Historical OHLC request freezes for 50+ ETF RICs over a 14-year range

I am trying to extract daily OHLCV data for a list of instruments using the Refinitiv Data Library.

Here is the code I am using:

import refinitiv.data as rd
import pandas as pd

rd.open_session()
instruments = [
"IWDA.L","VAVUAG.L","VWRP.L","VWRA.L","EEDS.L","ISACI.L","SWDA.L","EIMI.L",
"SASU.L","VWRL.L","IUIT.L","IUSA.L","XDEW.L","EMIM.L","LQDE.L","IITU.L",
"IHYG.L","IMEU.L","SSAC.L","CBU7.L","VWRD.L","XD9U.L","ISLQDA.L","SDIA.L",
"IEMB.L","XDWE.L","IHYU.L","JPEA.L","SMEA.L","XSPU.L","DTLA.L","AGGU.L",
"IEAA.L","IHYA.L","ISIJPA.L","IUAA.L","VEVE.L","SPY4.L","HMWO.L","SDIG.L",
"IGLT.L","FLOA.L","CORP.L","LYCSH2.L","LYXEUC.PA","XAD1.MI","CMOP.L","FEDF.L","COMO.PA"
]

ohlc_fields = ['OPEN_PRC', 'HIGH_1', 'LOW_1', 'TRDPRC_1', 'ACVOL_UNS']

print("Fetching Daily Data... This may take a moment due to the 14-year range.")
df_daily = rd.get_history(
universe=instruments,
fields=ohlc_fields,
interval="daily",
start="2012-01-01",
end="2026-03-11")

Issue:
The request hangs at the message:

Fetching Daily Data... This may take a moment due to the 14-year range.

The call does not complete even after several hours.

What I need help with:

Is my code correct for retrieving OHLCV history for these RICs?
Is there a faster or more efficient way to extract daily data for a large list of instruments?
How can I extract all data into one combined output file?
I also need to retrieve minute‑level data for the same symbols for the period 2025‑11‑01 to 2026‑03‑12. What is the recommended approach for that?

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All comments

Jirapongse

@myla.dumanhog

Thank you for reaching out to us.

Requesting a large amount of data in a single call can lead to unexpected issues on both the client and server sides. It is better to split the request into smaller ones

Regarding this historical real-time data, under the hood, the historical pricing API endpoint used by ld.get_history to retrieve historical real-time data does not support batch requests. It only processes one RIC per request, but the library manages this internally on behalf of the application.

A possible solution is to request one RIC at a time or use the historical_pricing asynchronous interface to retrieve data with one RIC per call

Regarding the intraday data (minute-level), the historical pricing also support 1 minute interval historical data. For example:

response = historical_pricing.summaries.Definition(
    universe='IWDA.L',
    interval=Intervals.MINUTE,
    fields = ['HIGH_1','LOW_1','OPEN_PRC','TRDPRC_1','NUM_MOVES','ACVOL_UNS'],
    start='2026-03-10 03:00:00',
    end= '2026-03-10 12:00:00',
    ).get_data()
response.data.df

wasin.w V3

Hello @myla.dumanhog

There is the API call rate limit information on the Data Library Usage and Limits Guideline page. You should check it too.

I see you are using the RD Library (Data library version 1), could you please try the https://developers.lseg.com/en/api-catalog/lseg-data-platform/lseg-data-library-for-python (Data Library version 2) which is the latest version and include bug fixes from the version 1? (see https://developers.lseg.com/en/article-catalog/article/essential-guide-to-the-data-libraries article for more detail).

Question 1: Is my code correct for retrieving OHLCV history for these RICs?

Answer: Yes. Your usage of the Data Library get_history() method is valid. I have tried the code in my local Workspace Codebook environment (Desktop Session). It works fine on my end.

Question 2: Is there a faster or more efficient way to extract daily data for a large list of instruments?

Answer: I see that you have request a daily interval of 14 years historical data. You may try to call the get_history() method multiple times with a smaller range of time (start - end) such as the first call for 2012-2015, the second call for 2015-2018, etc.

Update: Using the Async Interface that my colleague has mentioned also should be help too.

Then you can use Pandas's methods() to merge all result data.

Question 3: How can I extract all data into one combined output file?

Answer: I do not understand the question. All RICs data in a get_history method called always available in a single dataframe object.

Question 4: I also need to retrieve minute‑level data for the same symbols for the period 2025‑11‑01 to 2026‑03‑12. What is the recommended approach for that?

Answer: You can just change an interval parameter to "minute".

df_minute = ld.get_history(

    universe=instruments,

    fields=ohlc_fields,

    interval="minute",

    start="2025-11-01",

    end="2026-03-12"

)

Note: The call takes a long time since it is a "minute" interval. You may need to split the call in to multiple calls with shorter time ranges and shorter RICs list.

Update: Using the Async Interface that my colleague has mentioned also should be help too.

You can use the python built-in help() method to check the method detail including the supported interval.

help(ld.get_history)

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