Request Tick History Time and Sales Data using Python

Moragodkrit Chumsri
Developer Advocate Developer Advocate

Last Updated: 21 Oct 2021


LSEG Tick History is an Internet-hosted product on the DataScope Select platform that provides SOAP-based and a REST API for unparalleled access to historical high-frequency data across global asset classes dating to 1996. However, a legacy SOAP-based API is also available and is scheduled to be sunset. Therefore client who still uses SOAP-based API may need to migrate their application to use REST API instead.

One of the main use cases for an application using Tick History API is to use the API requesting Time and Sales data for RICs.  For SOAP-based API, the client can use the SubmitRequest method to request Time and Sales data. To migrate to the new API, the client has to re-implement the application to use REST instead.

This example demonstrates how to use the new REST API to request Time and Sales data. The example also explains how to interact with the REST API by not using the REST API Toolkit.Net SDK. Instead, it uses Python programming to demonstrate the API usages as Python script can work across OS and very easy to deal with the HTTP request. This example applies the steps provided in the Tick History REST API tutorial section, which locates on Developer Portal to implement the Python example.

On-Demand extraction request

Basically, there are two kinds of Tick History custom solution that are

  • On-Demand extractions- all report attributes specified in the single HTTP request that is submitted to run immediately.
  • Stored & scheduled- The user defines report attributes, stored for future use, and schedules the report to run at set times or triggered by events.

This example uses only On-Demand extraction to request Tick data/Tick History Time and Sales. However, the raw data extraction workflow can apply to several types of On Demand historical data requests:


    - Tick data

    - Market depth

    - Intraday bars

    - End of Day data

More details can be found at Tick History API User Guide

This example uses the following steps to implement On Demand Extraction:

1.  Get Authentication Token from DSS server.

2.  Retrieve available field list from the server (optional). We will skip this steps as the example will import or read reqeust payload from JSON file so we will specify required field list in JSON file instead.

3.  Request historical data from the server, using an on demand extraction request. The request will be queued, then executed.

4.  Check the request status. Poll it until the request is completed and get JobID.

5.  Retrieve the data using the JobID from step 4 and write it to file.

On-Demand Tick Data extraction request payload

In this request we need to:

  • Specifying the instrument on which we are reporting.
  • Specifying the type of report (Time and Sales)
  • Specifying the Time and Sales report type’s Quote and Trade fields to include in the report.
  • Specifying the report’s date range and other conditions.
  • Submitting the report request to run immediately.

1. Authentication Request

To access Tick Historical data, we have to pass Token to the request header, therefore the first step is to get a new Token from DSS server. This example will apply the method to send an Authentication request from Programming without SDK Tutorial to send a request.

2. Send On-Demand Request

HTTP Request

  • Endpoint URL:
  • Method: POST
  • Headers:

Prefer: respond-async

Content-Type: application/json

Authorization: <Authorization Token>


The body of the request must mention it is an extraction request. It contains several parts:

* **The type of extraction**: as we want tick data we set a value of **TickHistoryTimeAndSalesExtractionRequest**.

* **The list of field name**s: these were determined in the previous step of this tutorial.

* **The list of instrument identifiers**, each one with its type. Below sample, we define one instrument using a RIC.

* **The conditions**: they include the date range for the request.

Please find available parameters from Tick History REST API User Guide.

  • Sample JSON HTTP request payload


  "ExtractionRequest": {

    "@odata.type": "#DataScope.Select.Api.Extractions.ExtractionRequests.TickHistoryTimeAndSalesExtractionRequest",

    "ContentFieldNames": [

      "Trade - Price",

      "Trade - Volume",

      "Trade - Exchange Time",


    "IdentifierList": {

      "@odata.type": "#DataScope.Select.Api.Extractions.ExtractionRequests.InstrumentIdentifierList",

      "InstrumentIdentifiers": [{

        "Identifier": "CARR.PA",

        "IdentifierType": "Ric"



    "Condition": {

      "MessageTimeStampIn": "GmtUtc",

      "ApplyCorrectionsAndCancellations": false,

      "ReportDateRangeType": "Range",

      "QueryStartDate": "2016-09-29T00:00:00.000Z",

      "QueryEndDate": "2016-09-29T12:00:00.000Z",

      "DisplaySourceRIC": true




HTTP response

  • After we send an HTTP request message to the DSS server, the application will receive response message back and we expected to receive HTTP response status code of 202 Accepted, and the header will contain a location URL.

The next step is to check the request status by polling the location URL regularly until it returns an HTTP response status code 200 OK.

  • If the request is for a small amount of data, the response could have an HTTP status of 200 OK, and the body will contain a jobId and Notes. According to DATASCOPE SELECT document, the Notes provides information about the extraction execution, including processing statistics, embargo delays, suppressed items, and warnings.
  • We skip the step where we check the request status and go directly to the last step, which is to retrieve the data using the jobId. Other HTTP status codes can be encountered, follow this link for a full list with detailed explanations. It is strongly recommended that the code handle all possible status codes.

3. Check request status

HTTP request

Skip this step if the previous step returned an HTTP status of 200 OK.

If the previous step returned an HTTP status of 202 Accepted, this step must be executed and repeated in a polling loop until it returns an HTTP status of 200 OK.

  • URL:

This is the location URL, taken from the 202 response header received in the previous step.

  • Method: GET
  • Headers:

Prefer: respond-async

Content-Type: application/json

Authorization: <Authorization Token>

HTTP response

If we receive an HTTP status 202 Accepted response (the same as in the previous step), it means the request has not yet completed. We must wait a bit and check the request status again. If we receive an HTTP status 200 OK response, the body will contain a jobId and Notes and we can go to the last step to retrieve the data using the jobId.

4.Retrieve data

HTTP request

It is mandatory to have received a 200 OK response with a JobID from a previous step before proceeding with this last step.

  • URL:

Note the jobId value (0x058dcda3c29b5841) used as parameter in the URL:

  • Method: GET
  • Headers:
  • Prefer: respond-async
  • Content-Type: Accept-Encoding: gzip, deflate
  • Authorization:

HTTP response

We should get a response of this type:

Status:200 OK

Relevant headers:


Content-Encoding: gzip

Content-Type: text/plain



Here is the beginning of the response content, which for the above query contains more than 3000 lines:


#RIC,Domain,Date-Time,Type,Price,Volume,Exch Time

CARR.PA,Market Price,2016-09-29T07:00:11.672415651Z,Trade,23.25,63,07:00:11.000000000

CARR.PA,Market Price,2016-09-29T07:00:11.672415651Z,Trade,23.25,64,07:00:11.000000000

CARR.PA,Market Price,2016-09-29T07:00:11.672415651Z,Trade,23.25,27,07:00:11.000000000


Here is the end of the response content:


CARR.PA,Market Price,2016-09-29T11:59:46.352157769Z,Trade,23.25,8,11:59:46.000000000

CARR.PA,Market Price,2016-09-29T11:59:46.352798946Z,Trade,23.25,207,11:59:46.000000000

CARR.PA,Market Price,2016-09-29T11:59:46.552806988Z,Trade,23.245,182,11:59:46.000000000

Python Example


  • To run the example user should have python 2.7 or 3.6 installed on OS. User can download python installer from below link. Basically, the user can open the example with any text editor. There are a free Python IDE such as PyCharm Community edition and Visual Studio Code user can use to open python source file.
  • In order to access the Tick Historical end point, the user must have DSS account with permission to access Tick Historical’s REST API. Please contact LSEG Account representative if you need a new account or additional permission.
  • To use HTTP request and get responses back, This example use Python requests module. If user don’t have requests installed in python library, please run below pip install command to install requests module.

pip install requests

Step1: Get Authentication Token from DSS server.

Ask the user to input DSS username and password and then send an authentication request to DSS server to get a new Authentication Token.


def RequestNewToken(username="",password=""):

    _AuthenURL = ""

    _header= {}


    _header['Content-Type']='application/json; odata.metadata=minimal'







    print("Send Login request")



    if resp.status_code!=200:

        message="Authentication Error Status Code: "+ str(resp.status_code) +" Message:"+resp.text

        raise PermissionError(dumps(message))


    return loads(resp.text)['value']

Step2: Send On Demand Extraction request.

We have to import request and json module in the example. The json module is required to manage JSON data in the HTTP request and response.


from json import dumps, loads, load

from requests import post

from requests import get

import pandas as pd

Request historical data from the server, using an on demand extraction. We need to pass json_payload which is request body to the server.


#Function ExtractRAW

def ExtractRaw(token,json_payload):


    #Setup Request Header



    _header['Content-Type']='application/json; odata.metadata=minimal'




    #Send HTTP post message to DSS server using extract raw URL



To pass json_payload to HTTP post, we read the JSON request message from JSON file.


#Read the HTTP request body from JSON file. So we can change the request in JSON file instead.

queryString = {}

with open(_jsonFileName, "r") as filehandle:





Step3: Polling request status from the server.

We have to check the request status. This example uses a simple pooling loop to check status until it get status code 200 (Completed). Then we can get jobID from the response body.



        #Raise exception with error message if the returned status is not 202 (Accepted) or 200 (Ok)

        if resp.status_code!=200:

            if resp.status_code!=202:

                message="Error: Status Code:"+str(resp.status_code)+" Message:"+resp.text

                raise Exception(message)


            #Get location from header


            print("Get Status from "+str(_location))



            #pooling loop to check request status every 2 sec.

            while True:


                _pollstatus = int(resp.status_code)


                if _pollstatus==200:




                sleep(_retryInterval) #wait for _retryInterval period and re-request the status to check if it already completed


        # Get the jobID from HTTP response

        json_resp = loads(resp.text)

        _jobID = json_resp.get('JobId')

        print("Status is completed the JobID is "+ str(_jobID)+ "\n")


        # Check if the response contains Notes and print it.

        if len(json_resp.get('Notes')) > 0:


            for var in json_resp.get('Notes'):



Step4: Retrieve the data using the JobID and Write to file.

We use reqeusts.get to retrieve result and then write the content of the response message to file. Then we use dataframe to read file .csv.gz and print sample data to console.


# Request should be completed then Get the result by passing jobID to RAWExtractionResults URL


        print("Retrieve result from "+_getResultURL)



        #Write Output to file.

        outputfilepath = str(_outputFilePath + _outputFileName + str(os.getpid()) + '.csv.gz')

        if resp.status_code==200:

            with open(outputfilepath, 'wb') as f:



        print("Write output to "+outputfilepath+" completed\n")

        print("Below is sample data from "+ outputfilepath)

        #Read data from csv.gz and shows output from dataframe head() and tail() 





There are four parameters that the user can configure in this example.

  1. The output file path (_outputFilePath) ,
  2. File name used to write output file(_outputFileName),
  3. The _retryInterVal is the time that the example will wait and check for the status code of the request.  Note thatYou could set a short polling time to actually see that the initial responses have status 202, followed by a status 200. But in production code, you would set a reasonable polling interval of 30 seconds, following the best practices.
  4. JSON file name that the example used to import the JSON request body and pass it to the HTTP request message. See sample JSON data from section Sample JSON HTTP request payload.





How to run the example

This example requires JSON file "TickHistoricalRequest.json" which contains JSON payload for the HTTP request. We can modify the JSON file to change the request body.

Command line


Then we should see the following console output





Login to DSS Server

Enter DSS Username:9009xxx

Enter DSS Password:

Send Login request




Status Code=202

Get Status from'0x05bf56b371cb2f86')





Status is completed the JobID is 0x05bf56b371cb2f86




Extraction Services Version 11.1.37014 (36b953b5a32e), Built Jul  6 2017 18:36:01

User ID: 9009975

Extraction ID: 2000000001287334

Schedule: 0x05cd8b77f43b2f96 (ID = 0x0000000000000000)

Input List (1 items):  (ID = 0x05cd8b77f43b2f96) Created: 21-07-2017 17:18:10 Last Modified: 21-07-2017 17:18:10

Report Template (4 fields): _OnD_0x05cd8b77f43b2f96 (ID = 0x05cd8b7808bb2f96) Created: 21-07-2017 17:14:56 Last Modified: 21-07-2017 17:14:56

Schedule dispatched via message queue (0x05cd8b77f43b2f96), Data source identifier (4D3E6BEB8A4A45E8ACE4FB5513F79DE2)

Schedule Time: 21-07-2017 17:14:56

Processing started at 21-07-2017 17:14:57

Processing completed successfully at 21-07-2017 17:18:11

Extraction finished at 21-07-2017 10:18:11 UTC, with servers: tm02n01, TRTH (182.065 secs)

Instrument <RIC,SCB.BK> expanded to 1 RIC: SCB.BK.

Quota Message: INFO: Tick History Cash Quota Count Before Extraction: 500; Instruments Extracted: 1; Tick History Cash Quota Count After Extraction: 500, 100% of Limit; Tick History Cash Quota Limit: 500

Manifest: #RIC,Domain,Start,End,Status,Count

Manifest: SCB.BK,Market Price,2016-01-04T02:30:03.877259500Z,2017-01-04T09:35:53.196802542Z,Active,1325262




Retrieve result from'0x05bf56b371cb2f86')/$value

Write output to ./TestOutput6856.csv.gz completed


Below is sample data from ./TestOutput11860.csv.gz

     #RIC        Domain                       Date-Time   Type  Bid Price  \

0  SCB.BK  Market Price  2016-01-04T02:30:03.877259500Z  Quote      118.5   

1  SCB.BK  Market Price  2016-01-04T02:30:03.925248539Z  Quote        NaN   

2  SCB.BK  Market Price  2016-01-04T02:30:04.021279825Z  Quote        NaN   

3  SCB.BK  Market Price  2016-01-04T02:30:06.580796738Z  Quote        NaN   

4  SCB.BK  Market Price  2016-01-04T02:30:10.536533579Z  Quote        NaN   


   Bid Size  Ask Price  Ask Size  

0   10000.0        NaN       NaN  

1       NaN        NaN    6600.0  

2     200.0        NaN       NaN  

3    2300.0        NaN       NaN  

4       NaN        NaN   10800.0  


           #RIC        Domain                       Date-Time   Type  \

1325257  SCB.BK  Market Price  2017-01-04T09:35:41.880047249Z  Quote   

1325258  SCB.BK  Market Price  2017-01-04T09:35:42.871247756Z  Quote   

1325259  SCB.BK  Market Price  2017-01-04T09:35:43.407336298Z  Quote   

1325260  SCB.BK  Market Price  2017-01-04T09:35:53.196802542Z  Quote   

1325261  SCB.BK  Market Price  2017-01-04T09:35:53.196802542Z  Quote   


         Bid Price  Bid Size  Ask Price  Ask Size  

1325257        NaN       NaN        NaN  293300.0  

1325258        NaN  460000.0        NaN       NaN  

1325259        NaN  460500.0        NaN       NaN  

1325260      155.5   63600.0        NaN       NaN  

1325261        NaN       NaN      156.0  435500.0