Skipping values lseg data platform

I ran 5 times the synchronous code where I sent requests of 1000 RIC every 2 seconds to explicitly avoid the request time limitation.
The volume of data received is 3 Mb.
I received about 7000 data points per request, although in my version there is no limit on the number of data points.

(for example, if the request asked for 5000 instruments, but only 3000 were returned)

All 60,000 RICs I sent a request for returned with data, but the problem remains that the number of missing values ​​is still different.

The code provides for handling timeouts and server errors.
There are also cases when a completely empty column is returned in the response, for this situation there is a solution to handle it.
But when there is no data in a specific cell, it is very difficult to catch.
Even if you use additional loading for certain RICs, it may not return data for a specific RIC. In this case, how can I identify the lack of data in a cell? Is this due to the fact that a specific value is not in refenitiv or for some reason it did not reach me?
Can you suggest methods for identifying missing values ​​in a cell or "data point"?

Code:

FIELDS = ['TR.TotRevenue3YrCAGR', 'TR.F.IncAftTaxMargPct3YrIntmCAGR',

          'TR.EBITDA3YrInterimCAGR', 'TR.F.EBITDA5YrIntmCAGR',

          'TR.F.NetCFOp3YrIntmCAGR', 'TR.F.EPSDilExclExOrdComTot3YrIntmCAGR']

response = fundamental_and_reference.Definition(

                    universe=rics_batch,

                    fields=FIELDS,

                    parameters=PARAMS

                ).get_data()


                if response.is_success:

                    ld_df = response.data.df

Detect count of missing values:

missing_values_df = pd.DataFrame(data=df.replace("", np.nan).isna().mean() * 100).T.round(3)

Of course it does, because the code works in main thread (synchronously), which will wait for a response from the server. And it will not move to the next batch until it receives a response.
The response from the server takes about 1-5 seconds. Depending on the number of fields in the request and the server load.
For all the time that I have been using this library, I have not had a timeout of 300 seconds.

import pandas as pd

from time import sleep


import lseg.data as ld

from lseg.data.content import fundamental_and_reference

from lseg.data.errors import LDError




def fetch_data(rics, fields, params):

    ld.open_session()

    df = pd.DataFrame()

    step = 1000


    for rics_list in range(0, len(rics), step):

        attempt = 0

        gotData = False

        batch = rics[rics_list: rics_list + step]

        while not gotData and (attempt < 3):

            try:

                response = fundamental_and_reference.Definition(

                    universe=batch,

                    fields=fields,

                    parameters=params

                ).get_data()

                ld_df = response.data.df

                df = pd.concat([df, ld_df], ignore_index=True)

                print(f'got - {rics_list + step}')

                gotData = True

                # sleep(2)

            except LDError as lde:

                print(lde)

            except Exception as e:

                print(e)

            sleep(5 * attempt + 10)

            attempt += 1

    ld.close_session()

    return df




def main():

    with open('rics.txt') as file:

        rics = [line.strip() for line in file]


    FIELDS = ['TR.TotRevenue3YrCAGR', 'TR.F.IncAftTaxMargPct3YrIntmCAGR',

              'TR.EBITDA3YrInterimCAGR', 'TR.F.EBITDA5YrIntmCAGR',

              'TR.F.NetCFOp3YrIntmCAGR', 'TR.F.EPSDilExclExOrdComTot3YrIntmCAGR']

    PARAMS = {'Curn': 'USD', 'CH': 'Fd'}

    result = fetch_data(rics, FIELDS, PARAMS)

    result.to_csv('result.csv', header=True)




if __name__ == '__main__':

    main()