response = ld.content.pricing.Definition(universe='IWM1WO=R', fields='STRIKE_PR2').get_data()

However, I cannot get the historical intraday STRIKE_PR2 values:

response = ld.content.historical_pricing.events.Definition(

    universe='IWM1WO=R',

    start='2026-08-05T18:00:00Z',

    end='2026-08-05T18:30:00Z',
    fields='STRIKE_PR2'

).get_data()

Is this just the wrong way to fetch this data, or is this historical data simply not available?

Manually copying and pasting is not realistic for the case since we need a large amount of data for analysis later. Is there any API available, or would scraping be permitted?

Thanks,

Yun

Initially, when I searched for the "SDC" database directly, I received a prompt stating that I do not have permission for the SDC add-on (please see attached Screenshot 1).

However, when I used the "Deal Screener" app and filtered by the "M&A" asset class, I was able to successfully view the M&A SDC deal data. While the Excel export button in the top right corner is grayed out (disabled), the PDF export option is fully functional.

Given this situation, I have three questions regarding the feasibility and ethical compliance of my research:

Do the Deal Screener provide the same data as in SDC platinum (I see that Deal Screener also has SDC Deal Number, et. al.)?

Am I permitted to download and use this SDC deal data for my research via the PDF export function? I want to ensure that using this data does not violate any licensing agreements, considering I do not have direct access to the SDC add-on.

If I am allowed to use this data, how should I accurately cite the data source in my research paper? Should I cite it as "LSEG Workspace" or "SDC Platinum"?

I want to make sure I am doing things correctly before I proceed further with my data collection. I would greatly appreciate your guidance on this matter.

Can you please let me know what is the correct endpoint to receive values for these?

And if possible maybe provide an example Python code. Thanks.

Hello,

We are subscribed to multiple currencies using direct feed and we ask rate, bid rate and contributor name for same.

When first time data is received for currency all the fields are received and updated fields in subsequent update messages for the same currency(We are using streaming way of getting feed using RSSL connection).

My question is :

If contributor field is changed, does it always guarantee that ask rate, bid rate fields will also get updated and vice versa.

i'm new on this topic, i hope i can explain myself. We have access to different symbols (RICs?) via web, but we would like to automate data extraction, that is done manually 2-3 times a day by employees.

We need following symbols at specific Date and Time (most intra-day at a certain time (8:30 and 9:00, 13:30 and 14:00):
• CHF=
• BRT-
• EUROBOB-ARA
• GO50PPM-E-ARA
• GOFRT-BASLE
• GLFRT-BASLE
mostly ask/bid price, open, close.

Furthermore, we need:
• LGOc1
• EBOBNWEMc0
Last, Close, Net.Change, Fair Value

What would be the right API? As there are a lot of documentations, i couldnt find the right spot to start. I'll use Java (Postman to test the endpoints), can you help me out? I was trying the RDP Postman Collection, but didnt find those values.

thanks in advance

I am a student at the University of Toronto. I am trying to access Aftermarket Research reports
via the Refinitiv Data Platform API.

Currently, my account can generate tokens, but the access token does not include the required
scopes (trapi.data.research.read and trapi.alerts.research.crud). As a result, I receive
403 "insufficient_scope" errors when trying to subscribe to research alerts or download reports.

Could you please advise:

1. How can I request these scopes to be enabled for my account?
2. Should this be requested through my institution’s subscription admin / account manager,
   or directly via LSEG support?

Thank you very much!

Best regards,Jiaqi Huang

attempt to a list of RIC info with relative start date and end date with Frq . the return result is not in wanted frequency

let say i want a 20 day data with daily or minutely frq, how can it work. Condition here id that i must use get_data endpoint.

sample RIC =NVDAT222516000.U

also

1. where can i know what is the field i can query for this symbol
   2, what is the gloabap; parameter can be use in get_data→parameters? what is the available frq here

I'm building a database in Excel with about 100 listed companies and indices. I want to pull historical closing price data for each of them—ideally with the dates in the first column, and each company's closing price in separate columns.

I've used the formula builder tool in Excel to get the latest share price for one company, but I’m not sure how to:

1. Pull daily closing prices (historical data) for each company

2. Arrange the data in a format where the dates are in one column and each company has its own price column

Is there a specific formula I should use for this? Or is there an easier way to set it up?

Would appreciate any guidance!
Thank you.

We've been having some issues with live data from RDMS. Basically, we look at refinitiv as close to 7:30 PM UK time as possible to estimate the settlement price for brent.

For example for Dec brent (LCOZ5) - we were expecting the settlement price of 70.38 and the value we got back was 70.18.

The price we got appeared to be a delayed price.

I am trying to download mutual fund holdings data using the following code in codebook:

import refinitiv.data as rd

from refinitiv.data.errors import RDError

import pandas as pd 

import time

LP=[ 'LP40215045', 'LP40212543', 'LP40235060', 'LP40209239', 'LP40209247', 'LP40215141', 'LP40212555', 'LP40209256', 'LP40187035', 'LP40189201']

fields = ['TR.FdInvestorPctPortfolio.Date',       

       'TR.FdInvestorPctPortfolio.SecurityOwnedRIC',               

       'TR.FdInvestorPctPortfolio'            

      ]

retry_max=5

retry_count=1

chunks=[]

chunk_size=5

df_holdings = pd.DataFrame()

for index in range(0, len(LP), chunk_size):

    chunk=LP[index:index+chunk_size]

    retry=True

    retry_count=1

    while retry==True:

        try:

            retry=False

            time.sleep(5)

            rd.open_session()

            df = rd.get_data(    

               universe = chunk,    

               fields = fields,          

               parameters={'SDate':'2004-01-01','EDate':'2024-12-31','Frq':'FQ','StartNum':1,'EndNum':316})

            if len(df):

                df_holdings=pd.concat([df_holdings,df],axis=0,ignore_index=True)

            else:

                df_holdings=df

        except RDError as err:

            if 'Backed error. 400 Bad Request' in err.message:

                retry_count=retry_count+1

                if retry_count<=retry_max:

                    print('Retry'+chunk)

                    retry=True

                else:

                    print('Retry reach max and unable to get the data for'+chunk)

            else:

                print(f"{err.code}: {err.message}")

display(df_holdings)

df_holdings.dropna()

df_holdings.to_excel('provats.xlsx')

However, this code appears to be too slow and fails when I request data for more than 10 mutual funds at once.

Since I need to download data for thousands of mutual funds, I would like to ask how I can optimize this code to handle larger batches more efficiently.

Thank you in advance for your help.

Mema Myftar

I’m attempting to retrieve historical mutual fund holdings using the following code:

However, the table appears as follows:

As you can see, the code returns only the RICs from the most recent holdings, not those from earlier dates. I have two questions:

1. How can I retrieve historical RICs for my mutual fund holdings prior to the current date?
2. How can I add a date column to the table so that each record is associated with its corresponding date?

Thank you in advance.

We prioritize the security and integrity of our data and communications. End-to-end mTLS provides an additional layer of security by ensuring that both the client and the server authenticate each other, thereby mitigating the risk of man-in-the-middle attacks and unauthorized access.

We would greatly appreciate your insights and suggestions on how we can address this concern. Specifically, we are interested in understanding:

• Are there any plans to implement end-to-end mTLS or similar robust authentication mechanisms in the near future?
• If mTLS is not feasible, are there alternative security measures that we can consider to enhance the security of our API interactions?

We value our partnership with DSS and are confident that together we can find a solution that meets our security requirements.

Thank you for your attention to this matter. We look forward to your response and any guidance you can provide.

Hi,

How can I get the Market Cap of a stock using the pricing/snapshots service?

https://api.refinitiv.com/data/pricing/snapshots/v1/?universe=/{RIC}Error

or is there any other service that I can get the updated market data that should be change during trading time according to the stock price.

Thanks!

The screeenshot above is the minute data of BTC.

Therefore, I wonder if there is a method to get long-term hourly or minute data.

Eg. I want to grab more historical data of the Cryptos for further research.

df_chunk = fundamental_and_reference.Definition(

                        universe= tickers,

                        fields = ['TR.CLOSEPRICE.date','TR.CLOSEPRICE','TR.Volume'],

                        params = {"SDate": 0, "EDate": -756, "FRQ": "d"})

why is it raising error?

Error processing chunk 1: Definition.__init__() got an unexpected keyword argument 'params'

I’m doing some academic research around ESG and Credit Spreads and am using LSEG data for it.

The data I’m downloading from the LSEG Data platform via API. I’m able to download ESG and fundamental data, however, I’m struggling with the CDS Data.

I understand that the LSEG Data API is the new version of the Refinitiv API. I’m therefore accessing the LSEG Data platform via API, however, I’m struggling to get daily, weekly or monthly data. If I would get one (e.g. daily) I would build the respective averages I need myself. It’s some annual composite I need in the end. I have read all the Developer Community threads and have built scripts with them, however, I’m still struggling. Attached are the two Python files I’ve been working with (converted to txt files for security reasons). 

The universe I will collect CDS data for are ultimately all companies that have an ESG rating / coverage.

What am I doing wrong???

Thank you!

Stevan

I would like to use the python refinitiv data library to pull historic maturities for a relative ticker, such as:

USDSROIS1Y=

Please advise how to do this? As ticker is relative, the maturity changes every day, and the field "MATUR_DATE" does not return anything on an historic basis.

Thanks,

I'm trying to collect bids for LCOc1 as soon as the bid change in realtime.

My main issue is that I have a 10 min delay.

_________________________________

import time
import pandas as pd
import eikon as ek
import os

# Set Eikon API Key for access to financial data
ek.set_app_key('API_KEY_HERE')

# Define the instrument symbols
instruments = ["LCOc1"]

# Define the directory and file path for storing the CSV file
csv_directory = r"C:\Users\YOUR_PATH\" # Use raw string to handle Windows paths
csv_file = os.path.join(csv_directory, "eikon_LCOc1_data.csv")

# Ensure the directory exists
os.makedirs(csv_directory, exist_ok=True)

# Define the DataFrame columns
columns = ["Timestamp", "LCOc1_BID", "LCOc1_Time"]

# Track the last recorded bid values to avoid duplicate logging
last_bids = {"LCOc1": None}

try:
# Infinite loop to continuously fetch data
while True:
# Fetch BID data for LCOc1 using Eikon API
df = ek.get_data(instruments, ["CF_BID", "CF_TIME"])[0] # Get the first item of the returned tuple

# Check if data was retrieved
if df is None or df.empty:
print("Error: No data retrieved. Retrying...")
time.sleep(1) # Wait before retrying
continue

# Extract the bid price and time for LCOc1
try:
lco_data = df.loc[df['Instrument'] == 'LCOc1', ['CF_BID', 'CF_TIME']].iloc[0]
lco_bid = lco_data['CF_BID']
lco_time = lco_data['CF_TIME']
except IndexError:
print("Error: Missing bid data for LCOc1.")
time.sleep(1)
continue

# Log only if there is a change in bid prices
if lco_bid != last_bids["LCOc1"]:
timestamp = pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S.%f")
new_row = pd.DataFrame([[timestamp, lco_bid, lco_time]], columns=columns)

# Append data to the CSV file (create if it doesn't exist)
new_row.to_csv(csv_file, mode="a", index=False, header=not os.path.exists(csv_file))

# Log the update
print(f"New Data at {timestamp}: LCOc1 BID = {lco_bid}, Time = {lco_time}")

# Update last recorded bid values
last_bids["LCOc1"] = lco_bid

# Small delay to avoid excessive API calls
time.sleep(0.05) # Adjust as needed

except KeyboardInterrupt:
print("\nProcess interrupted. Data saved in:", csv_file)

_________________________________

The result looks like this :

So I have a 1 hour delay due to GMT (no problem) + 10 minutes that I don't know where it comes from.

On my Workspace, I have Realtime data going through Private network.

Thanks for your help in advance

G

I am quite new on this platform and I need some assistance.

I currently want to download futures data with the following variables

date
volume

open interest last trading date

time to maturity

ticker symbol

etc

I kindly need assistance on how to go about it

This is the output i'm getting, I want column net premium as header and under sub header quarter or year, is it possible

import refinitiv.data as rdfrom refinitiv.data.content import fundamental_and_referencerd.open_session()prepared_metric_daily_codes_ev_ebitda = [ 'TR.FwdEVToEBITDA(Period=NTM,SDate=,EDate=,Frq=D)', 'TR.FwdPEG(Period=NTM,SDate=,EDate=,Frq=D)', 'TR.RecMean(SDate=,EDate=,Frq=D)', 'TR.NumOfBuy(SDate=,EDate=,Frq=D)', 'TR.NumOfSell(SDate=,EDate=,Frq=D)', 'TR.NumOfHold(SDate=,EDate=,Frq=D)', 'TR.NumOfStrongBuy(SDate=,EDate=,Frq=D)', 'TR.NumOfStrongSell(SDate=,EDate=,Frq=D)', 'TR.NumOfRecommendations(SDate=,EDate=,Frq=D)', 'TR.FwdEVToEBITDA(Period=NTM,SDate=,EDate=,Frq=D).calcdate']ticker = ['GH.OQ']response = fundamental_and_reference.Definition( ticker, prepared_metric_daily_codes_ev_ebitda,).get_data()

data = response.data.df

The 'TR.FwdEVToEBITDA.calcdate' field is returning NaT for GH.OQ but works fine for othertickers. Could you clarify why this is happening? Is this expected behavior due to missingcalculations for this specific RIC during the requested period, or is there another issue?

Issue 2: PETR3.SA Close Price Returning NaN and Duplicate DatesAdditionally, I have encountered another issue when retrieving Close Price, Price TargetMean, Enterprise Value (EV), and Market Capitalization for PETR3.SA.1. The Close Price (TR.CLOSEPRICE) field is returning only NaN values for PETR3.SAacross the entire requested period (2006-01-01 to 2025-02-18).2. The .date field (from TR.CompanyMarketCap.date) contains duplicate dates,specifically:○ 2012-08-17○ 2013-01-24Here is the request I am using:

import refinitiv.data as rdfrom refinitiv.data.content import fundamental_and_referencerd.open_session()daily_metrics = [ 'TR.CLOSEPRICE(SDate=2006-01-01,EDate=2025-02-18,Frq=D,Adjusted=1)', 'TR.PriceTargetMean(SDate=2006-01-01,EDate=2025-02-18,Frq=D)', 'TR.H.EV(SDate=2006-01-01,EDate=2025-02-18,Frq=D)', 'TR.CompanyMarketCap(SDate=2006-01-01,EDate=2025-02-18,Frq=D)', 'TR.CompanyMarketCap(SDate=2006-01-01,EDate=2025-02-18,Frq=D).date']ticker = ['PETR3.SA']response = fundamental_and_reference.Definition( ticker, daily_metrics,).get_data()data = response.data.dfduplicated_dates = data['Date'][data['Date'].duplicated(keep=False)].unique()print(duplicated_dates)['2012-08-17 00:00:00', '2013-01-24 00:00:00']

Pattern Affecting Multiple RICs and Other FieldsI have noticed that this issue is not limited to GH.OQ and PETR3.SA. This pattern of missingvalues (NaN) or duplicate timestamps also appears for other RICs when retrieving specificmetrics. In particular:● TR.FwdEVToEBITDA.calcdate is NaT for some tickers but works for others.● TR.ISOriginalAnnouncementDate also exhibits similar issues, with missing values orunexpected duplicates across different RICs.Could you clarify:● Why is the Close Price returning only NaN values for PETR3.SA? Is there a known issuewith this data?● Why are duplicate dates appearing in the .date field? Is this expected behavior, orshould I handle it differently?● Is there an underlying reason why some metrics (like TR.FwdEVToEBITDA.calcdateand TR.ISOriginalAnnouncementDate) return inconsistent values for specific RICs?● Are there recommended best practices to verify or mitigate these issues whenretrieving historical data?I appreciate your time and support in resolving these issues. Looking forward to yourguidance.