def calc_σ(self, V):
try:
self.__verify(V)
except ValueError:
return np.NaN
σ = self.__σinit
n = 0
σdiff = 1
while σdiff >= self.__tol and n < self.__nmax:
bs = BlackScholes(self.__S, self.__K, self.__r, self.__T, σ,
self.__t, self.__q)
c = bs.call() if self.__type == 'C' else bs.put()
if c == V:
return σ
vega = self.__vega(bs.get_d1())
if vega == 0:
return np.NaN
increment = (c - V) / vega
σ -= increment
n += 1
σdiff = abs(increment)
return σ
def blackScholes():
data = request.json
strike = float(data['strike'])
stockPrice = float(data['stockPrice'])
volatility = float(data['volatility']) / 100
interestRate = float(data['interestRate']) / 100
repoRate = float(data['repoRate']) / 100
optionType = data['optionType']
time = float(data['time'])
blackScholes = BlackScholes(S=stockPrice,
K=strike,
r=interestRate,
T=time,
σ=volatility,
q=repoRate)
if (optionType == 'call'):
return str(round(blackScholes.call(), 2))
else:
return str(round(blackScholes.put(), 2))
def barrier_downandout(self, barrier):
sum = 0
bsm = BlackScholes(self.__S, self.__K, self.__r, self.__Δ, self.__σ)
for j in range(0, self.__m):
sT = self.__S
out = False
for i in range(0, int(self.__n)):
z = np.random.normal()
sT *= np.exp((self.__r - 0.5 * self.__σ * self.__σ) *
self.__dt + self.__σ * z * np.sqrt(self.__dt))
if sT < barrier:
out = True
if self.__option_type == 'call':
if not out:
sum += bsm.call()
if self.__option_type == 'put':
if not out:
sum += bsm.put()
return sum / self.__m
def barrier_upandin(self, barrier):
sum = 0
np.random.seed(51)
bsm = BlackScholes(self.__S, self.__K, self.__r, self.__Δ, self.__σ)
for j in range(0, self.__m):
sT = self.__S
inop = False
for i in range(0, int(self.__n)):
z = np.random.normal()
sT *= np.exp((self.__r - 0.5 * self.__σ * self.__σ) *
self.__dt + self.__σ * z * np.sqrt(self.__dt))
if sT > barrier:
inop = True
if self.__option_type == 'call':
if inop == True:
sum += bsm.call()
if self.__option_type == 'put':
if inop == True:
sum += bsm.put()
return sum / self.__m
