Eikon API is very unstable,returning NoneTypes for readily accessible data that iscurrently availab

Radeesh

The Eikon API is very unstable. It is returning NoneTypes for readily accessible data that is currently available in the Data Item Browser. Subsequent calls for the data are producing a result but the NoneType is random and occurs elsewhere in the list of requested instruments requiring multiple call backs to get all requested data. This is usually a result of the API having micro time outs and is well documented in my history. Please report this instability to the API team

this is happening on calls with a max of 5 rics.  Its not a limit issue,  the API is just micro timing out.  This instability happens occasionally and needs to be ameliorated on your side.  I’m getting the data on multiple requests which means its clearly not a limit otherwise I would be prevented from getting any data.

Notice all the NoneTypes in the following call.  They are random and for readily available instruments.  If I re run this call I will get the data that says None currently and find new NoneTypes for instruments that I just received data for.  Its API instability.  The API is essentially quickly timing out on my call and that is why its happening.  This issue is new as of 24 hours but historically seems to happen every few months and causes a lot of headaches and unnecessary re calls for data

 

 

tickers = ['Cc1', 'Cc2','Sc1','Sc2', 'SMc1','SMc2', 'Wc1', 'Wc2', 'KWc2', 'KWc1','BOc2', 'BOc1','RRc1','RRc2']

tickFullU={}

 

for ticker in tickers:

    try:

        tickFullU[ticker]= ek.get_timeseries(ticker, fields='*', start_date=str(pd.Timestamp.today()-pd.offsets.BusinessDay(n=10))[:10], end_date=str(pd.Timestamp.today()-pd.offsets.BusinessDay(n=1))[:10], interval='daily')

    except:

        try:

            tickFullU[ticker]= ek.get_timeseries(ticker, fields='*', start_date=str(pd.Timestamp.today()-pd.offsets.BusinessDay(n=10))[:10], end_date=str(pd.Timestamp.today()-pd.offsets.BusinessDay(n=1))[:10], interval='daily')

        except:

            try:

                tickFullU[ticker]= ek.get_timeseries(ticker, fields='*', start_date=str(pd.Timestamp.today()-pd.offsets.BusinessDay(n=10))[:10], end_date=str(pd.Timestamp.today()-pd.offsets.BusinessDay(n=1))[:10], interval='daily')

            except:

                tickFullU[ticker]= ek.get_timeseries(ticker, fields='*', start_date=str(pd.Timestamp.today()-pd.offsets.BusinessDay(n=10))[:10], end_date=str(pd.Timestamp.today()-pd.offsets.BusinessDay(n=1))[:10], interval='daily')

 

tickFullU

Out[170]:

{'Cc1': Cc1           HIGH     LOW    OPEN   CLOSE  VOLUME

Date                                             

 2026-03-23  473.75  456.50  465.25  459.50  253109

2026-03-24  465.75  459.00  460.00  462.50  184112

2026-03-25  468.50  458.00  461.25  467.25  155914

2026-03-26  468.75  462.00  466.75  467.00  164119

2026-03-27  470.50  460.75  466.00  462.00  179461

2026-03-30  465.75  455.00  462.75  455.75  165878

2026-03-31  462.00  451.25  455.50  457.75  288867

2026-04-01  460.00  448.50  457.50  454.25  203129

2026-04-02  460.50  451.25  454.00  452.25  165400

2026-04-03  460.50  451.25  454.00     NaN    <NA>,

'Cc2': None,

'Sc1': None,

'Sc2': None,

'SMc1': SMc1         HIGH    LOW   OPEN  CLOSE  VOLUME

Date                                         

 2026-03-23  332.4  324.9  327.6  326.6   70812

2026-03-24  327.5  321.4  326.7  322.4   66679

2026-03-25  324.4  318.1  322.4  319.8   76591

2026-03-26  323.2  316.4  319.0  322.1   71340

2026-03-27  323.5  314.7  321.7  315.3   65082

2026-03-30  316.6  312.3  315.3  314.9   69797

2026-03-31  318.0  313.5  315.1  316.4   75224

2026-04-01  319.0  312.5  317.0  318.2   80093

2026-04-02  318.8  313.9  318.2  315.2   66337

2026-04-03  318.8  313.9  318.2    NaN    <NA>,

'SMc2': SMc2         HIGH    LOW   OPEN  CLOSE  VOLUME

Date                                         

 2026-03-23  328.0  321.3  323.9  323.2   52316

2026-03-24  324.1  319.1  324.0  319.9   49059

2026-03-25  321.6  316.4  320.2  318.0   55608

2026-03-26  319.9  315.1  318.0  319.2   45103

2026-03-27  320.5  312.5  318.7  313.1   46829

2026-03-30  314.5  311.0  313.2  313.0   44112

2026-03-31  316.6  312.0  313.0  314.3   56179

2026-04-01  317.1  311.8  315.0  316.7   62442

2026-04-02  317.2  312.8  317.0  313.2   42189

2026-04-03  317.2  312.8  317.0    NaN    <NA>,

'Wc1': Wc1           HIGH     LOW    OPEN   CLOSE  VOLUME

Date                                             

 2026-03-23  606.50  577.75  596.50  587.75   97322

2026-03-24  596.00  582.50  591.00  590.00   70071

2026-03-25  599.50  579.25  589.00  597.75   65526

2026-03-26  607.75  593.00  599.50  605.00   55171

2026-03-27  613.25  599.25  605.00  605.00   52792

2026-03-30  612.00  597.75  607.75  607.00   60137

2026-03-31  625.00  605.25  609.75  616.25  101061

2026-04-01  622.75  593.25  618.00  597.50  106118

2026-04-02  609.25  594.00  597.50  598.25   89287

2026-04-03  609.25  594.00  597.50     NaN    <NA>,

'Wc2': Wc2           HIGH     LOW    OPEN   CLOSE  VOLUME

Date                                             

 2026-03-23  618.50  590.50  607.50  600.00   48424

2026-03-24  607.75  594.75  602.75  602.00   41029

2026-03-25  610.50  590.50  601.00  608.75   37217

2026-03-26  618.25  604.50  609.00  615.75   30696

2026-03-27  624.00  610.50  616.00  616.00   30289

2026-03-30  623.00  609.25  619.00  618.75   45402

2026-03-31  636.00  616.75  621.00  626.50   65654

2026-04-01  633.00  605.25  628.00  608.75   62872

2026-04-02  620.25  605.75  609.50  609.50   64316

2026-04-03  620.25  605.75  609.50     NaN    <NA>,

'KWc2': KWc2          HIGH     LOW    OPEN   CLOSE  VOLUME

Date                                             

 2026-03-23  632.75  606.75  622.50  618.25   24950

2026-03-24  628.00  615.00  622.00  619.75   20301

2026-03-25  634.00  609.25  619.00  633.00   23401

2026-03-26  645.50  631.00  633.75  641.25   25245

2026-03-27  654.50  640.50  641.75  647.50   27792

2026-03-30  654.25  635.75  649.50  640.75   24666

2026-03-31  662.25  638.75  644.25  648.75   35974

2026-04-01  656.00  624.75  649.25  628.25   32559

2026-04-02  640.25  623.25  628.25  631.00   38400

2026-04-03  640.25  623.25  628.25     NaN    <NA>,

'KWc1': KWc1          HIGH     LOW    OPEN   CLOSE  VOLUME

Date                                             

 2026-03-23  617.75  591.25  607.25  603.25   38825

2026-03-24  613.00  599.25  604.50  604.00   33383

2026-03-25  619.50  593.50  604.00  617.75   28101

2026-03-26  631.25  616.25  618.50  626.75   30837

2026-03-27  640.25  626.00  626.75  632.75   32259

2026-03-30  639.75  621.75  634.00  626.25   30971

2026-03-31  648.50  624.75  630.00  635.50   46452

2026-04-01  643.00  610.00  636.00  613.75   51998

2026-04-02  625.50  608.75  613.75  615.75   47973

2026-04-03  625.50  608.75  613.75     NaN    <NA>,

'BOc2': BOc2         HIGH    LOW   OPEN  CLOSE  VOLUME

Date                                         

 2026-03-23  66.15  64.08  65.30  65.36   77859

2026-03-24  66.17  64.71  65.80  65.67   66964

2026-03-25  67.14  64.86  65.28  66.92   61092

2026-03-26  68.26  66.69  66.70  67.88   55601

2026-03-27  69.00  66.23  67.88  67.45  117530

2026-03-30  69.64  67.59  67.85  68.48   61853

2026-03-31  69.29  68.05  68.50  68.88   70940

2026-04-01  69.08  67.08  68.98  67.19   73255

2026-04-02  69.03  66.87  67.09  68.74   74147

2026-04-03  69.03  66.87  67.09    NaN    <NA>,

'BOc1': BOc1         HIGH    LOW   OPEN  CLOSE  VOLUME

Date                                         

 2026-03-23  66.38  64.22  65.53  65.58  103427

2026-03-24  66.24  64.86  65.90  65.73   88200

2026-03-25  67.32  64.90  65.44  67.10   89097

2026-03-26  68.34  66.87  67.10  68.02   74748

2026-03-27  69.10  66.30  67.85  67.41  132667

2026-03-30  69.68  67.55  67.81  68.47   86324

2026-03-31  69.21  68.00  68.51  68.88   93280

2026-04-01  69.10  66.97  68.95  67.11  101337

2026-04-02  69.17  66.74  67.01  68.94  102573

2026-04-03  69.17  66.74  67.01    NaN    <NA>,

'RRc1': None,

'RRc2': RRc2          HIGH     LOW    OPEN   CLOSE  VOLUME

Date                                             

 2026-03-23  11.525  11.285  11.525  11.285     202

2026-03-24  11.490  11.250  11.290  11.340     142

2026-03-25  11.435  11.290  11.295  11.325     143

2026-03-26  11.395  11.305  11.395  11.325     109

2026-03-27  11.575  11.345  11.345  11.440     108

2026-03-30  11.755  11.575  11.610  11.685     346

2026-03-31  11.775  11.660  11.685  11.760     477

2026-04-01  11.755  11.625  11.755  11.665     348

2026-04-02  11.650  11.570  11.630  11.580     252

2026-04-03  11.650  11.570  11.630     NaN    <NA>}

Jirapongse

@Radeesh

Thank you for reaching out to us.

I couldn't replicate this issue with the provided code. If the problem happens randomly, it could be the problem in the server side or endpoint, not in the library. You may need to contact the Workspace support team directly to verify this issue.

The Eikon Data API (ek.get_timeseries) is quite and it connects to the old endpoint to retrieve historical data. Please use the LSEG Data Library for Python instead which retrieves historical data from the Historical Pricing API. The code will look like this:

import lseg.data as ld
import pandas as pd

ld.open_session()

tickers = ['Cc1', 'Cc2','Sc1','Sc2', 'SMc1','SMc2', 'Wc1', 'Wc2', 'KWc2', 'KWc1','BOc2', 'BOc1','RRc1','RRc2']
tickFullU={}
for ticker in tickers:
    try:
        tickFullU[ticker]= ld.get_history(
            universe = ['Cc1'], 
            fields = ['OPEN_PRC','HIGH_1','LOW_1','TRDPRC_1','ACVOL_UNS'], 
            start =str(pd.Timestamp.today()-pd.offsets.BusinessDay(n=10))[:10], 
            end=str(pd.Timestamp.today()-pd.offsets.BusinessDay(n=1))[:10], 
            interval = 'daily')
        tickFullU[ticker].rename(columns={'OPEN_PRC': 'OPEN', 'HIGH_1': 'HIGH','LOW_1':'LOW','TRDPRC_1':'CLOSE','ACVOL_UNS':'VOLUME'}, inplace=True)
    except Exception as e:
        print(f"An error occured {e}");
        

tickFullU

The examples are also available on GitHub. The client can also refer to this Essential Guide to the Data Libraries - Generations of Python library (EDAPI, RDP, RD, LD) article