running the code from Building historical index constituents | Devportal to access STOXX600 constitutes result in partly >600 constitutes as Leaver/Joiners for one day do not even out. How to solve this issue? For sure, it would be a solution to run get_constituents_as_of for every today, however, this is quite API-request intense.

Thank you in advance!

I want to analyze historical (quarterly) earnings per share forecasts from the management. How and where do I find them in the workspace or in Datastream?

Thanks in advance and best!

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.

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.

var response = Summaries.Definition().Universe("CFI2Z5")
.Interval(Summaries.Interval.PT5M)
.Sessions(HistoricalPricing.Sessions.normal)
.Adjustments(new List<Summaries.Adjustments>{ Summaries.Adjustments.RPO, Summaries.Adjustments.exchangeCorrection, Summaries.Adjustments.CRE, Summaries.Adjustments.RTS })
.End(DateTime.UtcNow.AddMinutes(1))
.Count(200).GetData();
Common.DisplayTable("Historical Intraday Summaries", response);

When I compare the returned volumes to the data table of the 5m chart of CFI2Z5, the ACVOL_UNS field often matches the chart values, but also sometimes we get much larger volumes for ACVOL_UNS:

Thanks,
Nick

With eikon I can get the CLOSE values (see below in a.).

Lseg doesn’t have CLOSE or anything similar. (see below in b.)

This is using your own codebook.

Please advise.

a. Refinitiv.Eikon

df_hist_ek = ek.get_timeseries(

    ["CAD="],

    start_date='2025-03-18',

    end_date='2025-03-24',

    interval='daily'

)

df_hist_ek

b. Lseg-data

df_hist_ld=ld.get_history(

    universe=["CAD="],

    interval='daily',

    start='2025-03-18',

    end='2025-03-24'

)

df_hist_ld

For a project I would like to extract monthly data.
What I need is for each month historically to extract which stocks were in a specific exchange.
For example: the names of all the stocks that where in the euronext Paris exchange in the month of march 2008. (all those that were active at that time, also those that were delisted in the meantime)
Is there a formula to extract this data?

Thanks a lot for your help!

I am currently looking for a way to retrieve the % of outstanding shares held by ETFs of a single equity. I know that I can do this manually by looking at the Fund Ownership of an individual equity, however, I am looking at 150 equities over the past 20 years on a monthly basis. Thus, doing it manually is not an option. Does anyone know a good workaround or way to get this type of data?

When checking out CodeCreator, I was not able to find an approach that resulted in time series objects.

Best regards and thank you in advance

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

For example in Bloomberg, CL1 refers to the current active WTI Crude contract. This points to CLX4 now, and rolls over to CLZ4 next month. CL2 points to the first deferred contract etc.

If so, what is the naming convention for generic contracts for Refinitiv? I am trying to get ElektronTimeseries data.

Thanks!

import refinitiv.data as rd
rd.open_session()
df = rd.get_data(
    ['4816.T'],
    [
        'TR.PriceClose', 
        'TR.PriceCloseDate'
    ],
    {
        'SDate': '2024-03-29',
        'EDate': '2024-02-25',
        'Adjusted': 0
    }
)

display(df)

Here is my C# code

Frame<int, string> frame = privateEikon.GetData(
   securities,
   fields,
   new Dictionary<string, string> { { "SDate", startDate }, { "EDate", endDate }, { "Frq", "D" } }
);

How can i do this? i need to get both the adjusted and unadjusted price.

If this is not possible then how can i get all stock splits on a given instrument?
I tried this and i only got one

import refinitiv.data as rd
rd.open_session()
df = rd.get_data(
    ['AAPL.O'],
    [
        'TR.AdjmtFactorAdjustmentDate',
        'TR.AdjmtFactorAdjustmentFactor',
        'TR.AdjmtFactorAdjustmentType',
        'TR.AdjmtFactorIsApplied',
        'TR.AdjmtFactorUnderlyingEventId'
    ]
)

display(df)

And it only return the adjustment on 2020-08-31 when i am sure there was another on 2014-06-09

import refinitiv.data as rd
rd.open_session()
df = rd.get_data(
    universe = ['NCSM.O','ZYNE.O'],
    fields = [
        'TR.PriceClose',
        'TR.Volume',
        'TR.PriceCloseDate'
    ],
    parameters={
        'SDate': '2024-09-11',
        'EDate': '2024-09-01'
    }
)

display(df)

And i get this result

result 

0 NCSM.O20.415902 2024-09-11 
1 NCSM.O20.884412988 2024-09-10
2 NCSM.O21.555194 2024-09-09
3 NCSM.O20.864047 2024-09-06
4 NCSM.O21.1711898 2024-09-05
5 NCSM.O20.964102 2024-09-04
6 NCSM.O20.7418982 2024-09-03
7 ZYNE.O1.302023-10-10
8 ZYNE.O1.302023-10-10
9 ZYNE.O1.302023-10-10
10 ZYNE.O1.302023-10-10
11 ZYNE.O1.302023-10-10
12 ZYNE.O1.302023-10-10
13 ZYNE.O1.302023-10-10

As you see for ZYNE i get the Last Priced Date,
Is there a way i can add to my series, the date that the response is for?
for example

7 ZYNE.O1.302023-10-10.2024-09-11
8 ZYNE.O1.302023-10-10.2024-09-10
9 ZYNE.O1.302023-10-10.2024-09-09
10 ZYNE.O1.302023-10-10.2024-09-06
11 ZYNE.O1.302023-10-10.2024-09-05
12 ZYNE.O1.302023-10-10.2024-09-04
13 ZYNE.O1.302023-10-10.2024-09-03

Here is the code i use to run in C#

                Frame<int, string> frame = privateEikon.GetData(
                    securities,
                    fields,
                    new Dictionary<string, string> { { "SDate", startDate }, { "EDate", endDate }, { "Frq", "D" } }
                );

import refinitiv.data as rd
import pandas as pd
from datetime import date
from dateutil.relativedelta import relativedelta
import os

type = ["GOVT","CORP"]
mths_back = (2024-2016)*12
enddate = date.today()
startdate = enddate - relativedelta(months=mths_back)
interval_on_lookback = '1W'
fields_TS = ['B_YLD_1','OAS_BID','AST_SWPSPD']

rd.open_session()
df_RIC = rd.discovery.search(
view = rd.discovery.Views.GOV_CORP_INSTRUMENTS,
top = 10000,
filter =  f"((DbType eq '{type[1]}') and (RCSBondGradeLeaf eq 'Investment Grade' or RCSBondGradeLeaf eq 'High Yield') and IsConvertible eq false and MaturityDate ge {startdate} and MaturityDate le {enddate} and (((SPsRatingsScope((RatingType eq 'S&P') and (CurrentRatingRank ge 15) and (CurrentRatingRank le 24))) or (RatingsScope((RatingType eq 'MDY') and (CurrentRatingRank ge 8) and (CurrentRatingRank le 17))) or (RatingsScope((RatingType eq 'FTC') and (CurrentRatingRank ge 9) and (CurrentRatingRank le 18)))) and (not (OfferingTypeDescription xeq 'Private placement-144A') and not (OfferingTypeDescription xeq 'Private placement-144A with reg rights') and not (OfferingTypeDescription xeq 'Private placement-144A no reg rights') and not (OfferingTypeDescription xeq 'Private placement-144A - Exchange Offer') and not (OfferingTypeDescription xeq 'Private place-144A Exch Offer with reg') and not (OfferingTypeDescription xeq 'Regulation S/Private placement-144A') and not (OfferingTypeDescription xeq 'Private placement-144A Exch Offer no reg') and not (OfferingTypeDescription xeq 'Private placement-144A - Section 3(c)(7)')) and RCSCurrency in ('C:6' 'C:3' 'C:4')))",
    select = "RIC",
order_by = "RIC"
    )
RICs=df_RIC['RIC'].to_list()


RICs = [x for x in RICs if isinstance(x, str)]
RICs = list(set(RICs))
RICs = sorted(RICs)

df1 =[]
for i in range(len(fields_TS)):
df0 = rd.get_history(
            RICs,
            start = startdate,
            end   = enddate,
            fields     = fields_TS[i],
            interval   = interval_on_lookback
    )
df0['interval'] = interval_on_lookback
df0['field'] = fields_TS[i]
df1 = df1.append(df0)
rd.close_session()
df1

I looked up at the Q&A and I tried to insert them with RHistory function: =RHistory("USD3MFSR";"BID.TIMESTAMP;BID.CLOSE";"NBROWS:1500 INTERVAL:1D";"CH:IN;Fd STREPEAT:N";B3)

Unfortunately it's not working. It says that CH it's not a valid refresh parameter. What can I do?

the permission scope is

"scope": "trapi.auth.cloud-credentials trapi.cfs.claimcheck.read trapi.cfs.subscriber.read trapi.data.est.sum trapi.data.fundamentals.bgs.std trapi.data.fundamentals.fs.std trapi.data.fundamentals.kpi.std trapi.data.fundamentals.ratios.std trapi.data.funds.assets.read trapi.data.historical-pricing.summaries.read trapi.data.pricing.premium-access trapi.data.pricing.read trapi.data.symbology.advanced.read trapi.data.symbology.read trapi.fundamentals.kpi trapi.fundamentals.universe trapi.graphql.subscriber.access trapi.metadata.read trapi.search.explore.read trapi.search.lookup.read trapi.search.metadata.read trapi.searchcore.lookup.read trapi.searchcore.metadata.read trapi.searchcore.read trapi.userdata.lists.read trapi.userdata.lists.write trapi.wealth.od.full.std trapi.wealth.org.info trapi.wealth.org.reference",

for APPLE

[
    {
        "universe": {
            "ric": "AAPL.O"
        },
        "status": {
            "code": "TS.Intraday.UserNotPermission.92000",
            "message": "User has no permission."
        }
    }
]

for TRI.N (Reuters)

[
    {
        "universe": {
            "ric": "TRI.N"
        },
        "interval": "P1W",
        "summaryTimestampLabel": "endPeriod",
        "adjustments": [
            "exchangeCorrection",
            "manualCorrection",
            "CCH",
            "CRE",
            "RTS",
            "RPO"
        ],
        "defaultPricingField": "TRDPRC_1",
        "headers": [
            {
                "name": "DATE",
                "type": "string"
            },
            {
                "name": "TRNOVR_UNS",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "NUM_MOVES",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "TRDPRC_1",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "HIGH_1",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "VWAP",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "ASK",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "BID",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "SALTIM",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "BLKVOLUM",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "OPEN_PRC",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "TRD_STATUS",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "LOW_1",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "BLKCOUNT",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "ACVOL_UNS",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "VWAP_OPEN",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "VWAP_HIGH",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "VWAP_LOW",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "OPEN_BID",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "BID_HIGH_1",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "BID_LOW_1",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "OPEN_ASK",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "ASK_HIGH_1",
                "type": "number",
                "decimalChar": "."
            },
            {
                "name": "ASK_LOW_1",
                "type": "number",
                "decimalChar": "."
            }
        ],
        "data": [
            [
                "2024-06-28",
                36965411,
                2925,
                168.15,
                168.18,
                167.1424,
                168.16,
                168.15,
                72600,
                46901,
                166,
                1,
                164.58,
                2,
                222033,
                165.7867,
                167.1424,
                165.7867,
                165.31,
                168.15,
                165.31,
                165.38,
                168.16,
                165.38
            ],
... others


            }
        }
    }
]

>>> res=rd.get_history(
...     ['GOOG.O', 'RPI.L'],
...     start = '2023-01-01',
...     end = datetime.today().strftime('%Y-%m-%d'),
...     # end = pd.to_datetime('today').strftime('%Y-%m-%d'),
...     interval='daily',
...     # fields = ['CLOSE', 'OPEN', 'HIGH', 'LOW']
...     fields = [
...         'TR.OPENPRICE',
...         'TR.HIGHPRICE', 
...         'TR.LOWPRICE' ,
...         'TR.CLOSEPRICE'
...     ]
... )
>>> res
               GOOG.O                       ...      RPI.L                      
           Open Price High Price Low Price  ... High Price Low Price Close Price
Date                                        ...                                 
2023-01-03      89.83      91.55     89.02  ...       <NA>      <NA>        <NA>
2023-01-04      91.01      91.24      87.8  ...       <NA>      <NA>        <NA>
2023-01-05      88.07      88.21     86.56  ...       <NA>      <NA>        <NA>
2023-01-06      87.36      88.47     85.57  ...       <NA>      <NA>        <NA>
2023-01-09     89.195      90.83     88.58  ...       <NA>      <NA>        <NA>
...               ...        ...       ...  ...        ...       ...         ...
2024-06-18     178.79     178.91    175.62  ...      465.0    422.05       425.0
2024-06-19       <NA>       <NA>      <NA>  ...      435.0     400.0       415.0
2024-06-20     176.71     178.74    176.46  ...      415.0     370.0       372.0
2024-06-21     178.49   182.5117    178.06  ...      389.5     365.0       373.5
2024-06-24     181.28     182.08    180.23  ...      399.0     362.0       390.0

[379 rows x 8 columns]

You can see that the 6 rows in the OPEN_PRC column are all integers.

The code to duplicate the issue...

// Create the platform session.
using ISession session = Sessions.GetSession();

// Open the session
session.Open();

var def = Summaries.Definition()
    .Universe("IBM.N")
    .Interval(Summaries.Interval.P3M)
    .TimestampLabel(Summaries.TimestampLabel.startPeriod)
    .Fields("OPEN_PRC", "HIGH_1", "LOW_1", "TRDPRC_1", "ACVOL_UNS", "NUM_MOVES")
    .Sessions(HistoricalPricing.Sessions.normal)
    .Count(1250)
    .Start("2023-01-02T00:00:00Z")
    .End("2024-05-28T00:00:00Z")
    .GetData();

Common.DisplayTable("Summary for ", dev);

Other instruments do not have the same problem with OPEN_PRC nor do different intervals. Here's a shot of the Visual Studio debugger showing the data type of the OPEN_PRC column...

Is there a way to not cache OMMStreamCache objects if it is a snapshot request, or could the amount of data held in memory be reduced? Here is our code when making a historical snapshot request...

        public IObservable<WorkspaceSnapShotStreamingResponse> GetTimeSeriesDataSnapShot(
            ISession workspaceSession,
            WorkspaceSnapShotStreamingRequest request,
            WorkspaceMetadataResponse metadataResponse)
        {
            return Observable.Create<WorkspaceSnapShotStreamingResponse>(observer =>
            {
                SerialDisposable activeSubscriptionToken = new SerialDisposable();
                if (workspaceSession.OpenState == Session.State.Opened)
                {
                    // Build the request
                    var summariesDefinition = BuildRequest(request);

                    // Make the request
                    activeSubscriptionToken.Disposable = summariesDefinition
                        .GetDataAsync(workspaceSession)
                        .ToObservable()
                        .Subscribe(OnSubscribeAction, observer.OnError);

                    // Deal with the response
                    void OnSubscribeAction(IDataSetResponse workspaceResponse)
                    {
                        if (workspaceResponse.Data != null)
                        {
                            if (workspaceResponse.Data.Table != null)
                            {
                                observer.OnNext(ConvertDataTableToWorkspacePriceRecords(
                                    workspaceResponse.Data.Table,
                                    request.Interval,
                                    request.View,
                                    TimeSeriesUpdateType.Update));
                                observer.OnCompleted();
                            }
                            else
                            {
                                Logger.Warn(workspaceResponse.Data.Errors);
                                observer.OnNext(new WorkspaceSnapShotStreamingResponse());
                            }
                        }
                    }
                }

                return activeSubscriptionToken;
            });
        }

        private ISummariesDefinition BuildRequest(WorkspaceSnapShotStreamingRequest request)
        {
            var summariesDefinition = Summaries
                .Definition(request.Symbol)
                .Interval(ResponseHelper.ConvertInterval(request.Interval, Logger))
                .Fields(request.View.MapTo.ToArray());

            if (request.Count > 0)
            {
                summariesDefinition.Count(request.Count);
            }
            else if (request.RequestRange.StartDateTime.HasValue)
            {
                summariesDefinition.Start((DateTime)request.RequestRange.StartDateTime);
            }
            else if (request.RequestRange.EndDateTime.HasValue)
            {
                summariesDefinition.End((DateTime)request.RequestRange.EndDateTime);
            }

            return summariesDefinition;
        }

I want to retrieve the stock price minutely in a time range:

df = ek.get_timeseries([".AXJO"], start_date = "2023-03-01", end_date = "2023-03-13", interval="minute")

but it shows error: No data available for the requested time range

When I remove the end_date, it shows the results, which I want to achieve but in different time range as below:

Please help to check how can I retrieve stock price minutely in a specific time range!

Thank you very much

I need to retrieve the Fitch and Moody's long term issuer credit ratings for all (or substantially all) US firms, as at the end of each month for the period from 2003-01-31 to 2023-12-31.

I'm completely new to APIs and Python, but from what I understand an API will be the best approach to retrieve such a large dataset. (The equivalent S&P data was much easier to obtain as a simple output from CapitalIQ.)

Below is the script I've tried so far: I'm using the S&P500, MidCap400 and SmallCap600 to cover as much of the market as possible, as I don't know if there is a way to retrieve the entire universe of all US firms and I don't have the permissions for the larger Russell indices.

Unfortunately, it also looks like the script is returning only the first rating in the time series, rather than the new rating each time there is a change.

Thank you---any suggestions would be appreciated!

import eikon as ek
import pandas as pd
import numpy as np

df, err = ek.get_data(instruments=['0#.SPX','0#.MID','0#.SPCY'],
fields=['TR.CommonName',
'TR.TickerSymbol',
'TR.IR.RatingTypeDescription',
'TR.FiIssuerFitchLongRating',
'TR.FiIssuerMoodysLongRating',
'TR.IR.RatingDate'],
parameters={'SDate':'-21Y', 'EDate':'0', 'FRQ':'M'}
)
df.columns = df.columns[:-2].tolist() + ['Moody\'s Rating','Fitch Rating']
df.head(50)

for my thesis I have to obtain times series return data for several mutual funds (at least monthly, but daily would be better) from 2000-today. However, the data is not available in Eikon (for example when I try it with the build formula tool in excel). Is it even possible to get return data for mutual funds in Eikon? Do I need access to Lipper for Investment Management? If yes, is it a separate product from Eikon (I guess then my university doesn't have it). Unfortunately there is not anyone from my uni that can help.

I would be happy if you share your ideas how I can get access to the data I need (time series data for mutual fund returns)

Thank you!

import refinitiv.data as 
from refinitiv.data.content import fundamental_and_reference
import datetime

rd.open_session()

growth_fields = ["TR.EPSMeanEstLastToNextYrGrowth",
                 "TR.EPSLTGEstValue(Period=FY1)",
                 "TR.LTGMean",
                 "TR.RevenueSmartEstFwdYrGrowth",
                 "TR.OrganicSalesGrowthMean(Period=FY1)",
                 "TR.GrowthCapexActValue(Period=FY0)",
                 "ROE",
                 "TR.ReturnonAvgCommEqtyPctIncomeAvailabletoCommExclExtraItems5YrAvg(Period=FY0)",
                 "TR.ROICActValue(Period=FY0)",
                 "TR.NetProfitMargin(Period=FY0)",
                 "TR.OperatingMarginPercent(Period=FY0)",
                 "TR.GrossMargin(Period=FY0)",
                 "TR.EBITDAMarginPercent(Period=FY0)",
                 "TR.LTDebtToTtlCapitalPct(Period=FY0)",
                 "TR.TtlDebtToTtlEquityPct(Period=FY0)",
                 "TR.EBITIntCov(Period=FY0)"]


lse = fundamental_and_reference.Definition(
    ["0#.INX"],
    fields = growth_fields
).get_data()

Ho do i specify a start and end date? it appears that there are lot of missing data in the cross section, so I am looking to download a time series, and I would like to have a login in place to have the latest available data if the most recent for a given date is not yet available. Ho do I do this?

import pandas as pd
import refinitiv.dataplatform as rdp

# Define the datetime range
start_date = '2023-08-18'
end_date = '2023-09-17'

all_data3 = pd.DataFrame()

# Get data
for RIC in rics_list:
    response = rdp.HistoricalPricing.get_summaries(
        universe=RIC,
        interval=rdp.Intervals.ONE_MINUTE,
        start=start_date,
        end=end_date,
        fields=['TRDPRC_1', 'ACVOL_UNS', 'HIGH_1', 'LOW_1', 'BID', 'ASK']
    )

    if response is not None and response.data.df is not None:
        df_bid_ask = response.data.df
        df_bid_ask.columns = pd.MultiIndex.from_product([[RIC], df_bid_ask.columns])

        df_bid_ask.index = pd.to_datetime(df_bid_ask.index)

        # Adjust the time filter for NYSE trading hours
        df_bid_ask = df_bid_ask.between_time('14:30', '21:00')

        df_bid_ask = df_bid_ask.loc[lambda x: x.index.dayofweek < 5]

        if all_data3.empty:
            all_data3 = df_bid_ask
        else:
            all_data3 = pd.concat([all_data3, df_bid_ask], axis=1)

# Display the concatenated data
print(all_data3.tail())

This code has been effective for gathering 1-minute interval data, but I've noticed significant gaps in the data due to the absence of tick data for periods without trades.

To address this, I'm looking to aggregate the data over longer intervals, specifically 20-minute and/or 30-minute periods. However, I am uncertain about the correct interval codes to use for these timeframes in the rdp.HistoricalPricing.get_summaries function.

Could anyone provide guidance on the appropriate interval codes for 20-minute and 30-minute data retrieval using the Refinitiv DataPlatform Python library? Any additional insights into handling intervals with sparse data would also be greatly appreciated.

Thank you in advance!