Discrepancies/Errors between Python API and Refinitiv Eikon data

bshapiro

Hello,

I have been recently retrieving brokerage recommendations from Reuters using the Python Eikon API.

I am interested in the price target and the grade recommendation assigned by a brokerage to a stock.

Because I need this information to be accurate, I have been handchecking data returned and have found many

errors and discrepancies between what the Python API returns and what is shown by Eikon in the Reuters terminal.

For example, I use the following code to pull recommendations for ticker IRT:

import json

symbol_list = ['IRT']
fields = [
              'TR.TPEstvalue.brokername',
              'TR.TPEstValue', 'TR.TPEstValue.date',
              'TR.BrkRecLabel', 'TR.TPEstvalue.analystname',
              'TR.RecLabelEst',
              'TR.RecEstValue', 'TR.BrkRecEstConfirmDate'
         ]
data = ek.get_data(instruments=symbol_list, fields=fields, raw_output=True)
print(json.dumps(data, indent=2, sort_keys=True))

The results for brokerage RBC are the following:

Python API says RBC is at a Neutral rating for ticker IRT.

However, the following is returned for when looking at recommendations on the Reuters Terminal:

As you can notice, the true grade is an Outperform/Buy rating, not Neutral.

I have posted before on this issue (see "Possible BrkRecLabel and RecEstValue Data Errors"), and this is not an issue of fields retrieved.

I have also been in contact with the Eikon Help Desk, who confirmed data is correct on their end (see case 11302953).

This is a Python API data issue that needs to be corrected. Let me know if more examples would be helpful. These discrepancies appear

in many brokerages and stocks so I have many available.

nick.zincone

Hi @bshapiro

I've reached out to content specialist who provided an explanation as to what is happening. Specifically, it appears to be a common issue if you mix specific data sets - target price and broker recommendations - in one request. Each data set is sorted differently in the backend.

A proposed solution is to perform 2 separate queries for the different datasets and combine them.

For example:

import eikon as ek
import pandas as pd
from functools import reduce
...

symbol_list = ['IRT']
fields = ['TR.BrkRecLabel.brokername', 'TR.BrkRecLabel', 
          'TR.BrkRecLabel.date'
         ]
data1, err = ek.get_data(instruments=symbol_list, fields=fields)
data1

symbol_list = ['IRT']
fields = ['TR.TPEstValue.brokername', 'TR.TPEstValue.analystname', 
          'TR.TPEstValue', 'TR.TPEstValue.date'
         ]
data2, err = ek.get_data(instruments=symbol_list, fields=fields)
data2

Combine them:

result = reduce(lambda x,y: pd.merge(x,y, on='Broker Name', how='outer'), [data1, data2])
result