Payoff is a mixed integer linear program (MILP) that minimizes the total interest cost of a loan by optimizing the payoff schedule. In other words, payoff answers, "How much should I pay each loan each month, given a budget and minimum payments?
First, payoff takes the following as inputs:
1. The term of each loan in months (1xN int array),
2. Starting principal loan amounts (1xN float array)
3. Yearly loan interest rates (1xN float array),
4. Minimum monthly payments by loan (1xN float array),
4. Budget (float)
Then, payoff returns the following:
1. The total interest cost given the optimal payment schedule (float),
2. The payment schedule (NxT array where T is the homogenous term length)
The following system of equations defines the MILP.
Objective:
Subject To:
(1)
(2)
(3)
(4)
(5)
(6)
Where D is the payment decision variable, M is a binary 'remnant' variable which is zero if the loan is paid off, P is the principal, P* is the starting principal, R is the yearly interest rate, B is the budget (i.e. maximum monthly payment), and F is the minimum monthly payment. The loan index ranges from i=0 to N, and the time-period index ranges from t=0 to Z.
Payoff is dependent on the following Python libraries:
1. ortools (https://developers.google.com/optimization/install)
2. numpy
Nice to haves are:
3. pandas
4. matplotlib
import sys
sys.path.append("C:/gitrepo/payoff/")
from source import *
>>> optimal_schedule = \
... opt_pay_schedule(
... term_months=[80, 120, 100, 100],
... principal_initial=[9871.59, 11040.71, 20879.70, 19940.74],
... rate_yearly=[0.0531, 0.0631, 0.0584, 0.0684],
... decision_min=[86.12, 70.54, 186.60, 204.72],
... budget=1800)
Optimal payoff interest cost = 6055.7417295947525
import pandas as pd
import itertools
paysched = pd.DataFrame(__ for __ in itertools.zip_longest(*optimal_schedule[1])).transpose()
>>> print(paysched[list(range(0,20))])
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
0 86.12 86.12 86.12 86.12 86.12 86.12 86.12 86.12 86.12 86.12 86.12 86.12 86.12 86.12 86.120000 86.12 86.12 86.12 86.12 86.12
1 70.54 70.54 70.54 70.54 70.54 70.54 70.54 70.54 70.54 70.54 70.54 70.54 70.54 70.54 1099.997247 1527.28 1527.28 1527.28 1527.28 1527.28
2 186.60 186.60 186.60 186.60 186.60 186.60 186.60 186.60 186.60 186.60 186.60 186.60 186.60 186.60 186.600000 186.60 186.60 186.60 186.60 186.60
3 1456.74 1456.74 1456.74 1456.74 1456.74 1456.74 1456.74 1456.74 1456.74 1456.74 1456.74 1456.74 1456.74 1456.74 427.282753 0.00 0.00 0.00 0.00 0.00
>>> print(paysched[list(range(20,40))])
20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
0 86.12 86.120000 86.12 86.12 86.12 86.12 86.12 86.12 86.12 86.12 86.12 86.12 169.826579 1800.0 1800.0 1800.0 1800.0 1188.48173 0.0 0.0
1 1527.28 850.216092 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.0 0.0 0.0 0.0 0.00000 0.0 0.0
2 186.60 863.663908 1713.88 1713.88 1713.88 1713.88 1713.88 1713.88 1713.88 1713.88 1713.88 1713.88 1630.173421 0.0 0.0 0.0 0.0 0.00000 0.0 0.0
3 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.0 0.0 0.0 0.0 0.00000 0.0 0.0
>>> print(paysched[list(range(40,60))])
40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Note: the matrix columns represents the time period (month), and the rows represents the loan index. Each cell-value represents the optimal payment amount.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent