def aggregate_results(self, user):
t = Transaction()
print('Start aggregating results')
year_of_death = 2080
current_pf_value = t.get_pf_value(timezone.now().date(), user)
median_income, median_expense = self.calc_average(user)
total_wealth = current_pf_value + self.get_current_wealth(user)
timespan = 3*365
from_date = (timezone.now() - datetime.timedelta(days=timespan)).date()
to_date = timezone.now().date()
# import pdb;pdb.set_trace()
# print(from_date, to_date, timespan)
# print(t.get_roi('All', from_date, to_date, user))
current_interest_rate = math.pow(t.get_roi(from_date, to_date, user) + 1,
Decimal(365/timespan))-1
# Regression to mean
# confidence for current_interest_rate as a predictor for future success
confidence = 0.2
# Average interest rate as more accurate predictor. Here Dow Jones performance between 1975 and 2013
average_interest_rate = 0.088
expected_interest_rate = (current_interest_rate - average_interest_rate) * confidence + average_interest_rate
# Recalibrate expected interest rate for retirement time with new assumptions
confidence = 0.1
# Average interest rate as more accurate predictor. Here Dow Jones performance between 1975 and 2013
average_interest_rate_retirement = 0.03
expected_interest_rate_retirement = (current_interest_rate - average_interest_rate_retirement) * confidence + average_interest_rate_retirement
delta_2015 = self.month_delta('2015-12-31')
# print(delta_2015)
wealth_in_2015, development = self.calc_wealth_next_month(self.get_current_wealth(user),
current_pf_value,
delta_2015,
median_income,
median_expense,
expected_interest_rate, [])
result_development = {}
result_development['incomeexpense'] = median_income-median_expense
result_development['interest'] = expected_interest_rate*100
result_development['interest_retirement'] = expected_interest_rate_retirement*100
for year_of_retirement in [2020, 2025, 2030]:
# print(year_of_retirement)
delta = self.month_delta(str(year_of_retirement)+'-12-31')
wealth, development = self.calc_wealth_next_month(self.get_current_wealth(user),
current_pf_value,
delta,
median_income,
median_expense,
expected_interest_rate, [])
# print('Wealth ok')
# print(wealth, year_of_retirement)
monthly_pension, development = self.mp(wealth,
year_of_retirement,
year_of_death,
expected_interest_rate_retirement, development)
# print('Pension ok', monthly_pension)
short_development = []
for num, item in enumerate(development):
if num % (12*5) == 0:
short_development.append(item)
result_development[year_of_retirement] = short_development
result_development['wealth' + str(year_of_retirement)] = wealth
result_development['pension' + str(year_of_retirement)] = monthly_pension
#result.append({'text': 'Estimated wealth '+str(year_of_retirement), 'value': wealth})
#result.append({'text': 'Retirement starting in '+str(year_of_retirement)+
# ', monthly payments, when using the money until '+str(year_of_death)+
# ', monthly payments:',
# 'value': monthly_pension})
return result_development
