Converting maturities to numeric format-CodePudding

I am trying to make a function to replace weird looking maturities into numbers:

For a reproducable example: the following block contains the data.

dict <- c("ON","TN","1W","1M","2M","3M","6M","9M","1Y","1Y3M","1Y6M","1Y9M","2Y","2Y3M", "2Y6M","2Y9M","3Y","3Y3M","3Y6M","3Y9M","4Y","4Y3M","4Y6M","4Y9M","5Y","5Y3M","5Y6M","5Y9M","6Y","6Y3M","6Y6M","6Y9M","7Y","7Y3M","7Y6M","7Y9M","8Y","8Y3M","8Y6M","8Y9M","9Y","9Y3M","9Y6M","9Y9M","10Y","11Y","12Y","13Y","14Y","15Y","20Y","25Y","30Y","40Y","50Y")

I tried to think about it as follows:

make a sub() function for the months (extract everything before M and after Y). Divide by 12.
Then add this value to another sub that I call to extract the years.
And then I guess a similar one for 0N (overnight so = 0) & TN (tomorrow next, so 1/365) although I would work with an if function for those? What do you think is most handy?

I was running this code. It correctly extracts the values after Y. Although it does not work for 10Y 11Y 12Y 13Y 14Y 15Y 20Y 25Y... Anyone that can provide support?

sub("*.Y", "", dict)

CodePudding user response：

We could replace the substring with str_replace_all and then evaluate

library(stringr)
setNames(sapply(trimws(str_replace_all(dict, c("ON" = "0",
    "TN" = "1/365", "W" = "*1/52", "M"="*1/12", "Y" = "*1 ")), 
    whitespace = "\\ "), function(x) eval(parse(text = x))), dict)

-output

          ON           TN           1W           1M           2M           3M           6M           9M           1Y         1Y3M 
 0.000000000  0.002739726  0.019230769  0.083333333  0.166666667  0.250000000  0.500000000  0.750000000  1.000000000  1.250000000 
        1Y6M         1Y9M           2Y         2Y3M         2Y6M         2Y9M           3Y         3Y3M         3Y6M         3Y9M 
 1.500000000  1.750000000  2.000000000  2.250000000  2.500000000  2.750000000  3.000000000  3.250000000  3.500000000  3.750000000 
          4Y         4Y3M         4Y6M         4Y9M           5Y         5Y3M         5Y6M         5Y9M           6Y         6Y3M 
 4.000000000  4.250000000  4.500000000  4.750000000  5.000000000  5.250000000  5.500000000  5.750000000  6.000000000  6.250000000 
        6Y6M         6Y9M           7Y         7Y3M         7Y6M         7Y9M           8Y         8Y3M         8Y6M         8Y9M 
 6.500000000  6.750000000  7.000000000  7.250000000  7.500000000  7.750000000  8.000000000  8.250000000  8.500000000  8.750000000 
          9Y         9Y3M         9Y6M         9Y9M          10Y          11Y          12Y          13Y          14Y          15Y 
 9.000000000  9.250000000  9.500000000  9.750000000 10.000000000 11.000000000 12.000000000 13.000000000 14.000000000 15.000000000 
         20Y          25Y          30Y          40Y          50Y 
20.000000000 25.000000000 30.000000000 40.000000000 50.000000000