def annual_savings_lst(vars, years):
res = [0 for i in range(years)]
bruto_salary = vars['Bruto Salary']
net_salary = core.net_income(bruto_salary)
rent = vars['Rent']
for i in range(years):
res[i] = int(0.1 * bruto_salary +
vars['Savings Rate From Net'] * net_salary - rent) * 12
rent *= (1 + vars['Rent Growth'])
bruto_salary *= (1 + vars['Salary Growth'])
net_salary *= (1 + vars['Salary Growth'])
return res
# run function
# print(assets(0))
# rent_savings()
import core
bruto_income = float(input("enter (bruto) salary "))
net_income = core.net_income(bruto_income)
savings = core.savings(bruto_income)
print("net income is: " + str(int(net_income)) + "\nsavings are " +
str(int(savings)))
def assets(vars): #timeperiod=vars.vars['YEARS_TO_FUTURE']):
vars_module.validate_vars(vars) # input validation
if vars['Rent'] > core.net_income(vars['Bruto Salary']) * vars[
'Savings Rate From Net']: # input validation
return 0 # can't afford rent. Saving Rate From Net var is to small or rent is too high
#monthly_savings_before_purch = int(0.1 * vars['Bruto Salary'] + vars['Savings Rate From Net'] * net_salary
# - vars['Rent'])
#annual_savings_before_purch = 12 * monthly_savings_before_purch
prepurch_savings_return = prepurch_savings_return_calc(
vars['Years To Save'])
annual_savings = annual_savings_lst(vars, vars['Years To Future'])
results_lst = [0 for i in range(vars['Years To Future'] + 1)]
compound_interest(vars['Start Amount'], vars['Years To Future'], prepurch_savings_return,\
additions_lst=annual_savings, results_lst= results_lst)
# results_lst is a list of length Years To Future + 1, now holds the savings amount for each year
# before purchase, until Years To Future
# now we need to calculate years_save = minimum years we need to save to have enough for minimum down payment
years_save = -1
for i in range(vars['Years To Future']):
if results_lst[i] >= 0.25 * house_value_calc(vars, i):
years_save = i
break
if years_save == -1:
return 0 # can't afford home, will never save enough for down payment
years_save = max(years_save, vars['Years To Save'])
'''
# savings_amnt is results_lst[
# prepurch_savings_return = 0.05
purch_price = house_value_calc(vars, vars['Years To Save']) # price of house at purchase time
#savings_before_purch = compound_interest(vars['Start Amount'], vars['Years To Save'], prepurch_savings_return,\
# annual_savings_before_purch, vars['Salary Growth'])
# take care of case when starting amount is less than 25% of house price (minimum down payment),
# save until have enough for down payment
years_save = vars['Years To Save'] # number of years to save for minimum down payment
amnt = savings_before_purch
house_price = purch_price
if savings_before_purch < 0.25 * purch_price: # minimum down payment is 25%
while amnt < 0.25 * house_price:
prepurch_savings_return = prepurch_savings_return_calc(years_save)
years_save += 1
amnt = compound_interest(vars['Start Amount'], years_save, prepurch_savings_return,\
annual_savings_before_purch, vars['Salary Growth'])
house_price = house_value_calc(vars, years_save)
if years_save == vars['Years To Future']:
return 0 # can't afford the home
'''
''' may want to add this: if input is less than the minimum num of years to save for down payment, return 0
if vars['years_save'] < years_save:
return 0 # need to save more years in order to buy home
'''
#years_save now depicts vars['years_save'] + additional years to save for minimum down payment
if vars['Years To Future'] <= years_save: # didn't buy home yet
#return int(compound_interest(vars['Start Amount'], vars['Years To Future'], prepurch_savings_return,\
# annual_savings_before_purch, vars['Salary Growth']))
return results_lst[vars['Years To Future']]
'''
if years_save >= vars['Years To Future']: # also didn't buy home yet, but waited longer, change investment return
return int(compound_interest(vars['Start Amount'], vars['Years To Future'], vars['Long Term Savings Return'],\
annual_savings_before_purch, vars['Salary Growth']))
'''
if years_save >= vars['Years To Future']:
return 0 # didn't have enough time to buy home
purch_price = house_value_calc(vars, years_save)
savings_amnt_at_purchase = results_lst[years_save]
# annual mortgage payment = monthly savings before purch. + rent fee (no longer need to pay rent)
# - keren hishtalmut savings
#mortgage payment is effected by salary growth, not by rent growth
mortgage_payment = net_salary_after_time(
vars, years_save) * vars['Savings Rate From Net'] * 12
mortgage_amnt = purch_price - savings_amnt_at_purchase
#print('house price ', int(house_price), 'amount ', int(amnt), 'mortgage amnt ', int(mortgage_amnt), 'years save ', years_save)
# print("mortgage amount: " + str(mortgage_amnt))
# should maybe change mortage payment to be list of mortgage payment, because payment is effected by salary growth
# and by rent growth
mortgage_len = mortgage_calc(vars,
mortgage_amnt,
mortgage_payment,
payment_increase_rate=vars['Salary Growth'])
if mortgage_len == -1:
return 0 #will never be able to pay off mortgage
if vars['Years To Future'] <= years_save + mortgage_len: #means bought house, but still hasn't paid off mortgage
# the timeperiod arg may be need to be fixed
# assets value = (house value at YEARS_TO_FUTURE) - (mortgage left to pay)
return int(
house_value_calc(vars, vars['Years To Future']) -
mortgage_calc(vars, mortgage_amnt, mortgage_payment,
vars['Years To Future'] -
years_save, vars['Salary Growth']))
# timeperiod > years to save + mortgage length
# print("years to pay mortgage: " + str(mortgage_len))
# how much would have paid if still renting
#rent_after_mortgage = compound_interest(vars['Rent'], years_save + mortgage_len,
# vars['Rent Growth'])
# might need to update to make salary increase at certain rate over time
#monthly_savings_after_mortgage = vars['Savings Rate From Net'] * net_salary_after_time(vars,\
# years_save + mortgage_len)\
# + rent_after_mortgage
#annual_savings_after_mortgage = 12 * monthly_savings_after_mortgage
#print(timeperiod, years_save, mortgage_len, years_down_payment)
#print( timeperiod - (years_save + mortgage_len + years_down_payment), annual_savings_after_mortgage)
annual_savings_after_mortgage = net_salary_after_time(vars, vars['Years To Future']) * \
vars['Savings Rate From Net'] * 12
savings_after_mortgage = compound_interest(
0,
vars['Years To Future'] - (years_save + mortgage_len),
vars['Long Term Savings Return'],
const_additions=annual_savings_after_mortgage,
additions_increase_rate=vars['Salary Growth'])
# print("savings before purchase: " + str(savings_before_purch))
# print("savings after mortgage: " + str(savings_after_mortgage))
# print("house worth after 50 years: " + str(end_price))
# print("total assets value: " + str(assets_value))
return int(
house_value_calc(vars, vars['Years To Future']) +
savings_after_mortgage)
def net_salary_after_time(vars, timeperiod):
return core.net_income(
compound_interest(vars['Bruto Salary'], timeperiod,
vars['Salary Growth']))
import core
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0.0, 50000.0, 10000.0)
y = [core.savings(i) for i in x]
z = [core.net_income(i) for i in x]
l = [int(y[i] + z[i]) for i in range(len(y))]
plt.plot(x, y, label='Savings')
plt.plot(x, z, label='Net Income')
plt.plot(x, l, label='Total: Savings + Net')
plt.legend(loc='upper left')
plt.xlabel('Gross Salary')
plt.ylabel('₪')
plt.savefig("graph1.png")
plt.show()