#Some libraries will be helpful to perform this program :) import pandas as pd import numpy as np import numpy_financial as npf import datetime from dateutil.relativedelta import relativedelta from decimal import Decimal import matplotlib.pyplot as plt import seaborn as sns

class MortgageLoan: def __init__(self, principal, duration, percentage, first_installment_date): self._principal = principal self._duration = duration self._percentage = percentage self._first_installment_date = first_installment_date self.last_installment_date = None @property def principal(self): return self._principal @principal.setter def principal(self, value): if value < 0: raise ValueError('Principal must be a positive int or float.') if not isinstance(value, (int, float)): raise TypeError('Principal must be type of int or float.') else: self._principal = value @property def duration(self): return self._duration @duration.setter def duration(self, value): if value < 0: raise ValueError('Duration must be a positive int.') if not isinstance(value, int): raise TypeError('Duration must be an int.') else: self._duration = value @property def percentage(self): return self._percentage @percentage.setter def percentage(self, value): if value < 0: raise ValueError('Percentage must be a positive int or float.') if not isinstance(value, (int, float)): raise TypeError('Percentage must be type of int or float.') else: self._percentage = value @property def commission_type(self): return self._commission_type @commission_type.setter def commission_type(self, value): if value in (0,1): self._commission_type = value else: raise ValueError(f'Commission type must be 0 or 1. 0 - for commission included in principal/n1 - for commission paid separately.') @property def first_installment_date(self): return self._first_installment_date @first_installment_date.setter def first_installment_date(self, value): self._first_installment_date = value if value != datetime.datetime.strptime(value, "%Y-%m-%d").strftime('%Y-%m-%d'): raise ValueError('First installment date must be a format of YYYY-MM-DD') else: self._first_installment_date = datetime.strptime(value, "%Y-%m-%d") @property def last_payment_date(self): if self.last_installment_date is None: self.last_installment_date = self.first_installment_date + relativedelta(months = self.duration) return self.last_installment_date

class AnnuityMortgage(MortgageLoan): def __init__(self, principal, duration, percentage, first_installment_date): super().__init__(principal, duration, percentage, first_installment_date) def installment(self): return round(-1*(npf.pmt(self.percentage/12, self.duration, self.principal)),2) def installment_table(self): schedule = [self.first_installment_date + relativedelta(months = i) for i in range(self.duration)] period = np.arange(self.duration) + 1 interest_due = -1*(npf.ipmt(self.percentage/12, period, self.duration, self.principal)) principal_payment = -1*(npf.ppmt(self.percentage/12, period, self.duration, self.principal)) installment_timetable = pd.DataFrame({'Payment_date': schedule, 'Principal_amount': np.around(principal_payment, decimals = 2), 'Interest_payment': np.around(interest_due, decimals = 2), 'Installment_amount':[self.installment() for _ in range(self.duration)], }) return installment_timetable

class DecreasingMortgage(MortgageLoan): def __init__(self, principal, duration, percentage, first_installment_date): super().__init__(principal, duration, percentage, first_installment_date) def first_installment(self): return round(self.principal/self.duration + self.principal * self.percentage / 12,2) def installment_table(self): schedule = [self.first_installment_date + relativedelta(months = i) for i in range(self.duration)] period = np.arange(self.duration) + 1 principal_payment = [round(self.principal/self.duration,2) for _ in range(self.duration)] principal_due = [self.principal - (self.principal/self.duration * i) for i in range(self.duration)] interest_due = [x * self.percentage/12 for x in principal_due] installment_amount = [round(a+b,2) for a,b in zip(interest_due, principal_payment)] installment_timetable = pd.DataFrame({'Payment_date': schedule, 'Principal_amount': principal_payment, 'Interest_payment': np.around(interest_due, decimals = 2), 'Installment_amount': installment_amount }) return installment_timetable

#Here is the space to define parametres of the loan pv = 280000 #principal to pay r = 0.035 #interest rate t = 300 #duration start = datetime.date.today() #start date annuitymortgage = AnnuityMortgage(pv,t, r, start) annuitymortgage_schedule = annuitymortgage.installment_table() #Value to work with first_annuity_installment = annuitymortgage.installment() decreasingmortgage = DecreasingMortgage(pv,t, r, start) decreasingmortgage_schedule = decreasingmortgage.installment_table() #Value to work with first_decreasing_installment = decreasingmortgage.first_installment() #Worse of scenario, if interest rates will grow up by 5 p.p up. sad_annuitymortgage = AnnuityMortgage(pv,t, r+0.05, start) sad_annuitymortgage_schedule = sad_annuitymortgage.installment_table() #Value to work with sad_first_annuity_installment = sad_annuitymortgage.installment() sad_decreasingmortgage = DecreasingMortgage(pv,t, r+0.05, start) sad_decreasingmortgage_schedule = sad_decreasingmortgage.installment_table() #Value to work with sad_first_decreasing_installment = sad_decreasingmortgage.first_installment() print(f"Information about mortgage loan paid in {t} monthly annuity installments," f"principal amount of {pv} and percentage of {r * 100:.2f}%. Amount of your installment is {first_annuity_installment}\n") print(annuitymortgage_schedule.head()) print("\n") print(f"Information about mortgage loan paid in {t} monthly decreasing installments" f"principal amount of {pv} and percentage of {r * 100:.2f}%. Amount of your first installment is {first_decreasing_installment}\n") print(decreasingmortgage_schedule.head())

#Annuity mortgage total interest - good scenario annuity_total_interest = annuitymortgage_schedule["Interest_payment"].sum() #Decreasing mortgage total interest - good scenario decreasing_total_interest = decreasingmortgage_schedule["Interest_payment"].sum() #Annuity mortgage total interest - bad scenario sad_annuity_total_interest = sad_annuitymortgage_schedule["Interest_payment"].sum() #Decreasing mortgage total interest - bad scenario sad_decreasing_total_interest = sad_decreasingmortgage_schedule["Interest_payment"].sum() print(f"For mortgage annuity installment you will pay {annuity_total_interest} in interest. If you have chosen " f"decreasing installment you would pay {decreasing_total_interest}. The difference is " f"{round(annuity_total_interest - decreasing_total_interest,2)}. Decreasing installment\n") print(f"In case of interest rate growth of 5 p.p. up, total difference is: \n" f"For annuity repayment scenario: {round(sad_annuity_total_interest - annuity_total_interest,2)}\n" f"For decreasing rapeyment scenario: {round(sad_decreasing_total_interest - decreasing_total_interest,2)}")

#Area chart for total cost of loan df = pd.DataFrame(annuitymortgage_schedule, columns=['Payment_date', 'Interest_payment', 'Principal_amount'])\ .set_index('Payment_date')\ .sort_values('Payment_date') pt = pd.pivot_table(df, index=['Payment_date'], values=['Interest_payment', 'Principal_amount']) pt = pt.cumsum() pt.plot.area(figsize = (7,7), xlabel = "Year", ylabel = "Total repayment") plt.title('Annuity repayment schema in breakdown for principal and interest.') df = pd.DataFrame(decreasingmortgage_schedule, columns=['Payment_date', 'Interest_payment', 'Principal_amount'])\ .set_index('Payment_date')\ .sort_values('Payment_date') pt = pd.pivot_table(df, index=['Payment_date'], values=['Interest_payment', 'Principal_amount']) pt = pt.cumsum() pt.plot.area(figsize = (7,7), xlabel = "Year", ylabel = "Total repayment") plt.title('Decreasing repayment schema in breakdown for principal and interest.') plt.show()

#Bar graph which show bad scenario of interest rates rise bar_annuity = (first_annuity_installment, sad_first_annuity_installment) bar_decreasing = (first_decreasing_installment, sad_first_decreasing_installment) df = pd.DataFrame({'Scenario':['Basic scenario', '+5 p.p interest rate'], 'installment':bar_annuity}) df.plot.bar(x='Scenario', y='installment', rot=0, figsize = (7,7), title = 'Change of annuity installment if interest rates will rise.') df = pd.DataFrame({'Scenario':['Basic scenario', '+5 p.p interest rate'], 'installment':bar_decreasing}) df.plot.bar(x='Scenario', y='installment', rot=0, figsize = (7,7), title = 'Change of decreasing installment if interest rates will rise.')

#Finally, you can download schedule as csv file for further analysis. Just use function below :) def download_schedule(): annuitymortgage_schedule.to_csv('annuityschedule.csv') decreasingmortgage_schedule.to_csv('decreasingschedule.csv') download_schedule()