@pyquant Here is the generic routine:

from dateutil import parser
from datetime import timedelta
from datetime import datetime
import math
import time

def date_range(start, end, intv):
    start = datetime.strptime(start,"%Y-%m-%d")
    end = datetime.strptime(end,"%Y-%m-%d")
    diff = (end  - start ) / intv
    for i in range(intv):
        yield (start + diff * i).strftime("%Y-%m-%d")
    yield end.strftime("%Y-%m-%d")

def get_daily(rics,fields,start,end):
    for ric in rics:        
        interval = math.ceil((parser.parse(end) - parser.parse(start)).days / 3000)
        l = list(date_range(start,end,interval))
        df1 = pd.DataFrame()
        df = pd.DataFrame()
        for i in range(interval):
            ts = ek.get_timeseries(rics=ric,fields=fields, start_date=l[0+i],end_date=l[1+i], interval='daily')
            df = df.append(ts)
            time.sleep(0.4)

    return df

rics = ['.GDAXI'] # Just for one ric at the moment I will extend this for multi-ric
fields = ['OPEN', 'HIGH', 'LOW', 'CLOSE']
start = '1990-06-04'
end = '2018-06-04'
df = get_daily(rics,fields,start,end)
df

I hope this can help.

Hi @pyquant Thanks for your question - the reason is because the timeseries API is limited to 3000 rows per interday request. You can see the limits for each in this document.

You can of course iterate these calls as you are already doing for each RIC. But you can also achieve this with 2 calls per RIC.

df1 = ek.get_timeseries(i, ['CLOSE'],start_date="1999-01-02", end_date="2008-10-22")
df2 = ek.get_timeseries(i, ['CLOSE'],start_date="2008-10-23")
df = pd.concat([df1, df2], axis=0)

I hope this can help - I have a generic routine for the date calcs somewhere I will add it when i find it.

Thanks. I will give it a try.