EOD price history limit in Eikon Python API

hi there,

I am trying to retrieve end of day OHLCV for some ETF instrument (XLV) from 01/01/2000 till today via the EIKON Python API. Unfortunately, i can only retrieve data up to 19/06/2018, which is not even 2 years?

I would like to understand why the history is limit to such a short period whereas the Excel API and the Eikon chart allow me to retrieve data up to the desired start date.

Here is the call below where ric='XLV', start_date='20000101', end_date='20200427'. Thank you

fields=["OPEN", "HIGH", "LOW", "CLOSE", "VOLUME"]
df = ek.get_timeseries(ric,
                       fields=fields,
                       calendar="tradingdays",
                       start_date=start_date,
                       end_date=end_date,
                       interval='daily')

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Accepted answers

Jirapongse

@Mehdi_deprecated_0

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.

All comments

chavalit-jintamalit

Hi @Mehdi_deprecated_0

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.

Jirapongse

@Mehdi_deprecated_0

jason.ramchandani01

Hi @Mehdi_deprecated_0 - 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.

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