async def eval_budget(income: float,
assets: float,
retirement_expenses: float,
expenses: List[float] = Query([]),
categories: List[str] = Query([]),
withdraw: float = 0.04,
real_rate: float = 0.06,
income_growth: float = 0.05):
try:
income /= 12
monthly_rate = (1 + (real_rate))**(1. / 12) - 1
fv = assets
goal = retirement_expenses * sum(expenses) * 12 / withdraw
original_month = 0
scaled_income = income
net_worths = []
while fv < goal:
fv = npf.fv(monthly_rate, 1, -(scaled_income - sum(expenses)), -fv)
net_worths.append(fv)
original_month += 1
scaled_income = scaled_income * (1 + income_growth / 12)
graph = np.empty((2, ceil(original_month / 12)))
curr_year = date.today().year
graph[0] = [curr_year + i for i in range(ceil(original_month / 12))]
count = 0
for index in range(len(net_worths)):
if ((index + 1) % 12 == 0):
graph[1][count] = net_worths[index]
count += 1
if (count < len(net_worths)):
graph[1][-1] = net_worths[-1]
time = {}
for cat in categories:
new_expenses = sum(expenses) - expenses[categories.index(cat)]
month = 0
fv = assets
scaled_income = income
while fv < goal:
fv = npf.fv(monthly_rate, 1, -(scaled_income - new_expenses),
-fv)
month += 1
scaled_income = scaled_income * (1 + income_growth / 12)
time[cat] = original_month - month
return ({
'months left': original_month,
'categories': time,
'graph': graph.tolist(),
'goal': goal
})
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
def simulate(simulation_trials, num_years):
n = 0
# df_define()
rate_index = a_transposed['rate'].loc[0]
pmt_index = a_transposed['pmt'].loc[0]
pv_index = abs(a_transposed['pv'].loc[0])
index_index = a_transposed['index'].loc[0]
global fv_array
global fv
fv_array.append(abs(npf.fv(rate_index / 100, 1, pmt_index, pv_index)))
for n in range(num_years - 1):
n += 1
rate_index = a_transposed['rate'].loc[n]
pmt_index = a_transposed['pmt'].loc[n]
pv_index = fv_array[n - 1]
index_index = a_transposed['index'].loc[n]
fv = round(abs(npf.fv(rate_index / 100, 1, pmt_index, pv_index)), 2)
fv_array.append(fv)
# print("rate",amount)
# print("rate_array",pmt_array)
b.append(fv_array)
def test_when_is_end_float(self, when):
args = (0.075, 20, -2000, 0)
result = npf.fv(*args) if when is None else npf.fv(*args, when)
assert_allclose(
result,
86609.362673, # Computed using Google Sheet's FV
rtol=1e-10,
)
def test_when_is_end_decimal(self, when):
args = (
Decimal('0.075'),
Decimal('20'),
Decimal('-2000'),
Decimal('0'),
)
result = npf.fv(*args) if when is None else npf.fv(*args, when)
assert_almost_equal(result, Decimal('86609.362673'), decimal=5)
async def eval_budget(income: float,
assets: float,
curr_expenses: float,
retirement_expenses: float,
withdraw: float = 0.04,
real_rate: float = 0.06,
income_growth: float = 0.05):
try:
goal = retirement_expenses * curr_expenses / withdraw
fv = assets
year = 0
incomes = []
net_worths = []
while fv < goal:
incomes.append(income)
fv = npf.fv(real_rate, 1, -(income - curr_expenses), -fv)
net_worths.append(fv)
year += 1
income = income * (1 + income_growth)
graph = np.empty((2, year))
curr_year = date.today().year
graph[0] = [curr_year + i for i in range(year)]
graph[1] = net_worths
return ({'months left': year * 12, 'graph': graph.tolist()})
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
def test_some_rates_zero(self):
# Check that the logical indexing is working correctly.
assert_allclose(
npf.fv([0, 0.1], 5, 100, 0),
[-500, -610.51], # Computed using Google Sheet's FV
rtol=1e-10,
)
def calc_parameter(ask_reply, input_string):
""" calc_parameter is a calculator to calculate the final goal based on ino given by users.
Parameters
---------
Param1 : Dictionary
The dictionary stores the float provided by user as value, stores corresponding terms as the key.
Param2 : String
The string is the specified goal for caculation.
The string implies what the dictionary would be since each value is dependent to the remaining values.
Returns
------
Return1 : Float
The final result of requested value after caculation.
"""
user_fin_dict = ask_reply
try:
if (ask_reply['input_string']) == 'present value':
output = abs(
npf.pv(user_fin_dict["required rate per year"],
user_fin_dict["required time period"],
0 - user_fin_dict["payment"],
user_fin_dict["future value"]))
elif (ask_reply['input_string']) == "future value":
output = abs(
npf.fv(user_fin_dict["required rate per year"],
user_fin_dict["required time period"],
0 - user_fin_dict["payment"],
0 - user_fin_dict["present value"]))
elif (ask_reply['input_string']) == "payment":
output = abs(
npf.pmt(user_fin_dict["required rate per year"],
user_fin_dict["required time period"],
0 - user_fin_dict["present value"],
user_fin_dict["future value"]))
elif (ask_reply['input_string']) == "required rate per year":
output = abs(
npf.rate(user_fin_dict["required time period"],
0 - user_fin_dict["payment"],
0 - user_fin_dict["present value"],
user_fin_dict["future value"]))
elif (ask_reply['input_string']) == "required time period":
output = abs(
npf.nper(user_fin_dict["required rate per year"],
0 - user_fin_dict["payment"],
0 - user_fin_dict["present value"],
user_fin_dict["future value"]))
else:
output = random.choice(unknown_input_reply)
return output
except KeyError:
print("I'm dead because of you !")
print("You should never have encountered this. What did you do?")
return None
def test_when_is_begin_decimal(self, when):
result = npf.fv(
Decimal('0.075'),
Decimal('20'),
Decimal('-2000'),
Decimal('0'),
when,
)
assert_almost_equal(result, Decimal('93105.064874'), decimal=5)
def infer_reasonable_share_price(ticker, financialreportingdf, stockpricedf, discountrate, marginrate):
years = 2 # period
dfprice = pd.DataFrame(
columns=['ticker', 'annualgrowthrate', 'lasteps', 'futureeps'])
pd.options.display.float_format = '{:20,.2f}'.format
# Find EPS Annual Compounded Growth Rate
# annualgrowthrate = financialreportingdf.epsgrowth.mean() #growth rate
# print('financialreportdf:\n',financialreportingdf)
try:
# Calcuate the rate per period
# parameter: periods , payment, present value, future value
firstEPS = financialreportingdf.eps.iloc[0]
lastEPS = financialreportingdf.eps.iloc[-1]
# Adjust firstEPS at least 1 , prevent npf.rate get NaN
if (firstEPS<1):
adj = 1 - firstEPS
firstEPS = firstEPS + adj
lastEPS = lastEPS + adj
annualgrowthrate = npf.rate(len(financialreportingdf.eps)-1, 0, -1 * firstEPS,lastEPS)
#print("Annual Growth Rate %f" % annualgrowthrate)
# Non Conservative
#print(financialreportingdf)
lasteps = financialreportingdf.eps.tail(1).values[0] # presentvalue
#print('1st eps ',financialreportingdf.eps.iloc[0])
#print('last eps ',financialreportingdf.eps.iloc[-1])
#print('annual growth rate ',annualgrowthrate)
# conservative
# lasteps = financialreportingdf.eps.mean()
# np.fv, compute the future value. parameters: interest rate , periods, payment, present value
futureeps = abs(npf.fv(annualgrowthrate, years, 0, lasteps))
dfprice.loc[0] = [ticker, annualgrowthrate, lasteps, futureeps]
except:
print('eps does not exist')
dfprice.set_index('ticker', inplace=True)
# conservative
dfprice['peratio'] = findMinimumPER(stockpricedf, financialreportingdf)
# future stock price
dfprice['FV'] = dfprice['futureeps'] * dfprice['peratio']
#print('dfprice:\n',dfprice)
#print('discountrate: %s' % discountrate)
dfprice['PV'] = abs(npf.pv(discountrate, years, 0, fv=dfprice['FV']))
if dfprice['FV'].values[0] > 0:
dfprice['marginprice'] = dfprice['PV']*(1-marginrate)
else:
dfprice['marginprice'] = 0
dfprice['lastprice'] = stockpricedf.Close.tail(1).values[0]
dfprice['suggestion'] = np.where(
(dfprice['lastprice'] < dfprice['marginprice']), 'BUY', 'SELL')
return dfprice
async def eval_budget(income: float,
assets: float,
curr_expenses: float,
retirement_expenses: float,
withdraw: float = 0.04,
income_growth: float = 0.05):
try:
goal = retirement_expenses * curr_expenses / withdraw
start_year = 1950
curr_year = date.today().year
net_worths = [None] * 75
for i in range(len(net_worths)):
init = np.random.randint(start_year, curr_year - 10)
fv = assets
counter = 0
net_worths[i] = [[], []]
while fv < 2.5 * goal and counter + init < curr_year:
if (sp_500[init + counter] != 0):
fv = npf.fv(
sp_500[init + counter], 1,
-(income *
(1 + income_growth * counter) - curr_expenses), -fv)
net_worths[i][0].append(fv)
if (fv >= goal and not net_worths[i][1]):
net_worths[i][1] = counter
counter += 1
net_worths = [row for row in net_worths if row[1]]
net_worths.sort(key=lambda x: x[1])
graph = [[], [], [], []]
graph[3] = net_worths[3 * len(net_worths) // 4 - 1]
limit = graph[3][1]
graph[0] = [curr_year + i for i in range(limit)]
graph[1] = net_worths[len(net_worths) // 4 - 1][0][:limit]
graph[2] = net_worths[len(net_worths) // 2 - 1][0][:limit]
graph[3] = graph[3][0][:limit]
lengths = [i[1] for i in net_worths]
return ({'average_years': sum(lengths) / len(lengths), 'graph': graph})
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
def futurevalue(num_years):
n = 0
# df_define()
rate_index = a_transposed['rate'].loc[0]
pmt_index = a_transposed['pmt'].loc[0]
pv_index = abs(a_transposed['pv'].loc[0])
index_index = a_transposed['index'].loc[0]
global fv_array
global fv
fv_array.append(
round(abs(npf.fv(rate_index / 100, 1, pmt_index, pv_index)), 2))
for n in range(num_years - 1):
n += 1
rate_index = a_transposed['rate'].loc[n]
pmt_index = a_transposed['pmt'].loc[n]
pv_index = fv_array[n - 1]
index_index = a_transposed['index'].loc[n]
fv = round(abs(npf.fv(rate_index / 100, 1, pmt_index, pv_index)), 2)
fv_array.append(fv)
# print("rate",amount)
# print("rate_array",pmt_array)
a.append(fv_array)
n = num_years - 1
print("rate", a_transposed['rate'].loc[n])
print("pmt", a_transposed['pmt'].loc[n])
print("PV1", fv_array[n - 1])
print("PV2", a_transposed['pmt'].loc[n])
print(
"fv",
round(
npf.fv(a_transposed['rate'].loc[n] / 100, 1,
a_transposed['pmt'].loc[n], fv_array[n - 1]), 2))
async def eval_category(income: float,
assets: float,
retirement_expenses: float,
expenses: float,
savings: float,
withdraw: float = 0.04,
real_rate: float = 0.06,
income_growth: float = 0.05):
try:
monthly_rate = (1 + (real_rate))**(1. / 12) - 1
fv = assets
goal = retirement_expenses * expenses * 12 / withdraw
original_month = 0
scaled_income = income
while fv < goal:
fv = npf.fv(monthly_rate, 1, -(scaled_income - expenses), -fv)
original_month += 1
scaled_income = scaled_income * (1 + income_growth / 12)
new_expenses = expenses - savings
month = 0
fv = assets
scaled_income = income
while fv < goal:
fv = npf.fv(monthly_rate, 1, -(scaled_income - new_expenses), -fv)
month += 1
scaled_income = scaled_income * (1 + income_growth / 12)
time_saved = original_month - month
return ({'months saved': time_saved})
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
def get_reg_dep(self) -> float:
_fv = (fv(
self.rate / (100 * self.freq),
self.freq * self.num_of_years,
0,
self.ini_dep,
self.dep_when,
) + self.fin_bal)
self.reg_dep = -pmt(
self.rate / (100 * self.freq),
self.freq * self.num_of_years,
0,
_fv,
self.dep_when,
)
return self.reg_dep
def generate_decision_1(ticker, financialdf, discountrate, marginrate,
stockpriceserie, parameters):
lasteps = financialdf.EPS.iloc[-1]
dfprice = pd.DataFrame(columns=['ticker', 'lasteps', 'futureeps'])
pd.options.display.float_format = '{:20,.2f}'.format
# print('parameters', parameters)
annualgrowthrate = float(parameters.annual_growth_rate)
pe_ratio = float(parameters.pe)
n_future_year = 5
try:
if 1 + annualgrowthrate >= 0:
futureeps = abs(npf.fv(annualgrowthrate, n_future_year, 0,
lasteps))
else:
# This is necessary to catch following condition:
# 1. Last EPS is negative value
# 2. 1 + annualgrowthrate < 0
# Necessary because if (1+annualgrowthrate)^5 will result in - value.
# And this negative value will negate the negative EPS.
# Positive result in this value is mathematicaly correct but against
# a healthy understanding about business logic.
futureeps = 0
# print('5.1', [ticker, lasteps, futureeps])
dfprice.loc[0] = [ticker, lasteps, futureeps]
# print('5.2')
except:
# print('Last EPS:', lasteps)
# print('Annual Growth Rate:', annualgrowthrate)
print('EPS does not exist or annual growth rate is missing.')
dfprice.set_index('ticker', inplace=True)
dfprice['pe'] = pe_ratio
dfprice['FV'] = dfprice['futureeps'] * dfprice['pe']
dfprice['PV'] = npf.pv(discountrate, n_future_year, 0, dfprice['FV']) * -1
# print(dfprice[['FV', 'PV']])
dfprice['marginprice'] = np.where((dfprice['futureeps'] > 0),
dfprice['PV'] * (1 - marginrate), 0)
dfprice['currentshareprice'] = stockpriceserie[-1]
dfprice['decision'] = np.where(
(dfprice['currentshareprice'] < dfprice['marginprice']), 'Buy', 'Sell')
# open_in_excel(dfprice)
return dfprice
def __init__(self, data) -> None:
self.data = data
self.rooms = self.data.get("rooms")
self.TOTALS = {}
self.O31_Y31 = []
self.D30_D39 = 15292
self.J18 = 36
self.broker = False # C11
self.debt_financing = True # J6
self.mezzanine_financing = True # J26
self.zip_code = self.data.get("zip_code") # C7
self.total_rooms = sum([tp[1] for tp in self.rooms]) # C29
# Sale Assumptions
self.sale_period = 48 # C15
self.holding_period = self.sale_period // 12 # C14
self.selling_costs = 0.04 # C16
self.annual_appreciations = 0.03 # C17
# Tax Assumptions
self.depreciation_life = 27.5 # C21
self.tax_rate = 0.2 # C22
self.capital_gain_tax = 0.25 # C23
# Operating Assumptions: Per Unit
self.managment_per_unit = 35 # F7
self.pest_trash_per_unit = 250 # F8
self.insurance_per_unit = 1000 # F9
self.water_per_unit = 125 # F10
self.utilities_per_unit = 10 # F11
self.maintenance_per_unit = 50 # F12
self.snow_landscape_per_unit = 50 # F13
self.vacancy = 0.02 # F16
# Capex Assumptions
self.initial_capex = 10000 # F19
if self.broker:
self.capex = (-0.9) * (
0.02 * self.data.get("price")) + self.initial_capex # O7
else:
self.capex = self.initial_capex
# Operating Assumptions
self.taxes = self.data.get("taxes")
self.managment = self.managment_per_unit * self.total_rooms * 12 # G7
self.pest_trash = self.pest_trash_per_unit * 12 # G8
self.insurance = self.insurance_per_unit * self.total_rooms # G9
self.water = self.water_per_unit * self.total_rooms * 12 # G10
self.utilities = self.utilities_per_unit * self.total_rooms * 12 # G11
self.maintenance = self.maintenance_per_unit * self.total_rooms * 12 # G12
self.snow_landscape = self.snow_landscape_per_unit * 12 # G13
self.opex_annual = sum( # G5
[
self.taxes,
self.managment,
self.pest_trash,
self.insurance,
self.water,
self.utilities,
self.maintenance,
self.snow_landscape,
])
self.D29 = self.rent_roll_assumptions() # D29
# self.opex_ratio = ceil(self.opex_annual / (self.D29 * 12)) # G14
# Senior Debt Assumptions
self.down_payment_percent = 0.25 # J7
self.down_payment_sum = self.down_payment_percent * self.data.get(
"price") # J8
self.total_investment = int(self.down_payment_sum + self.initial_capex)
self.assumed_value = np.fv(
self.annual_appreciations / 12,
self.sale_period,
0,
-self.data.get("price"),
)
self.sale_proceeds_cash_flow = self.assumed_value * (
1 - self.selling_costs)
self.loan_amount = self.senior_debt_assumptions().get("loan_amount")
self.monthly_payment = self.senior_debt_assumptions().get(
"monthly_payment")
self.interest_rate = self.senior_debt_assumptions().get(
"interest_rate")
refi_table_interest = int(self.loan_amount * (self.interest_rate / 12))
refi_table_principal = self.monthly_payment - refi_table_interest
self.REFI_TABLE = {
1: {
"beginning_bal": self.loan_amount,
"refi_table_interest": refi_table_interest,
"refi_table_principal": refi_table_principal,
"ending_bal": self.loan_amount - refi_table_principal,
},
}
for j in range(2, 361):
refi_table_interest_dynamic = round(
self.REFI_TABLE.get(j - 1).get("ending_bal") *
self.interest_rate / 12)
refi_table_principal_dynamic = (self.monthly_payment -
refi_table_interest_dynamic)
self.REFI_TABLE.update(
{
j: {
"beginning_bal":
self.REFI_TABLE.get(j - 1).get("ending_bal"),
"refi_table_interest":
refi_table_interest_dynamic,
"refi_table_principal":
refi_table_principal_dynamic,
"ending_bal":
self.REFI_TABLE.get(j - 1).get("ending_bal") -
refi_table_principal_dynamic,
}
}, )
self.net_proceed = self.sale_proceeds_cash_flow - self.REFI_TABLE.get( # M24
self.sale_period).get("ending_bal")
self.CASH_FLOW_TABLE = {
1: {
"gpi": self.D30_D39,
"vl": self.D30_D39 * self.vacancy,
"opex": (self.opex_annual / 12),
"capex": -self.capex / 12,
"dbtsvrs": -(self.monthly_payment),
}
}
for i in range(2, self.holding_period + 1):
current_gpi = round(
self.CASH_FLOW_TABLE[i - 1].get("gpi") +
(self.CASH_FLOW_TABLE[i - 1].get("gpi") * 0.01),
0,
)
current_opex = round(self.CASH_FLOW_TABLE[i - 1].get("opex") +
(self.CASH_FLOW_TABLE[i - 1].get("opex") *
0.02))
current_capex = round(self.CASH_FLOW_TABLE[i - 1].get("capex") * 0,
0)
self.CASH_FLOW_TABLE.update({
i: {
"gpi": current_gpi,
"vl": ceil(current_gpi * self.vacancy),
"opex": current_opex,
"capex": current_capex,
"dbtsvrs": -(self.monthly_payment),
},
})
# Calculate investment_1
investment_1 = npf.pv(rate=0.03, nper=15, pmt=0, fv=10000)
# Note that the present value returned is negative, so we multiply the result by -1
print("Investment 1 is worth " + str(round(-investment_1, 2)) +
" in today's dollars")
# Calculate investment_2
investment_2 = npf.pv(rate=0.05, nper=10, pmt=0, fv=10000)
print("Investment 2 is worth " + str(round(-investment_2, 2)) +
" in today's dollars")
import numpy_financial as npf
# Calculate investment_1
investment_1 = npf.fv(rate=0.05, nper=15, pmt=0, pv=-10000)
print("Investment 1 will yield a total of $" + str(round(investment_1, 2)) +
" in 15 years")
# Calculate investment_2
investment_2 = npf.fv(rate=0.08, nper=15, pmt=0, pv=-10000)
print("Investment 2 will yield a total of $" + str(round(investment_2, 2)) +
" in 15 years")
import numpy as np
import numpy_financial as npf
# Calculate investment_1
investment_1 = npf.fv(rate=0.08, nper=10, pmt=0, pv=-10000)
print("Investment 1 will yield a total of $" + str(round(investment_1, 2)) +
" in 10 years")
future value : -> float 终值
'''
if interest_type == 'simple':
final = principal * (1 + rate * n)
elif interest_type == 'compound':
final = principal * (1 + rate)**n
elif interest_type == 'continuous':
final = principal * np.exp(rate * n)
else:
raise Exception('Invalid Type!')
return final
#%%
# 计算终值
npf.fv(0.1, 4, 0, -1000)
# 计算现值
npf.pv(0.1, 4, 0, -1500)
# 计算净现值
rate, cashflows = 0.08, [-40_000, 5_000, 8_000, 12_000, 30_000]
npf.npv(rate, cashflows).round(5)
#%%
npf.pv(0.06, 3, -100, 0)
npf.pv(0.06, 3, -100, 0, when='begin')
# 验证
npf.pv(0.06, 3, 100, 0, when='begin') / npf.pv(0.06, 3, 100, 0) == 1.06
def test_float(self):
assert_allclose(
npf.fv(0.075, 20, -2000, 0, 0),
86609.362673042924,
rtol=1e-10,
)
def future_value(rate, nper, pmt, pv):
global y
# y = Decimal(npf.fv(rate, nper, pmt, pv))
y = (npf.fv(rate, nper, pmt, pv))
)
#SAVING AND LOADING NUMPY OBJECTS
arr_15 = np.array([[1, 2], [3, 4]])
np.save('randarray', arr_15)
arr_16 = np.load('randarray.npy')
arr_16
np.savetxt('randcsv.csv', arr_15)
arr_17 = np.loadtxt('randcsv.csv')
print(arr_17)
print(
"#**********************#FINANCIAL FUNCTIONS#**************************#")
#FINANCIAL FUNCTIONS
import numpy_financial as npf
npf.fv(8 / 12, 10 * 12, -400, 400)
period = np.arange(1 * 12) + 1
principle = 3000.00
ipmt = npf.ipmt(0.0925 / 12, period, 1 * 12, principle)
ppmt = npf.ipmt(0.0925 / 12, period, 1 * 12, principle)
for payment in period:
index = payment - 1
principle = principle + ppmt[index]
print(
f"{payment} {np.round(ppmt[index],2)} {np.round(ipmt[index],2)} {np.round(principle,2)}"
)
np.round(npf.nper(0.0925 / 12, -150, 3000.00), 2)
npf.npv(0.08, [-1500, 4000, 5000, 6000, 7000])
def test_broadcast(self):
result = npf.fv([[0.1], [0.2]], 5, 100, 0, [0, 1])
# All values computed using Google Sheet's FV
desired = [[-610.510000, -671.561000], [-744.160000, -892.992000]]
assert_allclose(result, desired, rtol=1e-10)
def plot_affordability(
savings: int, saving_rate: int, r: float, income: int, rate_type: str,
lti: float,
target_date: str) -> Tuple[go.Figure, str, str, str, str, str]:
"""
Callback to populate data in the savings plot according to the input values entered by user.
Assumes that all interest is paid monthly at a rate of 1/12 * r and all reinvested.
Stores data to data-store.
Args:
savings: Total current savings
saving_rate: Amount saved each month
r: interest rate (%)
income: user input income (£ thousands)
rate_type: type of stamp duty rate payable
lti: loan to income ratio
target_date: purchase date with format YYYY-MM-DD
"""
if all(v is not None
for v in [savings, saving_rate, r, income, lti, target_date]):
fig = go.Figure()
start_date = datetime.datetime.today().date()
target_date = datetime.datetime.strptime(target_date, "%Y-%m-%d")
periods = (target_date.year - start_date.year) * 12 + (
target_date.month - start_date.month) + 1
x = pd.date_range(datetime.datetime.now(), periods=periods, freq="M")
savings_array = np.array([
npf.fv((r / 100) / 12, i, -saving_rate, -(savings * 1000))
for i in range(periods)
])
mortgage = lti * (income * 1000)
budget = np.array([
iterative_p(s, mortgage, stamp_duty_payable, rate_type,
target_date)[1] for s in savings_array
])
fig.add_trace(
go.Scatter(
x=x,
y=budget,
fillcolor="rgba(0,176,246,0.2)",
line={
"color": "black",
"width": 0.8
},
hovertemplate=
"Date: %{x} \nMax affordable: £%{y:,.3r}<extra></extra>",
))
# Work out the values on the target date
start_date = datetime.datetime.today()
num_months = (target_date.year - start_date.year) * 12 + (
target_date.month - start_date.month)
mortgage = lti * income * 1000
savings_on_target_date = npf.fv((r / 100) / 12, num_months,
-saving_rate, -(savings * 1000))
# Work out deposit, stamp duty and max affordable price for each mortgage size
deposit, property_value, stamp_duty = iterative_p(
savings_on_target_date, mortgage, stamp_duty_payable, rate_type,
target_date)
deposit_on_target_date_str = f"£{int(deposit): ,}"
mortgage_on_target_date_str = f"£{int(mortgage): ,}"
property_value_on_target_date_str = f"£{int(property_value): ,}"
stamp_duty_on_target_date_str = f"£{int(stamp_duty): ,}"
data_storage = {
"savings": savings_on_target_date / 1000,
"deposit": deposit,
"mortgage": mortgage,
"value": property_value,
"stamp_duty": stamp_duty,
"stamp_duty_rate": rate_type,
"income": income,
"target_date": target_date.strftime("%Y-%m-%d")
}
data_storage = json.dumps(data_storage)
return (
fig,
deposit_on_target_date_str,
mortgage_on_target_date_str,
property_value_on_target_date_str,
stamp_duty_on_target_date_str,
data_storage,
)
else:
raise PreventUpdate
import numpy_financial as npf res = npf.fv(rate=1, nper=1, pmt=0, pv=-100) print(res)
print('*'*50)
while continue_yn=='y':
if question == 'ask':
question = input('Do you wish to (M)anually Calculate your PV & FV, or run the (A)ssignment Numbers (M or A)?').upper()
if question == 'M':
print()
cash = float(input('Enter in the Original Price: '))
rate = float(input("Enter in the Intreset Rate (i.e. 5% is 0.05): "))
nper = float(input("Enter in the Number of Years: "))
pmt = float(input("Enter in the Yearly Revenue: "))
pv = np.pv(rate, nper, pmt, cash)
fv = np.fv(rate, nper, pmt, pv)
print('*'*50)
print(f'Intial Investment:$ {cash}')
print(f'Intrest Rate: {rate*100}%')
print(f'Revenue:$ {pmt}')
print(f'Number of Periods: {nper}')
investmentGB(pv, fv)
print()
continue_yn = input('Do you wish to continue? (Y or N)').lower()
print()
elif question == 'A':
import numpy_financial as npf
'''fv：计算预估投资价值'''
rate = 0.035
nper = 20
pmt = -2000
pv = -1000000
fv = npf.fv(rate=rate / 12, nper=nper * 12, pmt=pmt, pv=pv)
# rate：年化投资回报率
# nper：投资年数
# pmt：每月投入资金（负值）
# pv：期初值（负值）
print(
f'期初资产￥{abs(pv)}元，每月再投入￥{abs(float(pmt)):.2f}元，年均收益率{rate * 100:.2f}%，{nper}年后期末价值为：￥{abs(fv):.2f}元。'
)
'''pv：计算未来金额在现在的价值'''
rate = 0.035
nper = 20
pmt = -2000
fv = 5000000
pv = npf.pv(rate=rate / 12, nper=nper * 12, pmt=pmt, fv=fv)
# rate：年化投资回报率
# nper：投资年数
# pmt：每月投入资金（负值）
# fv：预期达到的资产总值(正值）
print(
f'年均收益率{rate * 100:.2f}%，每月投入￥{abs(float(pmt)):.2f}元，投资{nper}年后，期末资产￥{abs(float(fv)):.2f}元，需要投入本金：￥{abs(pv):.2f}元。'
)
'''pmt：计算每期支付金额'''
rate = 0.075
nper = 20
pv = 1000000
def test_decimal(self):
assert_almost_equal(
npf.fv(Decimal('0.075'), Decimal('20'), Decimal('-2000'), 0, 0),
Decimal('86609.36267304300040536731624'),
decimal=10,
)
def future_value(n, iy, pv):
future_value = pv * (1 + iy)**n
print(future_value, " future value of inv")
def discount_factor(iy, n):
disc = 1 / (1 + iy)**n
print(str(round(disc, 5)), ' discount factor')
TVM.compound_int(n=2, iy=.05, pv=100)
TVM.future_value(n=2, iy=.05, pv=100)
TVM.discount_factor(n=2, iy=.05)
inv = npf.pv(rate=.05, nper=10, pmt=0, fv=100000)
investment_2 = npf.fv(rate=.08, nper=10, pmt=0, pv=-100000)
print("Investment 2 will yield a total of $" + str(round(investment_2, 2)) +
" in 10 years")
print(inv)
# Calculate investment_3
investment_3 = npf.fv(rate=.08, nper=10, pmt=0, pv=-100000)
print("Investment 3 will yield a total of $" + str(round(investment_3, 2)) +
" in 10 years")
# Calculate investment_3 and adjsut for inflation of 3%
investment_3_discounted = npf.pv(rate=.03, nper=10, pmt=0, fv=investment_3)
print("After adjusting for inflation, investment 3 is worth $" +
str(round(-investment_3_discounted, 2)) + " in today's dollars")
#Discounting cashflows with NPV
# NPV = sum of all discounted cashflows
def get_fv(self):
return fin.fv(self.rate, self.n, self.pmt, self.pv, self.when)[0]
def update_plot(
cash_r: float,
property_r: float,
securities_r: float,
rental_income: int,
rental_income_months: int,
cash_allocation: int,
securities_allocation: int,
mortgage_idx: int,
bills: int,
housing_upkeep: int,
rent_out: int,
savable_income: int,
allocation_scenarios: str,
mortgage_data: str,
) -> Tuple[go.Figure, str]:
"""
Updates plot showing household balance sheet and income / expenditure over time.
Args:
cash_r: rate of return on cash (%)
property_r: rate of return on property (%)
securities_r: rate of return on securities (%)
rental_income: expected rental income (£ /month)
rental_income_months: number of months expected rental income
cash_allocation: user initial allocation to cash (£ ,000)
securities_allocation: user initial allocation to securities (£ ,000)
mortgage_idx: index of selected mortgage
bills: monthly bills (£)
housing_upkeep: monthly housing upkeep (£)
rent_out: monthly rent expenditure (£)
savable_income: monthly savable income (£)
allocation_scenarios: JSON str of saved scenarios
mortgage_data: JSON str of saved mortgage data
Returns:
go.Figure object of household balance sheet and income / expenditure statement
"""
# Will only show plot if a mortgage has been selected
if mortgage_idx is None:
raise PreventUpdate
data = json.loads(mortgage_data)
mortgage = data[mortgage_idx]
# Set date range to mortgage term
term = mortgage.get("term", 20) * 12
x = pd.date_range(datetime.datetime.now(), periods=term, freq="M")
fig = go.Figure()
# 3. Liabilities
if mortgage.get("mortgage_size", 0) == 0:
mortgage_balance = np.array([0] * term)
else:
mortgage_balance = _get_mortgage_balance(mortgage)
fig.add_trace(
go.Scatter(
x=x,
y=mortgage_balance,
fillcolor="rgba(0,176,246,0.2)",
line={
"color": "red",
"width": 1.5
},
name="Liabilities",
hovertemplate="Date: %{x} \nTotal value: £%{y:,.3r}<extra></extra>",
))
# 4. Assets
cash_array = np.array([
npf.fv((cash_r / 100) / 12, i, 0, -(cash_allocation * 1000))
for i in range(term)
])
fig.add_trace(
go.Scatter(
x=x,
y=cash_array,
fillcolor="rgba(0,176,246,0.2)",
line={
"color": "green",
"width": 0.8
},
name="Cash",
hovertemplate="Date: %{x} \nCash value: £%{y:,.3r}<extra></extra>",
))
purchase_price = mortgage.get("purchase_price")
property_array = np.array([
npf.fv((property_r / 100) / 12, i, 0, -(purchase_price * 1000))
for i in range(term)
])
fig.add_trace(
go.Scatter(
x=x,
y=property_array,
fillcolor="rgba(0,176,246,0.2)",
line={
"color": "orange",
"width": 0.8
},
name="Property",
hovertemplate=
"Date: %{x} \nProperty value: £%{y:,.3r}<extra></extra>",
))
# TODO: Fix bug where not updated if rent isn't set to a number
# Assumes that all net income is reinvested in securities
if all(v is not None for v in
[savable_income, rental_income, bills, housing_upkeep, rent_out]):
# Get the net income during rent period
net_income_rental_period = _get_net_savings(savable_income,
rental_income, bills,
housing_upkeep, rent_out)
rental_income_months = min(term, rental_income_months)
net_income_rental_period = np.array([net_income_rental_period] *
rental_income_months)
securities_array_rent = np.array([
npf.fv((securities_r / 100) / 12, i, -net_income,
-(securities_allocation * 1000))
for i, net_income in enumerate(net_income_rental_period)
])
# Get the net income during post rent period
post_rent_months = term - rental_income_months
if post_rent_months > 0:
net_income_post_rental_period = _get_net_savings(
savable_income, 0, bills, housing_upkeep, rent_out)
net_income_post_rental_period = np.array(
[net_income_post_rental_period] * post_rent_months)
start_balance = (securities_array_rent[-1]
if len(securities_array_rent) > 0 else
securities_allocation * 1000)
securities_array_post_rent = np.array([
npf.fv((securities_r / 100) / 12, i, -net_income,
-start_balance)
for i, net_income in enumerate(net_income_post_rental_period)
])
securities_array = np.append(securities_array_rent,
securities_array_post_rent)
else:
securities_array = securities_array_rent
else:
securities_array = np.array([
npf.fv((securities_r / 100) / 12, i, -0,
-(securities_allocation * 1000)) for i in range(term)
])
fig.add_trace(
go.Scatter(
x=x,
y=securities_array,
fillcolor="rgba(0,176,246,0.2)",
line={
"color": "purple",
"width": 0.8
},
name="Securities",
hovertemplate=
"Date: %{x} \nSecurities value: £%{y:,.3r}<extra></extra>",
))
total_array = cash_array + property_array + securities_array
fig.add_trace(
go.Scatter(
x=x,
y=total_array,
fillcolor="rgba(0,176,246,0.2)",
line={
"color": "green",
"width": 1.5
},
name="Total assets",
hovertemplate="Date: %{x} \nTotal value: £%{y:,.3r}<extra></extra>",
))
# 5. Wealth
wealth = total_array - mortgage_balance
fig.add_trace(
go.Scatter(
x=x,
y=wealth,
fillcolor="rgba(0,176,246,0.2)",
line={
"color": "black",
"width": 2
},
name="Wealth",
hovertemplate="Date: %{x} \nTotal value: £%{y:,.3r}<extra></extra>",
))
# Show other scenarios
if allocation_scenarios is not None:
allocation_scenarios = json.loads(allocation_scenarios)
for scenario in allocation_scenarios:
dates = [
datetime.datetime.strptime(i, "%Y-%m-%d")
for i in scenario["x"]
]
data = dict(zip(dates, scenario["wealth"]))
scenario_data = []
for date in x:
date = date.to_pydatetime().replace(hour=0,
minute=0,
second=0,
microsecond=0)
if date in data.keys():
scenario_data.append(data[date])
else:
scenario_data.append(np.nan)
fig.add_trace(
go.Scatter(
x=x,
y=scenario_data,
fillcolor="rgba(0,176,246,0.2)",
line={
"color": "grey",
"width": 2
},
name=scenario["name"],
hovertemplate=
"Date: %{x} \nTotal value: £%{y:,.3r}<extra></extra>",
))
x_as_str = x.format(formatter=lambda x: x.strftime("%Y-%m-%d"))
allocation_scenario = json.dumps({
"x": list(x_as_str),
"wealth": list(wealth)
})
return fig, allocation_scenario
def test_when_is_begin_float(self, when):
assert_allclose(
npf.fv(0.075, 20, -2000, 0, when),
93105.064874, # Computed using Google Sheet's FV
rtol=1e-10,
)
