How to write a javascript formula for the time to clear debt on a credit card calculator?

Question

I have a web developer creating a credit card calculator in Javascript and need a formula to use for calculating the time to clear debt based on inputs of balance, APR, minimum repayment per month (%) and minimum repayment per month (£).

I currently have the below formula but need to adjust it to account for the check to see which minimum repayment to use (£ or %) to occur every month rather than just at the start of the calculation.

We are trying to replicate the minimum repayment calculators seen on MSE and Which.

Please help!

$a$ = credit card balance

$b$ = repayment amount per month

$c$ = credit card APR

$e$ = current credit card monthly rate

$h$ = total interest paid

$k$ = minimum % required per month

$m$ = minimum amount required per month

$p$ = amount payable based on %

$q$ = time to clear debt

$e$ = $(\text{POWER}(((\frac{c}{100}+1),0.08333)-1) \times 100)\%$

$p = k \times a$

Formula for time to clear debt (q):

IF

$p > m, q = \frac{\log (\frac{-p}{ea-p})}{\log(1+e)}, q = \frac{log (\frac{-m}{ea-m})}{log(1+e)}$

Answer 1

I typed this toy example in Python before you confirmed you need Javascript but, to be honest, it's fairly banal to port it to any other language.

It's just a toy example, which needs to be tidied up and made into a function, but I'm hoping it can be a starting point.

Note I am assuming that the rate remains constant throughout.

You can of course tweak and perfect the example in many ways. In Python you would probably want to use the decimal class - not sure in Javascript.

import pandas as pd
import numpy as np

df = pd.DataFrame(columns = ['balance bop',
                             'actual payment','interest due','interest paid','interest unpaid','amortisation',
                             'balance eop'], index = np.arange(0,500), data =0)

df.loc[0,'balance eop'] = 10e3
rate = 1e-2 # ie 12% per year
min_p = 150

for r in np.arange(1,df.shape[0]):
    df.loc[r,'balance bop'] = df.loc[r-1,'balance eop']
    df.loc[r,'actual payment'] = min( df.loc[r,'balance bop'], min_p)
    available = df.loc[r,'actual payment']
    df.loc[r,'interest due'] = df.loc[r-1,'interest unpaid'] * (1 + rate) + df.loc[r,'balance bop'] * rate
    df.loc[r,'interest paid'] = min(available , df.loc[r,'interest due']   )
    df.loc[r,'interest unpaid'] = df.loc[r,'interest due'] - df.loc[r,'interest paid']
    available -= df.loc[r,'interest paid']
    df.loc[r,'amortisation'] = min( df.loc[r,'balance bop' ], available )
    df.loc[r,'balance eop'] = round(df.loc[r,'balance bop'] - df.loc[r,'amortisation'], 3)