Hi @Chrisma Joy.Salut

To avoid limitations and you can consider using ek.get_data() and provide wider time window.

df,err = ek.get_data('JP3MT=RR',['TR.BIDYIELD.date','TR.BIDYIELD'],{"Sdate":'1970-05-01', "Edate":'2019-07-17'})
df.dropna(inplace=True)
df.head(1)

@Chrisma Joy.Salut

If I try

df = ek.get_timeseries(['JP6MT=RR','JP3MT=RR'],fields='CLOSE', start_date='1994-06-01', interval='daily') 
print('Return Row=',len(df.index)) 

it return

Return Row=1513

But and if I change it to request just one RIC

df = ek.get_timeseries(['JP6MT=RR'],fields='CLOSE', start_date='1994-06-01', interval='daily') 
print('Return Row=',len(df.index)) 

Return Row= 3000

I guess that it may relate with 3000 limit of points for inter-day data mentioned in the following post. And it looks like the problem still exists.

https://community.developers.refinitiv.com/questions/15616/eikon-scripting-api-issues-with-timeseries.html

https://community.developers.refinitiv.com/questions/6355/bug-in-get-timeseries.html

@Chrisma Joy.Salut

Refer to this discussion thread, for the interday timeseries interval, it is limited to 3K rows for a single request.

From the raw output, the first returned data point for JP3MT=RR is '2013-05-27' while the first returned data point for JP6MT=RR is '2013-06-05'. For this reason, when the data was displayed on the dataframe, you see the NaN value for JP6MT=RR between '2013-05-27' and '2013-06-04'.

To avoid this issue, you need to specify end_date. The range could be 3-4 years for 2 RICs.

bills=['JP3MT=RR','JP6MT=RR'] 
df = ek.get_timeseries(bills, fields='CLOSE', start_date='1994-06-01', end_date='1998-12-31',interval='daily')

Thank you @jirapongse.phuriphanvichai @moragodkrit Will relay this to the client.

Hi @Chrisma Joy.Salut

You can refer to this document for the limitation and how to handle it.

@jefferson.low

Yes, it is possible to write a loop to full all data points from 1994-06-01. However, it is unable to get the exact 3000 rows for each request. The code looks like:

import eikon as ek 
import pandas as pd 
import datetime 

bills=['JP3MT=RR','JP6MT=RR'] 
dfList = [] 
startDate = datetime.datetime(1994, 6, 1) 
while startDate < datetime.datetime.now(): 
   endDate = startDate + datetime.timedelta(days=1500) 
   df = ek.get_timeseries(bills, fields='CLOSE', start_date=startDate, end_date=endDate,interval='daily') 
   dfList.append(df) 
   startDate = endDate + datetime.timedelta(days=1) 

result = pd.concat(dfList) 
result