@GoGoGroundhog I'm not aware of an endpoint designed for this type of metadata within rd/rdp libraries. The workaround I used is Average Volume Analytics API endpoint that outputs this data in info section of the response. This API covers all global equities. This solution obviously depends on your ability to access this API.

One more caveat is that these session times are Refinitiv session times (in line with our timeseries data/apps/charts), but not the session times established by the exchanges themselves (so for Nasdaq Refinitiv open/close is 14:30 UTC/21:02 UTC, while exchange open/close is 14:30 UTC/21:00 UTC). These 2 mins actually make a big difference. Closing cross trade is reported a few milliseconds after 21:00 and these additional 2 minutes allow to account for this important part of the session's volume. I hope this helps.

import refinitiv.data as rd
from datetime import datetime

session = rd.session.desktop.Definition().get_session()
rd.session.set_default(session)
session.open()

def mkt_hours_query(symbol):
    request_body={
        'universe': [symbol],
        'requestTime': datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ"),
        'columns': [
            {'id': '1',
            'field': 'avat',
            'blendedVenue': False,
            'filters': {'official': True},
            'averagingInterval': 20,
            'startTime': {'marketTime': True},
            'endTime': {'marketTime': True}}
        ]
    }

    request = rd.delivery.endpoint_request.Definition(
        url = "https://api.refinitiv.com/data/analytics/average-volume-analytics/v2/request",
        method= rd.delivery.endpoint_request.RequestMethod.POST, 
            body_parameters= request_body)
    response = request.get_data()
    open_time_utc = datetime.strptime(response.data.raw['info'][symbol]['1']['startTime'], "%Y-%m-%dT%H:%M:%SZ").time()
    close_time_utc = datetime.strptime(response.data.raw['info'][symbol]['1']['endTime'], "%Y-%m-%dT%H:%M:%SZ").time()
    return open_time_utc, close_time_utc

mkt_hours_query('VOD.L) 

@GoGoGroundhog thanks for your question - so for timeseries history - you can request which session(s) you want and this will adjust to whichever RIC you present:

response = historical_pricing.summaries.Definition(
    universe = "VOD.L", 
    interval = Intervals.ONE_MINUTE,     # Supported intervals: ONE_MINUTE, FIVE_MINUTES, TEN_MINUTES, THIRTY_MINUTES, ONE_HOUR
    count = 500,
    sessions = [
        MarketSession.PRE, 
        MarketSession.NORMAL, 
        MarketSession.POST
    ]
).get_data()
response.data.df

Is this is the sort of thing you are after? I hope this can help.

@GoGoGroundhog Also you can check the times as well by requesting history for any instrument for a day say:

response = historical_pricing.summaries.Definition(
    universe = "VOD.L", 
    interval = Intervals.TEN_MINUTES,     # Supported intervals: ONE_MINUTE, FIVE_MINUTES, TEN_MINUTES, THIRTY_MINUTES, ONE_HOUR
    #count = 500,
    start = datetime.datetime(2023,2,13,0,0,0),      
    end = datetime.datetime(2023,2,13,23,59,59),   
    sessions = [
        #MarketSession.PRE, 
        MarketSession.NORMAL, 
        #MarketSession.POST
    ]
).get_data()
response.data.df

I hope this can help.

Hi Jason, thanks for your answer. Unfortunately this is not what we are after.
We need official trading session metadata for several purposes, e.g. for filtering realtime updates outside the session and we also present trading sessions to the end-user as additional information.