Tick History Futures Quota has been reached or exceeded

I am getting below error in pythong when using RTH REST Api:

Quota Message: INFO: Tick History Futures Quota Count Before Extraction: 0; Instruments Approved for Extraction: 0; Tick History Futures Quota Count After Extraction: 0, 100% of Limit; Tick History Futures Quota Limit: 0
Quota Message: ERROR: The RIC 'STXE' in the request would exceed your quota limits. Adjust your input list to continue.
Quota Message: WARNING: Tick History Futures Quota has been reached or exceeded

I have checked from the GUI and it says below:

What should i do in this case ? Do i need to change anything in Python code or need to contact ny Refinitiv Account manager ?

Below is my Python Code:

# coding: utf-8


# In[2]:


# ====================================================================================
# On Demand request demo code, using the following steps:
# - Authentication token request
# - On Demand extraction request
# - Extraction status polling request
#   Extraction notes retrieval
# - Data retrieval and save to disk (the data file is gzipped)
#   Includes AWS download capability
# ====================================================================================


# Set these parameters before running the code:


filePath = "P:\\ODI_Projects\\temp\\"  # Location to save downloaded files
fileNameRoot = "Python_Test"  # Root of the name for the downloaded files
useAws = True
# Set the last parameter above to:
# False to download from TRTH servers
# True to download from Amazon Web Services cloud (recommended, it is faster)


# Imports:
import requests
import json
import shutil
import time
import gzip
import os


# ====================================================================================
# Step 1: token request


#proxy setting
os.environ["HTTPS_PROXY"] = "http://proxy:8080"


reqStart = "https://selectapi.datascope.refinitiv.com/RestApi/v1"
requestUrl = reqStart + "/Authentication/RequestToken"


requestHeaders = {
    "Prefer": "respond-async",
    "Content-Type": "application/json"
}


requestBody = {
    "Credentials": {
        "Username": myUsername,
        "Password": myPassword
    }
}


r1 = requests.post(requestUrl, json=requestBody, headers=requestHeaders)


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('Replace myUserName and myPassword with valid credentials, then repeat the request')


# In[7]:


# Step 2: send an on demand extraction request using the received token


requestUrl = reqStart + '/Extractions/ExtractRaw'


requestHeaders = {
    "Prefer": "respond-async",
    "Content-Type": "application/json",
    "Authorization": "token " + token
}


requestBody = {
    "ExtractionRequest": {
        "@odata.type": "#DataScope.Select.Api.Extractions.ExtractionRequests.TickHistoryRawExtractionRequest",
        "IdentifierList": {
            "@odata.type": "#DataScope.Select.Api.Extractions.ExtractionRequests.InstrumentIdentifierList",
            "InstrumentIdentifiers": [{
                "Identifier": "STXEc1",
                "IdentifierType": "Ric"
            }]
        },
        "Condition": {
            "MessageTimeStampIn": "GmtUtc",
            "ReportDateRangeType": "Range",
            "QueryStartDate": "2021-08-20T12:00:00.000Z",
            "QueryEndDate": "2021-08-20T12:10:00.000Z",
            "Fids": "22,30,25,31,14265,1021",
            "ExtractBy": "Ric",
            "SortBy": "SingleByRic",
            "DomainCode": "MarketPrice",
            "DisplaySourceRIC": "true"
        }
    }
}


r2 = requests.post(requestUrl, json=requestBody, headers=requestHeaders)
r3 = r2


# 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))


# In[8]:


# 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')


# In[9]:


# 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 TRTH, 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.




# Display data:
# ZF 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.




# In[10]:


# 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 = requestUrl = reqStart + "/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))




fileName = filePath + fileNameRoot + ".step5.csv.gz"
print('Saving compressed data to file:' + fileName + ' ... please be patient')
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)




# In[11]:


# Step 6 (cosmetic): formating the response received in step 4 or 5 using a panda dataframe


from io import StringIO
import pandas as pd


timeSeries = pd.read_csv(StringIO(uncompressedData))
timeSeries


# In[ ]: