We would like to start this tutorial where Eikon Scrpting Proxy Quickstart has left off. This brief tutorial will discuss the steps required to retrieve the data that is presented in the Estmates section of "Company Tearsheet" Eikon Excel example.
Please refer to Quick Start for the details
- TR Eikon Scripting Proxy is installed
- Python is installed
- Eikon Python API is installed
This is how the Estimates look in Eikon Excel Company Tearsheet template example
We learn how to retrieve the same data content via Eikon scripting from python
- Using Eikon Excel for Lookup
- Using DIB as Reference
- Using Python Excel to Access Data
- From All Programs menu, we expand Thomson Reuters menu item and select Thomson Reuters Eikon - Microsoft Excel.
- Once Excel is open, from Thomson Reuters menu item we choose "Sign In" option, and enter your Thomson Reuters Eikon credentials, hitting "Sign In" button. The status should change to "Online"
- Next, from Thomson Reuters menu item, we choose Templates
And select from the Template Library first "Fundamentals" on the left, then "Company Tearsheet" on the right, then click "Open":
- We are looking at the template, or example "Company Tearsheet". In this tutorial we will aim to replicate the data retrieval in Estimates parts of this example by using eikon scripting from python. Let's start by clicking on the bottom tab "Company Tearsheet", while zeroing-in on the section "Estimates"
- Now we are ready for the most important of the steps in lookup, the actual lookup. As we click on one of the cells that contain the estimate functions, for example E24, in the Excel formula window we see the function call that is required to pull this data:
For example, E24 conpains TR.RevenueHigh with parameters period being "FY1", scale equaling 6, and currency #1, referring to "USD"
The repeated lookup will allow us to learn how Company Tearsheet example is built, down to every cell, every function call complete with the required information. These details we are going to use to call the same functions from python eikon.
Eikon Data Item Browser is a tool that we use to reference eikon scripting. Once we have started Eikon Scripting Proxy and signed into Thomson Reuters Eikon, we have an option to start DIB:
We use DIB to search for the right data item, for example, for Instrument we enter "IBM", and Data Item Name we look up is TR.RevenueHigh,we are able to:
* Lookup the complete list of parameters with possible values (on the right side panel)
* Review the specific value we expect for IBM, on the main panel
And now we proceed to the most interesting part. We know the data items we would like to use by looking it up in Eikon Excel examples, and we can parametrize the data items per requirement, by looking up the data items in Data Item Browser.
- We start Python interpreter. We will work on the python script in separate text editor, pasting and running the script into python interpreter when we are ready to test.
- Let us flex our muscle by running the following little script:
df, err = ek.get_data("IBM",
[
('TR.RevenueActValue', {'Period': 'FY0','Scale': 6, 'Curn': 'USD'}),
('TR.RevenueMeanEstimate', {'Period': 'FY1','Scale': 6, 'Curn': 'USD'}),
('TR.RevenueMeanEstimate', {'Period': 'FY2','Scale': 6, 'Curn': 'USD'})
])
df
We should see the output:
Instrument Revenue - Actual Revenue - Mean Estimate
0 IBM 79919 78717.97129
Revenue - Mean Estimate 0 78663.43974
- Next we try to retrieve data for the three different periods, as required for Esimates data region, while laying the results out into rows:
df1, err = ek.get_data("IBM",
[
('TR.RevenueActValue', {'Period': 'FY0','Scale': 6, 'Curn': 'USD'}),
('TR.RevenueMeanEstimate', {'Period': 'FY1','Scale': 6, 'Curn': 'USD'}),
('TR.RevenueMeanEstimate', {'Period': 'FY2','Scale': 6, 'Curn': 'USD'})
],sort_on=None, output='pandas', debug=True)
df2, err = ek.get_data("IBM",
[
('TR.RevenueHigh', {'Period': 'FY1','Scale': 6, 'Curn': 'USD'}),
('TR.RevenueHigh', {'Period': 'FY2','Scale': 6, 'Curn': 'USD'}),
('TR.RevenueLow', {'Period': 'FY1','Scale': 6, 'Curn': 'USD'}),
('TR.RevenueLow', {'Period': 'FY2','Scale': 6, 'Curn': 'USD'})
],sort_on=None, output='pandas', debug=True)
df3, err = ek.get_data("IBM",
[
('TR.RevenueLow', {'Period': 'FY1','Scale': 6, 'Curn': 'USD'}),
('TR.RevenueLow', {'Period': 'FY2','Scale': 6, 'Curn': 'USD'})
],sort_on=None, output='pandas', debug=True)
df4, err = ek.get_data("IBM",
[
('TR.RevenueMeanEstimate', {'Period': 'FY1','RollPeriods': 'False','Scale': 6, 'Curn': 'USD'}),
('TR.RevenueMeanEstimate', {'Period': 'FY2','RollPeriods': 'False','Scale': 6, 'Curn': 'USD'})
],sort_on=None, output='pandas', debug=True)
df1
df2
df3
df4
We see the output:
df1 Instrument Revenue - Actual Revenue - Mean Estimate
0 IBM 79919 78717.97129
Revenue - Mean Estimate 0 78663.43974
df2 Instrument Revenue - High Revenue - High Revenue - Low Revenue - Low 0 IBM 80564 81180 76706 76078 df3 Instrument Revenue - Low Revenue - Low 0 IBM 76706 76078 df4 Instrument Revenue - Mean Estimate Revenue - Mean Estimate 0 IBM 78717.97129 78663.43974
- Now we are looking to retrieve the complete set of data required, while formatting the output into columns:
Please refer to the complete script
Please note that there can be only one request to get the field in get_data call. Therefore, for the same field call, but with different parameters, we use another call.
When we run this script, the result should be:
We'd like to conclude this tutorial by inviting you to experiment with both the tools and the approach. Hope you will find the tools useful and approach working, but not set in stone. We encourage you to share your results and successes, as well as your questions and problems with us on