volume data across venue

Hello,

I recently noticed a discrepancy in trading volume from Eikon Data API for Canadian stocks. In particular, when comparing the consolidated volume for a ticker (say, 'TD') using the RIC = 'TD.CCP' with the aggregated volume for the ticker across all trading venues, I found that before 2024 they are quite close to each other, but since 2024, the consolidated volume is about a half of the aggregated volume. See the ratio of Agg-Volume to Consolidated-Volume below.

I have tested for several tickers with similar results. So I wonder if the '*.CCP' RICs under report the total volume for Canadian stocks recently?

Look forward to advice and suggestions.

Thanks,

Thanh-Hai

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Jirapongse

@tta

Thank you for reaching out to us.

This forum is dedicated to software developers using LSEG APIs. The moderators on this forum do not have deep expertise in every bit of content available through LSEG products, which is required to answer content questions such as this one.

The best resource for content questions is the Helpdesk support team, which can be reached by submitting queries through LSEG Support. The support team will either have the required content expertise ready available or can reach out to relevant content experts to get the answer for you.

Please include the URL of this discussion in your raised question to prevent it from being redirected back to this Q&A forum.

Otherwise, you can share the snippet code that is used to retrieve that data. Therefore, we can run the code in our environment to check the raw data.

tta

Thanks for your response. Here is the code I use to generate the chart,

def get_volume(RICs:List[str], fields:List[str]=['TR.Volume.Date', 'TR.Volume'], period:dict={'SDate':'2024-01-01', 'EDate':'2025-12-31'}):
'''
Core function to get volume data by venues from Workspace's API
(Workspace must be running and port 9000 is enabled)

'''
try:
    df = ld.get_data(RICs, fields=fields, parameters=period)
    # convert data type:
    df['Volume']=pd.to_numeric(df.Volume, errors='coerce')
    return df
except Exception as e:
    print(f"An unexpected error occurred: {e}")

def search_equity_venue (ticker:str, exch_ctry_code:str="'G:6J'"):
'''
Search venue data for an equity
- Inputs:
1. Ticker: string
2. exch_ctry_code: string, Exchange Country code, e.g., US = 'G:6J', CA = 'G:8W'
'''
#ld.open_session()
import refinitiv.data.content.search as search
search = search.Definition(
view = search.Views.EQUITY_QUOTES,
top = 10000,
filter = f"(AssetState ne 'DC' and SearchAllCategoryv2 eq 'Equities' and ExchangeType ne 'Consolidated' and (RCSExchangeCountry xeq {exch_ctry_code} and (TickerSymbol eq {ticker!r})))",
select = "DTSubjectName,ExchangeName,RIC,IssueISIN,Gics,AssetState,BusinessEntity,PI,SearchAllCategoryv3,SearchAllCategoryv2,SearchAllCategory,RCSTRBC2012Leaf,RCSAssetCategoryLeaf,RCSCurrencyLeaf,ExDividendDate,CUSIP,CinCUSIP,SEDOL,TickerSymbol,RCSExchangeCountryLeaf,RCSExchangeCountry"
).get_data()
return search.data.df

def get_volume2(ticker:str, ticker_con:str, stDate:str='2025-01-02', enDate:str='2026-02-28'):

# Get venue RIC:
ven_search = search_equity_venue(ticker, exch_ctry_code="'G:8W'")
ven_RICS = ven_search.RIC.tolist()

# get ven-volume:
vol_ven = ven.get_volume(ven_RICS, period={'SDate':stDate, 'EDate':enDate})
# aggregate by date:
vol_agg = vol_ven.groupby('Date', as_index=False).agg({'Volume':'sum'})
vol_agg.rename(columns={'Volume':'Venue_Agg_Volume'},inplace=True)

# get consolidate volume:
vol_con = ven.get_volume(ticker_con, period={'SDate':stDate, 'EDate':enDate})

vol_comb = pd.merge(vol_con, vol_agg, on='Date')
vol_comb['Ratio'] = vol_comb['Venue_Agg_Volume']/vol_comb['Volume']

return vol_comb

ticker = 'TD'

vol_TD = get_volume2('TD', 'TD.CCP', stDate='2019-01-02')

vol_TD.plot(x='Date', y='Ratio')

tta

Sorry for a reference mistake in function get_volume2(). Please remove the prefix: ven. in ve.get_volume() with get_volume() defined above. And pd is of course for pandas.

Jirapongse

@tta

I checked the code and found that it retrieves the historical volumes with the following two queries.

Get the historical volumes for these RICs

df1 = ld.get_data(
    universe = ['TD.TO', 'TD.GO', 'TD.ALP', 'TD.CD2', 'TD.CXC', 'TD.NEO', 'TD.NLB', 'TD.NBC', 'TD.LNX', 'TD.OMG', 'TD.NED', 'TD.CXX'],
    fields = ['TR.Volume.Date', 'TR.Volume'],
    parameters = {'SDate': '2019-01-02', 'EDate': '2026-02-28'})

df1

Then, sum the volumes with the dates.

df1.groupby('Date', as_index=False).agg({'Volume':'sum'})

Get the historical volumes for this RIC (TD.CPP).

df2 = ld.get_data(
    universe = 'TD.CCP',
    fields = ['TR.Volume.Date', 'TR.Volume'],
    parameters = {'SDate': '2019-01-02', 'EDate': '2026-02-28'})

df2

Finally, it then compares the values in df2 with the aggregated volumes from df1 for each date. The comparison shows discrepancies. There is no problem in the code. It could be a content related question.

You can contact the content support team directly through LSEG Support to clarify how the volume of TD.CPP relates to the combined volumes of those RICs.

Please include the URL of this discussion in your raised question to prevent it from being redirected back to this Q&A forum.

tht

Thanks for the confirmation of data discrepancy.

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