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On Thu, Feb 17, 2022 at 9:48 PM Dushime Mudahera Richard <mudaherarich@gmail.com> wrote:

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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