Question
i want to capture the enteredLoanAmount
age enteredAge enteredTerm, and gender also
from the form that i have in signup page then calculations will follow
and print out the results in a table
As per now when i run in the terminal i can enter all the values and get
the results in the terminal
or i want to get the inputs from the user form that i created and display
it in a table
his is my form.py
from django import forms
from crispy_forms.helper import FormHelper
from crispy_forms.layout import Submit
class BasicForm(forms.Form):
GENDERS = (
('M', 'Male'),
('F', 'Female')
)
enteredLoanAmount = forms.CharField()
Enter_age = forms.CharField()
Enter_loan_term = forms.CharField()
gender = forms.ChoiceField(widget=forms.RadioSelect, choices=GENDERS)
# We should use a validator to make sure
# the user enters a valid number format
def __init__(self,*args, **kwargs):
super().__init__(*args,*kwargs)
self.helper = FormHelper(self)
self.helper.form.method='post'
self.helper.add_input(Submit('Save','Submit'))
my templates
{% extends 'base.html' %}
{% load crispy_forms_tags %}
{% block content %}
<div class="card" style = "width:25rem; margin:auto;">
<div class="card-header" style="background-color:#248cd1; color:white;">
Create your Order
</div>
<div class="card-body">
<form method="POST" action="">
{% csrf_token %}
{% crispy form %}
</form>
</div>
</div>
{% endblock %}
my views
from django.shortcuts import render
import math
import pandas as pd
import numpy as np
import numpy_financial as npf
from .forms import BasicForm
def signup(request):
if request.method == 'POST':
form = BasicForm(request.POST)
if form.is_valid():
# Entering the loan amount
isCorrectAmount = True
while isCorrectAmount:
enteredLoanAmount = request.POST.get(float(input("Enter Loan amount (Min -
1,000,000): ")))
if enteredLoanAmount < 1_000_000:
print("Minimum Loan amount is 1,000,000")
enteredLoanAmount = float(input("Enter Loan amount (Min - 1,000,000): "))
else:
isCorrectAmount = False
# Entering the age
isCorrectAge = True
while isCorrectAge:
enteredAge = int(input("Enter Age (18 - 50 years): "))
if enteredAge < 18 or enteredAge > 50:
print("Sorry, age does not meet criteria!")
enteredAge = int(input("Enter Age (18 - 50 years):"))
else:
isCorrectAge = False
# Entering the loan term
isCorrectTerm = True
while isCorrectTerm:
enteredTerm = int(input("Enter Loan term (3 - 35 years): "))
if enteredTerm < 3 or enteredTerm > 35:
print("Sorry, loan term does not meet criteria!")
enteredTerm = int(input("Enter Loan term (3 - 35 years):"))
else:
isCorrectTerm = False
# Entering the gender
enteredGender = input("Enter Gender (MALE or FEMALE): ")
# Assumptions
general_assumptions = pd.read_csv('data/general_assumptions.csv')
surrender_rates = pd.read_csv('data/surrender_rates.csv')
mortality_rates = pd.read_csv('data/mortality_rates.csv')
disability_rates = pd.read_csv('data/disability_rates.csv')
critical_illness = pd.read_csv('data/critical_illness_rates.csv')
amortization_assumptions = pd.read_csv('data/amortization_assumptions.csv')
sumAssured = enteredLoanAmount
coverExpiry = enteredAge + enteredTerm
if coverExpiry > 55:
print("Cover exceeds 55 years !")
def vlookup(value,df,coli,colo):
"""
vlookup function to reference other tables
"""
return next(iter(df.loc[df[coli]==value][colo]), None)
# Mortality Calculations
radix = float((general_assumptions[(general_assumptions['assumption'] ==
'radix')]).amount)
def mortality_calc():
""" Function to do calculations based on mortality rates """
mortality = []
lx = []
dx = []
Dx = []
Nx = []
Cx = []
Mx = []
Rx = []
Sx = []
if enteredGender == 'male' or enteredGender == 'MALE':
qx = 'Graduated Rates(qx) Males'
else:
qx = 'Graduated Rates(qx) Females'
for i in range(len(mortality_rates)):
agex = 18 + i
if i == 0:
lx_value = radix
lx.append(lx_value)
else:
lx_value = (lx[-1]) - (vlookup(agex,mortality_rates,'age x',qx)*lx[-1])
lx.append(lx_value)
Dx_value = (lx_value * ((1+0.04)**(-agex)))
Dx.append(Dx_value)
for i in range(len(mortality_rates)):
agex = 18 + i
try:
dx_value = lx[i] - lx[i+1]
except IndexError:
dx_value = lx[-1]
dx.append(dx_value)
Nx_value = sum(Dx[i:])
Nx.append(Nx_value)
Cx_value = ((1/((1+0.04)**(agex+1))) * dx_value)
Cx.append(Cx_value)
for i in range(len(mortality_rates)):
Mx_value = sum(Cx[i:])
Mx.append(Mx_value)
for i in range(len(mortality_rates)):
Rx_value = sum(Mx[i:])
Rx.append(Rx_value)
for i in range(len(mortality_rates)):
Sx_value = sum(Nx[i:])
Sx.append(Sx_value)
for i in range(len(mortality_rates)):
agex = 18 + i
qx_val = (vlookup(agex,mortality_rates,'age x',qx))
mortality.append({'age x': agex, qx : qx_val, 'lx': lx[i], 'dx': dx[i], 'Dx':
Dx[i],
'Nx': Nx[i], 'Cx': Cx[i], 'Mx': Mx[i], 'Rx': Rx[i], 'Sx': Sx[i]})
return pd.DataFrame(mortality)
mortality_calculations = mortality_calc()
numpy_mortality = np.array(mortality_calculations)
# Assurances and Annuities
def assurances_calc():
""" Function to calculate assurances and annuities """
aNa = []
ax_val_list = []
qx_list = []
lx_list = []
Mx_list = []
Dx = []
axn_list = []
ax_n_list = []
dxn_list = []
Ax_list = []
Axn_list = []
Ax1n_list = []
t1v_list = []
if enteredGender == 'male' or enteredGender == 'MALE':
qx = 'Graduated Rates(qx) Males'
else:
qx = 'Graduated Rates(qx) Females'
for i in range(46):
X = enteredAge + i
qx_val = vlookup(X, mortality_calculations, 'age x', qx)
qx_list.append(qx_val)
lx = vlookup(X, mortality_calculations, 'age x', 'lx')
lx_list.append(lx)
Dx_val = vlookup(X, mortality_calculations, 'age x', 'Dx')
Dx.append(Dx_val)
Mx = vlookup(X, mortality_calculations, 'age x', 'Mx')
Mx_list.append(Mx)
for i in range(46):
t = i + 1
X = enteredAge + i
ax_val = (sum(Dx[i:]))/Dx[i]
ax_val_list.append(ax_val)
if i == 0:
n = enteredTerm
if n < 1:
n = 0
aNa.append({'t': t, 'X': X, 'qx': qx_list[i], 'lx': lx_list[i], 'Dx': Dx[i],
'Mx': Mx_list[i], 'ax': ax_val_list[i], 'n': n})
n = n-1
ana_dataframe = pd.DataFrame(aNa)
for i in range(46):
X = enteredAge + i
if i == 0:
n = enteredTerm
if n < 1:
n = 0
axn = vlookup((X+n), ana_dataframe, 'X', 'ax')
axn_list.append(axn)
dxn = vlookup((X+n), ana_dataframe, 'X', 'Dx')
dxn_list.append(dxn)
n = n - 1
ax_n = ax_val_list[i] - ((dxn/Dx[i])*axn)
ax_n_list.append(ax_n)
Ax = Mx_list[i]/Dx[i]
Ax_list.append(Ax)
ana_dataframe['ax+n'] = axn_list
ana_dataframe['Dx+n'] = dxn_list
ana_dataframe['ax:n'] = ax_n_list
ana_dataframe['Ax'] = Ax_list
for i in range(46):
X = enteredAge + i
if i == 0:
n = enteredTerm
if n < 1:
n = 0
Axn = vlookup((X+n), ana_dataframe, 'X', 'Ax')
Axn_list.append(Axn)
n = n - 1
ana_dataframe['Ax+n'] = Axn_list
for i in range(46):
Ax1n = Ax_list[i] - ((dxn_list[i]/Dx[i])*Axn_list[i])
Ax1n_list.append(Ax1n)
ana_dataframe['Ax1:n'] = Ax1n_list
#Net Premium
net_premium = (enteredLoanAmount*Ax1n_list[0])/ax_n_list[0]
for i in range(46):
t = i+1
if i == 0:
t1v = 0
elif t < enteredTerm:
t1v = (enteredLoanAmount*Ax1n_list[i]) - (net_premium*ax_n_list[i])
else:
t1v = 0
t1v_list.append(t1v)
ana_dataframe['t-1 v reserve'] = t1v_list
return ana_dataframe
assurances = assurances_calc()
numpy_assurances = np.array(assurances)
def create_decrements_table():
""" function to create the decrements table """
decrements_table = []
years = 36
if enteredGender == 'male' or enteredGender == 'MALE':
dx = 'Graduated Rates(qx) Males'
bx = 'males'
else:
dx = 'Graduated Rates(qx) Females'
bx = 'females'
for year in range(years):
if year == 0:
agex = enteredAge
t_1apx = 1
else:
agex = enteredAge + year
t_1apx = decrements_table[year-1]['t-1apx'] * decrements_table[year-1]['apx'
]
if (year+1) <= enteredTerm:
qdx = vlookup(agex,mortality_calculations,'age x',dx)
qbx = vlookup(agex,disability_rates,'age x',bx)
qcx = vlookup(agex,critical_illness,'age x',bx)
aqdx = qdx*(1-(0.5*(qbx+qcx))+((1/3)*qbx*qcx))
aqbx = qbx*(1-(0.5*(qdx+qcx))+((1/3)*qdx*qcx))
aqcx = qcx*(1-(0.5*(qbx+qdx))+((1/3)*qbx*qdx))
apx = 1-aqdx-aqbx
else:
qdx = 0
qbx = qdx
qcx = qdx
aqdx = qdx
aqbx = qdx
aqcx = qdx
apx = qdx
agex = qdx
decrements_table.append({'year t':year+1, 'age':agex, 'qdx':qdx, 'qbx':qbx,
'qcx':qcx, 'aqdx':aqdx, 'aqbx':aqbx,
'aqcx':aqcx, 'apx':apx, 't-1apx':t_1apx})
return pd.DataFrame(decrements_table)
# Create the decrements table
decrements_table = create_decrements_table()
numpy_decrements = np.array(decrements_table)
def create_amortization_table():
""" Function to create the armotization table """
amortization_table = []
period = enteredTerm + 1
principal = enteredLoanAmount
installment = npf.pmt(0.042/1,enteredTerm,-principal)
for i in range(period):
if i == 0:
payment = 0
interest = 0
new_principal = 0
new_balance = principal
else:
payment = installment
interest = npf.ipmt(0.042/1,i,enteredTerm,-principal)
new_principal = npf.ppmt(0.042/1,i,enteredTerm,-principal)
new_balance = amortization_table[i-1]['Balance'] - new_principal
amortization_table.append({'Period':i, 'Payment':payment, 'Interest':
interest, 'Principal':new_principal,
'Balance':new_balance})
return pd.DataFrame(amortization_table)
# Create the amortization table
amortization_table = create_amortization_table()
numpy_amortization = np.array(amortization_table)
# Determining Premiums and Profit
initial_expenses = 0.35
second_year_expenses = 0.15
subsequent_years_expenses = 0.10
initial_commission = 0.20
second_year_commission = 0.07
subsequent_year_commission = 0.05
critical_illness_rate = 0.5
risk_discount_rate = 0.1
annual_premium = (enteredLoanAmount*numpy_assurances[0][13])/(-0.55+0.37+
numpy_assurances[0][10]-0.37*numpy_assurances[0][10])
def calculate_premiums_and_profit():
"""Function to calculate premiums and profit """
premiums = []
period = enteredTerm + 1
interest_rate = 0.1
for i in range(period):
t = i + 1
if i == 0:
expenses = initial_expenses * annual_premium
commission = initial_commission * annual_premium
elif i == 1:
expenses = second_year_expenses * annual_premium
commission = second_year_commission * annual_premium
else:
expenses = subsequent_years_expenses * annual_premium
commission = subsequent_year_commission * annual_premium
net_cashflow = annual_premium - expenses - commission
interest = net_cashflow * interest_rate
try:
loan_death_cost = numpy_decrements[i][5] * numpy_amortization[i+1][-1]
remaining_balance = (enteredLoanAmount-numpy_amortization[i+1][-1])*
numpy_decrements[i][5]
disability_cost = numpy_decrements[i][6]*numpy_amortization[i+1][-1]
critical_illness_cost = critical_illness_rate*numpy_amortization[i+1][-1]*
numpy_decrements[i][7]
cost_increase_provisions = (numpy_decrements[i][8]*numpy_assurances[i+1][-1])
- numpy_assurances[i][-1]
except IndexError:
pass
profit = net_cashflow+interest-loan_death_cost-remaining_balance-
disability_cost-cost_increase_provisions
profit_signature = profit*numpy_decrements[i][-1]
discount_vt = (1/(1+risk_discount_rate))**t
discounted_profit = profit_signature*discount_vt
discount_vt1 = (1/(1+risk_discount_rate))**(t-1)
discounted_premium = discount_vt1*annual_premium*numpy_decrements[i][-1]
premiums.append({'time t': t, 'Premium': annual_premium, 'Expenses':
expenses, 'Commission': commission,
'Net cashflow': net_cashflow, 'Interest': interest,
'Outstanding loan death cost': loan_death_cost,
'Remaining balance to beneficiaries': remaining_balance, 'Disability cost':
disability_cost,
'Critical illness cost': critical_illness_cost, 'Cost of increase in
provisions': cost_increase_provisions,
'Profit':profit, 'Profit Signature': profit_signature, 'Discount vt':
discount_vt,
'Discounted profit': discounted_profit, 'Discount vt-1': discount_vt1,
'Discounted
Premium': discounted_premium})
return pd.DataFrame(premiums)
premiums_and_profit = calculate_premiums_and_profit()
# NPV of profits
NPV_profits = sum(premiums_and_profit['Profit'])
# NPV of premiums
NPV_premiums = sum(premiums_and_profit['Discounted Premium'])
# Profit Margin
profit_margin = round((NPV_profits/NPV_premiums)*100,2)
# Round off annual premium to 0 decimal places
annual_premium = round(annual_premium,0)
print("\n\n############## PROGRAM OUTPUT ##############")
print(f"\nAnnual Premium to pay: UGX {annual_premium}")
print(f"\nCover up to: {coverExpiry} years of age")
print(f"\nProfit Margin: {profit_margin}%")
else:
form = BasicForm()
return render(request, 'signup.html', {'form': form})
def home(request):
form = BasicForm(request.POST)
return render(request, 'base.html')
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