@Mehdi

I have run the same code and it returns 3000 data points from 2008-05-27 to 2020-04-24. 3,000 data points are the current limit for the get_timeseries interday intervals, as mentioned in the EIKON DATA API USAGE AND LIMITS GUIDELINE.

Hi @Mehdi

Please review API limitation at https://developers.refinitiv.com/eikon-apis/eikon-data-api/docs?content=49692&type=documentation_item

Basically you reached the maximum number of records in a single API call. (3000 rows)

You should split the API call into multiple smaller periods.

Hi @Mehdi - you can use the following code to return you a timeseries for as long dates as possible. It currently only works for a single RIC per time but you can loop across a list of RICS and use this and then concatenate to form larger frames. First we need to import some packages:

import eikon as ek
import pandas as pd
import numpy as np
from dateutil import parser
from datetime import timedelta
from datetime import datetime
import math
import time
ek.set_app_key('YOUR API KEY HERE')

Next we need a date_range helper function:

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")

Finally we create our new get_daily function (you can of course use other intervals etc). Here we are using a looped get_timeseries call which increments in chunks of 3000 rows - which as you have seen from the answers above is the current per call limit. I have also added a small sleep in there to avoid throttling limits.

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

then you can just call the get_daily function with a larger date range and hey presto it all works beautifully:

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 you can find this of use.