Intraday asset prices best practices

@diarmuid.omahony You would use the get_timeseries API call to get intraday history. In short API limits are applied to all our APIs and you can find out details here - for get_timeseries:

• get_timeseries: The current limit value (10-Oct-2019) is 3,000 data points (rows) for interday intervals and 50,000 data points for intraday intervals. This limit applies to the whole request, whatever the number of requested instrument

So you have upto 50k datapoints per API call which you can configure in any way you need them - so say you want equity data that trades for say 8 hrs a day that would be 8hrs x 60min =480 mins per ric per day say (you need to check these Im just giving the logic here). So you can chunk a longer list of RICs into appropriate chunks to get a full dataframe back and then concatenate them - here please look at the sample code below which does this for the S&P 500 index:

First define our chunks routine:

def chunks(l, n):     
  for i in range(0,len(l),n):         
    yield l[i:i+n]

next we get a long list of RICs in this case for 0#.SPX (S&P500) - we then iterate over chunks and concatenate resultant dataframes:

data1 = pd.DataFrame() 
univ,err = ek.get_data('0#.SPX','TR.CommonName') 
long_ric_list = univ['Instrument'].astype(str).values.tolist() 
ric_chunk = list(chunks(long_ric_list, 100)) 

for r in ric_chunk:     
  df = ek.get_timeseries(r,['CLOSE'],start_date='2021-03-26',interval='minute',count=500)     
  if len(data1):         
    data1 = data1 = data1.join(df)    
  else:     
    data1 = df              

data1

Obviously - you can change the amount in each chunk to suit your needs - or say if you wanted a long history for 1 RIC you could just iterate for each RIC in universe and get 50k rows per instrument back. I hope this can help.

Hi @diarmuid.omahony

It works with AMZN.OQ

Can you try again?