def Macaulay_Duration(M, r, rate, n, m):
'''
计算麦考利久期
-------------
Params
M: -> float 票面金额
r: -> float 贴现率
rate: -> float 票面利率
n: -> int 年份
m: -> int 每年计息次数
Returns
-------
D1: -> float 麦考利久期(以期数计)
D2: -> float 麦考利久期(以年计)
'''
rate = rate / m
r = r / m
n = n * m
C = rate * M
PV = -npf.pv(r, n, C, M)
df = pd.DataFrame(columns=['期数', '现金流', '贴现值', '占价格比率', '与期数乘积'])
df.loc[:, '期数'] = np.arange(1, n + 1)
df.loc[:, '现金流'] = np.array([C] * (n - 1) + [M + C])
df.loc[:,
'贴现值'] = df.loc[:, '现金流'].values / ((1 + r)**df.loc[:, '期数'].values)
df.loc[:, '占价格比率'] = df.loc[:, '贴现值'] / PV
df.loc[:, '与期数乘积'] = df.loc[:, '期数'].values * df.loc[:, '占价格比率']
D1 = df.loc[:, '与期数乘积'].sum()
D2 = D1 / m
return D1, D2
def discounted_payback_period(self):
# discount future cash flows at desired return rate
annual_cash_flow_df = self.annual_cashflow_df.copy()
annual_cash_flow_df[DISCOUNTED_CASHFLOW] = np_fi.pv(
self.annual_discount_rate,
pmt=0,
nper=annual_cash_flow_df.index,
fv=-annual_cash_flow_df[CASHFLOW])
annual_cash_flow_df[CUMULATIVE_CASHFLOW] = np.cumsum(
annual_cash_flow_df[DISCOUNTED_CASHFLOW])
# get when the project becomes profitable
mask_positive = annual_cash_flow_df[CUMULATIVE_CASHFLOW] >= 0
try:
min_full_year = annual_cash_flow_df[
mask_positive].index.values.min() - 1
profit_point = min_full_year + abs(
annual_cash_flow_df.loc[min_full_year, CUMULATIVE_CASHFLOW] /
annual_cash_flow_df.loc[min_full_year + 1,
DISCOUNTED_CASHFLOW])
except:
raise Exception("Project not profitable within given time period")
annual_cash_flow_df.to_csv(
os.path.join(self.results_path, self.results_file_name))
return round(profit_point, 2)
def calculate_typeB(self, data):
data['principal_amount'] = npf.pv(self.eair, data['loan_term'],
-data['monthly_amortization'])
data['sum_payments'] = data['monthly_amortization'] * data['loan_term']
data['total_interest'] = data['sum_payments'] - \
data['principal_amount']
return data
def get_pres_val(self) -> float:
return pv(
self.rate / (100 * self.freq),
self.freq * self.num_of_years,
0,
self.fin_bal,
self.dep_when,
)
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 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
def get_ret_fund(self) -> float:
self.ret_fund = -round(
pv(
self.rate / (100 * self.freq),
self.freq * self.num_of_years,
self.reg_wdr,
when=self.wdr_when,
),
2,
)
return self.ret_fund
def PV(
rate: func_xltypes.XlNumber,
nper: func_xltypes.XlNumber,
pmt: func_xltypes.XlNumber,
fv: func_xltypes.XlNumber = 0,
type: func_xltypes.XlNumber = 0
) -> func_xltypes.XlNumber:
"""PV, one of the financial functions, calculates the present value of a
loan or an investment, based on a constant interest rate.
https://support.office.com/en-us/article/
pv-function-23879d31-0e02-4321-be01-da16e8168cbd
"""
return npf.pv(float(rate),
float(nper),
float(pmt),
fv=float(fv),
when=int(type))
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 bond_pricing(rate, n, M, m):
'''
零息债券定价
-------
Params
rate: -> float 贴现率
n: -> flaot 期限
M: -> float 票面金额
m: -> int 每年付息次数
Return
price: -> float 债券价格
'''
# 计算贴现率
rate = rate / m
# 计算期数
n = n * m
price = npf.pv(rate, n, 0, -M)
return price
def test_when(self):
# begin
assert_equal(npf.rate(10, 20, -3500, 10000, 1),
npf.rate(10, 20, -3500, 10000, 'begin'))
# end
assert_equal(npf.rate(10, 20, -3500, 10000),
npf.rate(10, 20, -3500, 10000, 'end'))
assert_equal(npf.rate(10, 20, -3500, 10000, 0),
npf.rate(10, 20, -3500, 10000, 'end'))
# begin
assert_equal(npf.pv(0.07, 20, 12000, 0, 1),
npf.pv(0.07, 20, 12000, 0, 'begin'))
# end
assert_equal(npf.pv(0.07, 20, 12000, 0),
npf.pv(0.07, 20, 12000, 0, 'end'))
assert_equal(npf.pv(0.07, 20, 12000, 0, 0),
npf.pv(0.07, 20, 12000, 0, 'end'))
# begin
assert_equal(npf.pmt(0.08 / 12, 5 * 12, 15000., 0, 1),
npf.pmt(0.08 / 12, 5 * 12, 15000., 0, 'begin'))
# end
assert_equal(npf.pmt(0.08 / 12, 5 * 12, 15000., 0),
npf.pmt(0.08 / 12, 5 * 12, 15000., 0, 'end'))
assert_equal(npf.pmt(0.08 / 12, 5 * 12, 15000., 0, 0),
npf.pmt(0.08 / 12, 5 * 12, 15000., 0, 'end'))
# begin
assert_equal(npf.ppmt(0.1 / 12, 1, 60, 55000, 0, 1),
npf.ppmt(0.1 / 12, 1, 60, 55000, 0, 'begin'))
# end
assert_equal(npf.ppmt(0.1 / 12, 1, 60, 55000, 0),
npf.ppmt(0.1 / 12, 1, 60, 55000, 0, 'end'))
assert_equal(npf.ppmt(0.1 / 12, 1, 60, 55000, 0, 0),
npf.ppmt(0.1 / 12, 1, 60, 55000, 0, 'end'))
# begin
assert_equal(npf.nper(0.075, -2000, 0, 100000., 1),
npf.nper(0.075, -2000, 0, 100000., 'begin'))
# end
assert_equal(npf.nper(0.075, -2000, 0, 100000.),
npf.nper(0.075, -2000, 0, 100000., 'end'))
assert_equal(npf.nper(0.075, -2000, 0, 100000., 0),
npf.nper(0.075, -2000, 0, 100000., 'end'))
def test_decimal_with_when(self):
"""
Test that decimals are still supported if the when argument is passed
"""
# begin
assert_equal(
npf.rate(Decimal('10'), Decimal('20'), Decimal('-3500'),
Decimal('10000'), Decimal('1')),
npf.rate(Decimal('10'), Decimal('20'), Decimal('-3500'),
Decimal('10000'), 'begin'))
# end
assert_equal(
npf.rate(Decimal('10'), Decimal('20'), Decimal('-3500'),
Decimal('10000')),
npf.rate(Decimal('10'), Decimal('20'), Decimal('-3500'),
Decimal('10000'), 'end'))
assert_equal(
npf.rate(Decimal('10'), Decimal('20'), Decimal('-3500'),
Decimal('10000'), Decimal('0')),
npf.rate(Decimal('10'), Decimal('20'), Decimal('-3500'),
Decimal('10000'), 'end'))
# begin
assert_equal(
npf.pv(Decimal('0.07'), Decimal('20'), Decimal('12000'),
Decimal('0'), Decimal('1')),
npf.pv(Decimal('0.07'), Decimal('20'), Decimal('12000'),
Decimal('0'), 'begin'))
# end
assert_equal(
npf.pv(Decimal('0.07'), Decimal('20'), Decimal('12000'),
Decimal('0')),
npf.pv(Decimal('0.07'), Decimal('20'), Decimal('12000'),
Decimal('0'), 'end'))
assert_equal(
npf.pv(Decimal('0.07'), Decimal('20'), Decimal('12000'),
Decimal('0'), Decimal('0')),
npf.pv(Decimal('0.07'), Decimal('20'), Decimal('12000'),
Decimal('0'), 'end'))
def get_pv(self):
return fin.pv(self.rate, self.n, self.pmt, self.fv, self.when)[0]
def bond_price(face_value, rate, nper):
return npf.pv(rate=rate, nper=nper, fv=face_value, pmt=0) * -1
print()
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':
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
fv = 0
per = 240
pmt = npf.pmt(rate=rate / 12, nper=nper * 12, pv=pv, fv=fv)
# rate：年化投资回报率
def test_pv_decimal(self):
assert_equal(
npf.pv(Decimal('0.07'), Decimal('20'), Decimal('12000'),
Decimal('0')), Decimal('-127128.1709461939327295222005'))
def test_pv(self):
assert_almost_equal(npf.pv(0.07, 20, 12000, 0), -127128.17, 2)
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
npf.fv(0.06, 3, -100, 0)
npf.fv(0.06, 3, -100, 0, when='begin')
def test_decimal_with_when(self):
"""
Test that decimals are still supported if the when argument is passed
"""
# begin
assert_equal(npf.rate(Decimal('10'), Decimal('20'), Decimal('-3500'),
Decimal('10000'), Decimal('1')),
npf.rate(Decimal('10'), Decimal('20'), Decimal('-3500'),
Decimal('10000'), 'begin'))
# end
assert_equal(npf.rate(Decimal('10'), Decimal('20'), Decimal('-3500'),
Decimal('10000')),
npf.rate(Decimal('10'), Decimal('20'), Decimal('-3500'),
Decimal('10000'), 'end'))
assert_equal(npf.rate(Decimal('10'), Decimal('20'), Decimal('-3500'),
Decimal('10000'), Decimal('0')),
npf.rate(Decimal('10'), Decimal('20'), Decimal('-3500'),
Decimal('10000'), 'end'))
# begin
assert_equal(npf.pv(Decimal('0.07'), Decimal('20'), Decimal('12000'),
Decimal('0'), Decimal('1')),
npf.pv(Decimal('0.07'), Decimal('20'), Decimal('12000'),
Decimal('0'), 'begin'))
# end
assert_equal(npf.pv(Decimal('0.07'), Decimal('20'), Decimal('12000'),
Decimal('0')),
npf.pv(Decimal('0.07'), Decimal('20'), Decimal('12000'),
Decimal('0'), 'end'))
assert_equal(npf.pv(Decimal('0.07'), Decimal('20'), Decimal('12000'),
Decimal('0'), Decimal('0')),
npf.pv(Decimal('0.07'), Decimal('20'), Decimal('12000'),
Decimal('0'), 'end'))
# begin
assert_equal(npf.pmt(Decimal('0.08') / Decimal('12'),
Decimal('5') * Decimal('12'), Decimal('15000.'),
Decimal('0'), Decimal('1')),
npf.pmt(Decimal('0.08') / Decimal('12'),
Decimal('5') * Decimal('12'), Decimal('15000.'),
Decimal('0'), 'begin'))
# end
assert_equal(npf.pmt(Decimal('0.08') / Decimal('12'),
Decimal('5') * Decimal('12'), Decimal('15000.'),
Decimal('0')),
npf.pmt(Decimal('0.08') / Decimal('12'),
Decimal('5') * Decimal('12'), Decimal('15000.'),
Decimal('0'), 'end'))
assert_equal(npf.pmt(Decimal('0.08') / Decimal('12'),
Decimal('5') * Decimal('12'), Decimal('15000.'),
Decimal('0'), Decimal('0')),
npf.pmt(Decimal('0.08') / Decimal('12'),
Decimal('5') * Decimal('12'), Decimal('15000.'),
Decimal('0'), 'end'))
# begin
assert_equal(npf.ppmt(Decimal('0.1') / Decimal('12'),
Decimal('1'), Decimal('60'), Decimal('55000'),
Decimal('0'), Decimal('1')),
npf.ppmt(Decimal('0.1') / Decimal('12'), Decimal('1'),
Decimal('60'), Decimal('55000'),
Decimal('0'), 'begin'))
# end
assert_equal(npf.ppmt(Decimal('0.1') / Decimal('12'), Decimal('1'),
Decimal('60'), Decimal('55000'), Decimal('0')),
npf.ppmt(Decimal('0.1') / Decimal('12'), Decimal('1'),
Decimal('60'), Decimal('55000'), Decimal('0'),
'end'))
assert_equal(npf.ppmt(Decimal('0.1') / Decimal('12'), Decimal('1'),
Decimal('60'), Decimal('55000'), Decimal('0'),
Decimal('0')),
npf.ppmt(Decimal('0.1') / Decimal('12'), Decimal('1'),
Decimal('60'), Decimal('55000'), Decimal('0'),
'end'))
# begin
assert_equal(npf.ipmt(Decimal('0.1') / Decimal('12'), Decimal('1'),
Decimal('24'), Decimal('2000'),
Decimal('0'), Decimal('1')).flat[0],
npf.ipmt(Decimal('0.1') / Decimal('12'), Decimal('1'),
Decimal('24'), Decimal('2000'),
Decimal('0'), 'begin').flat[0])
# end
assert_equal(npf.ipmt(Decimal('0.1') / Decimal('12'), Decimal('1'),
Decimal('24'), Decimal('2000'),
Decimal('0')).flat[0],
npf.ipmt(Decimal('0.1') / Decimal('12'), Decimal('1'),
Decimal('24'), Decimal('2000'),
Decimal('0'), 'end').flat[0])
assert_equal(npf.ipmt(Decimal('0.1') / Decimal('12'), Decimal('1'),
Decimal('24'), Decimal('2000'),
Decimal('0'), Decimal('0')).flat[0],
npf.ipmt(Decimal('0.1') / Decimal('12'), Decimal('1'),
Decimal('24'), Decimal('2000'),
Decimal('0'), 'end').flat[0])
def do_one_tick_calculation(company):
## ?? not working.
output = json.dumps(company, indent=4, sort_keys=False)
re_output = re.sub(r'",\s+', '", ', output)
print(re_output)
print(f"======= {company['name']} ======")
rev_growth = do_calc_growth_rate(company["rev_list"])
print("rev_growth: ", rev_growth)
grosspft_growth = do_calc_growth_rate(company["grosspft_list"])
print("grosspft_growth: ", grosspft_growth)
eps_growth = do_calc_growth_rate(company["eps_list"])
print("eps_growth: ", eps_growth)
bvps_growth = do_calc_growth_rate(company["bvps_list"])
print("bvps_growth: ", bvps_growth)
fcf_growth = do_calc_growth_rate(company["fcf_list"])
print("fcs_growth: ", fcf_growth)
# tencap calculation
net_income = company["net_income"]
income_tax = company["income_tax"]
depreciation = company["depreciation"]
receivable_chg = company["receivable_chg"]
payable_chg = company["payable_chg"]
capexp = company["capexp"]
sale_property = company["sale_property"]
share_num = company["share_num"]
close_price = company["close_price"]
owner_earnings = net_income + income_tax + depreciation + receivable_chg + \
payable_chg + capexp + sale_property
tencap_price = (owner_earnings*10)/share_num
tencap_price_fmt = "{:.2f}".format(tencap_price)
price_ratio = close_price/tencap_price
price_ratio_fmt = "{:.2f}".format(price_ratio)
print("===========")
print(f"{'*close_price: ' :<20} {close_price}")
print(f"{'*tencap_price:' :<20} {tencap_price_fmt :<10} [ {price_ratio_fmt} ]")
# pabriDcf
growth_rate = 0
growth_rate_name = ""
if (bvps_growth != 0):
growth_rate = bvps_growth
growth_rate_name = "bvps_growth"
elif (eps_growth != 0):
growth_rate = eps_growth
growth_rate_name = "eps_growth"
elif (grosspft_growth != 0):
growth_rate = grosspft_growth
growth_rate_name = "grosspft_growth"
elif (rev_growth != 0):
growth_rate = rev_growth
growth_rate_name = "rev_growth"
print(f"{'*growth_rate:' :<20} {growth_rate :<10} [{growth_rate_name}] ")
cash_from_operations = company["cash_from_operations"]
capexp = company["capexp"]
sale_property = company["sale_property"]
total_cash = company["total_cash"]
total_debt = company["total_debt"] *-1
share_num = company["share_num"]
fcf_y0 = cash_from_operations + capexp + sale_property
discount_rate = 0.1
fcf_y1 = fcf_y0 * (1 + growth_rate)
fcf_y2 = fcf_y1 * (1 + growth_rate)
fcf_y3 = fcf_y2 * (1 + growth_rate)
fcf_y4 = fcf_y3 * (1 + growth_rate)
fcf_y5 = fcf_y4 * (1 + growth_rate)
fcf_y6 = fcf_y5 * (1 + growth_rate)
fcf_y7 = fcf_y6 * (1 + growth_rate)
fcf_y8 = fcf_y7 * (1 + growth_rate)
fcf_y9 = fcf_y8 * (1 + growth_rate)
fcf_y10 = fcf_y9 * (1 + growth_rate)
fcf_y1_dcf = npf.pv(discount_rate, 1, 0, fcf_y1)*-1
fcf_y2_dcf = npf.pv(discount_rate, 2, 0, fcf_y2)*-1
fcf_y3_dcf = npf.pv(discount_rate, 3, 0, fcf_y3)*-1
fcf_y4_dcf = npf.pv(discount_rate, 4, 0, fcf_y4)*-1
fcf_y5_dcf = npf.pv(discount_rate, 5, 0, fcf_y5)*-1
fcf_y6_dcf = npf.pv(discount_rate, 6, 0, fcf_y6)*-1
fcf_y7_dcf = npf.pv(discount_rate, 7, 0, fcf_y7)*-1
fcf_y8_dcf = npf.pv(discount_rate, 8, 0, fcf_y8)*-1
fcf_y9_dcf = npf.pv(discount_rate, 9, 0, fcf_y9)*-1
fcf_y10_dcf = npf.pv(discount_rate, 10, 0, fcf_y10)*-1
termination_value = fcf_y10_dcf *10
current_npv = fcf_y1_dcf + fcf_y2_dcf + fcf_y3_dcf + fcf_y4_dcf + fcf_y5_dcf + \
fcf_y6_dcf + fcf_y7_dcf + fcf_y8_dcf + fcf_y9_dcf + fcf_y10_dcf + \
termination_value+ total_cash + total_debt
mydbg2(fcf_y1, fcf_y1_dcf)
mydbg2(fcf_y8, fcf_y8_dcf)
#print(current_npv)
intrinsic_value_per_share = current_npv/share_num
intrinsic_price_ratio = close_price/intrinsic_value_per_share
intrinsic_price_ratio_fmt = "{:.2f}".format(intrinsic_price_ratio)
pabridcf_price = intrinsic_value_per_share/2
pabridcf_price_ratio = close_price/pabridcf_price
pabridcf_price_ratio_fmt = "{:.2f}".format(pabridcf_price_ratio)
print(f"{'*pabDcf_price:' :<20} {round(pabridcf_price, 2):<10} [ {pabridcf_price_ratio_fmt} ]")
print(f"{'==pabIntrinsic:' :<20} {round(intrinsic_value_per_share, 2):<10} [ {intrinsic_price_ratio_fmt} ]" )
# payback (eight years)
payback_sum = fcf_y1 + fcf_y2 + fcf_y3 + fcf_y4 + fcf_y5 + \
fcf_y6 + fcf_y7 + fcf_y8
payback_price = payback_sum/share_num
payback_price_ratio = close_price/payback_price
payback_price_ratio_fmt = "{:.2f}".format(payback_price_ratio)
print(f"{'*payback_price:' :<20} {round(payback_price):<10} [ {payback_price_ratio_fmt} ]" )
import scipy as sp import numpy_financial as npf print(npf.pv(0.075,10,50,1000)) #numpy.pv(rate, nper, pmt, fv=0, when='end')
# Calculate the discount factor
discount_factor = 1 / ((1 + growth_rate)**(growth_periods))
print("Discount factor: " + str(round(discount_factor, 2)))
# Derive the initial value of the investment
initial_investment_again = 100 * (1 + -0.05)**10 * 1 / (1 + -0.05)**10
print("Initial value: " + str(round(initial_investment_again, 2)))
# Import numpy as np
import numpy as np
import numpy_financial as npf
# 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)) +
@author: USUARIO """ import pandas as pd import numpy as np import numpy_financial as npf import matplotlib.pyplot as plt #Valor Presente Valor Futuro i = 0.05 periodos = 10 pagamento = 0 vp = -10000 valor_presente = npf.pv(i, periodos, pagamento, valor_futuro) valor_presente valor_futuro = npf.fv(i, periodos, pagamento, vp) valor_futuro #Case 1 - Empresa investir em uma nova planta ativo = 100000000 passivo_oneroso = 40000000 patrimonio_liquido = 60000000 ki = 0.12 ke = 0.17 wacc = ((passivo_oneroso / ativo) * ki) + ((patrimonio_liquido / ativo) * ke) wacc
print(comp_int, " in interest growth")
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
#VER: https://cmdlinetips.com/2020/02/useful-personal-finance-functions-numpy-financial/
# https://numpy.org/numpy-financial/latest/
import numpy_financial as np
interestRate = 0.02 # Annual interest rate
compoundingFrequency = 2 # Twice an year
futureLumpSum = 100
presentValue = np.pv(interestRate / compoundingFrequency,
compoundingFrequency,
0,
futureLumpSum,
when='end')
print("Interest rate:%2.2f" % interestRate)
print("Compounding Frequency:%d" % compoundingFrequency)
print("Future value:%5.2f" % futureLumpSum)
print("Present value:%5.2f" % presentValue)
