Create an IHS Markit connection using your user name and password. c is an ihsmarkitrs object.

username = 'ABCDEF';
password = 'ABC123';
c = ihsmarkitrs(username,password);

Retrieve signal information for the last 10 days using the IHS Markit connection. Specify the ABR factor code and US Total Cap universe. Also, specify the ticker security type and percentile data format. The percentile format provides factor ranking data. d is a table that contains signal information and the date and data variables.

code = 'ABR';
universeid = 'US Total Cap';
startdate = datetime('today')-10;
enddate = datetime('today');
identifier = 'ticker';
datatype = 'percentile';
d = signals(c,code,universeid,startdate,enddate,identifier,datatype);

Access the first few rows of ranking data for the first day in the date range by using the data variable.

data = d.data{1};
head(data)

ans =

  8×2 table

    ticker    value
    ______    _____

    'SVU'       1  
    'LBY'       1  
    'TLRY'      1  
    'WIFI'      1  
    'TCS'       1  
    'AOBC'      1  
    'TTD'       1  
    'ZOES'      1  

The variables of the resulting table are ticker and value. The ticker variable contains the ticker security identifiers. The value variable contains the factor ranking data.

Find all ticker security identifiers in data that have the most attractive value using rank values 1 through 10. Create a table to store the rank values and perform an inner join to retrieve the most attractive securities. Display the last few attractive securities.

value = 1:10; % Define array of ranks 1 through 10
T = table(value','VariableNames',{'value'}); % Create table of the ranks in one variable
securities = innerjoin(data,T); % Perform inner join to find securities within the ranks
tail(securities)

ans =

  8×2 table

    ticker     value
    _______    _____

    'CDPYF'     10  
    'CNXN'      10  
    'DRNA'      10  
    'PSX'       10  
    'BRC'       10  
    'ICHR'      10  
    'MNLO'      10  
    'MBI'       10  

Use the factor rank data in your portfolio selection process or further analysis in your existing model.