@mbert

There are other APIs that can provide historical data. For example:

• Thomson Reuters Tick History (TRTH)
• Datastream Web Service
• Elektron Data Platform APIs (Historical Pricing)

However, each product may have its own limitations.

You may need to contact Refinitiv Sales or Account team for the solution that meets your requirements.

To use the get_data method to retrieve close price, the code looks like:

rics = ['PD1CSCRKSPOT', 'PD5CSCRKSPOT', 'MEDCSCRKSPOT', 'NWECSTOPSPOT']

data3 = ek.get_data(rics,['TR.ClosePrice.Date','TR.ClosePrice'], {'Sdate':'1990-01-01', 'EDate':'2019-12-17'}) 

Then, the data frame could be reformatted with the following code:

rics = ['PD1CSCRKSPOT', 'PD5CSCRKSPOT', 'MEDCSCRKSPOT', 'NWECSTOPSPOT']

data3 = ek.get_data(rics,['TR.ClosePrice.Date','TR.ClosePrice'], {'Sdate':'1990-01-01', 'EDate':'2019-12-17'}) 
 
dfs = dict(tuple(data3[0].groupby('Instrument'))) 
dfarray = [] 
for ric, data in dfs.items(): 
   df_tmp = dfs[ric].dropna() 
   df_tmp = df_tmp.drop_duplicates() 
   df_tmp = df_tmp.set_index('Date') 
   df_tmp = df_tmp.drop(['Instrument'], axis=1) 
   df_tmp = df_tmp.rename(columns={"Close Price":ric}) 
   dfarray.append(df_tmp) 
 
result = pd.concat(dfarray, axis=1, sort=True) 
result.columns.name = 'CLOSE' 
result

The output is:

@mbert

From my test, it divides the maximum list (3000) by the number of items to calculate the data points returned for each item. For example, if there are four items in the list, the number of data points for each item will not exceed 750.

For the list ['PD1CSCRKSPOT', 'PD5CSCRKSPOT', 'MEDCSCRKSPOT', 'NWECSTOPSPOT'], I set the raw_output to True to get the raw output and count the number of data points for each item.

The number of data points for each item is 750 although the last item doesn't have data.

With this method, you will approximately know the number of data points returned for each item when requesting the time-series data.

Thanks @jirapongse.phuriphanvichai. Would you happen to know if there is a more reliable way to obtain this data? Either through the Eikon Python API or another Reuters service? The fact that this service silently drops data depending on query size is very error prone and leads to downstream issues in the reliability of analysis.

Is there a way to get all series using the get_data() query? While this still has limts, 10K data points is let restrictive. As indicated here https://community.developers.refinitiv.com/questions/17137/my-get-timeseries-call-is-producing-an-error-which.html it appears possible in certain cases, but is this broadly applicable?

Thanks @jirapongse.phuriphanvichai. Following up on this, I am a bit confused why get_data does not limit the amount of data returned? Looking at a query like

rics = ['PD1CSCRKSPOT', 'PD5CSCRKSPOT', 'MEDCSCRKSPOT']
eikon.get_data(rics, ['TR.ClosePrice.Date', 'TR.ClosePrice'], {'Sdate': '1990-01-01', 'EDate': '2019-12-17'})
 
(         Instrument                  Date  Close Price
 0      PD1CSCRKSPOT  1990-01-01T00:00:00Z          NaN
 1      PD1CSCRKSPOT  1990-01-02T00:00:00Z          NaN
 2      PD1CSCRKSPOT  1990-01-03T00:00:00Z          NaN
 3      PD1CSCRKSPOT  1990-01-04T00:00:00Z          NaN
 4      PD1CSCRKSPOT  1990-01-05T00:00:00Z          NaN
 5      PD1CSCRKSPOT  1990-01-08T00:00:00Z          NaN
 6      PD1CSCRKSPOT  1990-01-09T00:00:00Z          NaN
 7      PD1CSCRKSPOT  1990-01-10T00:00:00Z          NaN
 8      PD1CSCRKSPOT  1990-01-11T00:00:00Z          NaN
 9      PD1CSCRKSPOT  1990-01-12T00:00:00Z          NaN
 10     PD1CSCRKSPOT  1990-01-15T00:00:00Z          NaN
 11     PD1CSCRKSPOT  1990-01-16T00:00:00Z          NaN
 ...             ...                   ...          ...
 23175  MEDCSCRKSPOT  2019-12-02T00:00:00Z         3.65
 23176  MEDCSCRKSPOT  2019-12-03T00:00:00Z         3.05
 23177  MEDCSCRKSPOT  2019-12-04T00:00:00Z         3.21
 23178  MEDCSCRKSPOT  2019-12-05T00:00:00Z         3.22
 23179  MEDCSCRKSPOT  2019-12-06T00:00:00Z         3.21
 23180  MEDCSCRKSPOT  2019-12-09T00:00:00Z         3.04
 23181  MEDCSCRKSPOT  2019-12-10T00:00:00Z         2.88
 23182  MEDCSCRKSPOT  2019-12-11T00:00:00Z         3.16
 23183  MEDCSCRKSPOT  2019-12-12T00:00:00Z         2.51
 23184  MEDCSCRKSPOT  2019-12-13T00:00:00Z         2.97
 23185  MEDCSCRKSPOT  2019-12-16T00:00:00Z         4.78
 23186  MEDCSCRKSPOT  2019-12-17T00:00:00Z          NaN
 
 [23187 rows x 3 columns], None)

this does not appear to be limiting the response to < 10,000 points as per the documentation from https://developers.refinitiv.com/eikon-apis/eikon-data-api/docs?content=49692&type=documentation_item, i.e.

Datapoints returned per request - A datapoint is a 'cell', or a unique field value for a unique instrument on a unique time stamp. Datapoint limits vary by the content set being retrieved (for example, timeseries limits are different from news headline limits), but all are throttled on a per request basis and are not aggregated across all applications. Here are Datapoint limit examples per Eikon Data API function type

get_data: The current limit value (10-Oct-2019) is around 10,000 data points.

In addition, I'm wondering what the best way to get associated mappings from get_timeseries fields to get_data fields? e.g. in the above case knowing that TR.ClosePrice corresponds to CLOSE.

Lastly, I'm wondering if there is some better documentation somewhere on the DataGrid_StandardAsync and TimeSeries services that get_data and get_timeseries respectively rely on. For example, in your above get_data query at first glance it looks like data was returned for the entire range according to a weekday calendar

{"responses":[{"columnHeadersCount":1,"data":[["PD1CSCRKSPOT","1990-12-10T00:00:00Z",""],["PD1CSCRKSPOT","1990-12-11T00:00:00Z",""],["PD1CSCRKSPOT","1990-12-12T00:00:00Z",""],["PD1CSCRKSPOT","1990-12-13T00:00:00Z",""],["PD1CSCRKSPOT","1990-12-14T00:00:00Z",""],["PD1CSCRKSPOT","1990-12-17T00:00:00Z",""],...

but I can't see any documentation confirming this?