filePath + fileNameRoot + ".step5.csv.gz"
chunk_size = 1024
rr = r5.raw
with open(fileName, 'wb') as fd:
shutil.copyfileobj(rr, fd, chunk_size)
fd.close
print ('Finished saving compressed data to file:' + fileName + '\n')
#Now let us read and decompress the file we just created.
#For the demo we limit the treatment to a few lines:
maxLines = 10
print ('Read data from file, and decompress at most ' + str(maxLines) + ' lines of it:')
uncompressedData = ""
count = 0
with gzip.open(fileName, 'rb') as fd:
for line in fd:
dataLine = line.decode("utf-8")
#Do something with the data:
print (dataLine)
uncompressedData = uncompressedData + dataLine
count += 1
if count >= maxLines:
break
fd.close()
#Note: variable uncompressedData stores all the data.
#This is not a good practice, that can lead to issues with large data sets.
#We only use it here as a convenience for the next step of the demo, to keep the code very simple.
#In production one would handle the data line by line (as we do with the screen display)
