def pricer(self, S0, K, r, T, N, sigma, is_call=True, div=0., is_eu=False):
model = TrinomialLattice(S0, K, r, T, N,
{"sigma": sigma,
"div": div,
"is_call": is_call,
"is_eu": is_eu})
return model.price()
def pricer(self, S0, K, r, T, N, sigma,
is_call=True, div=0., is_eu=False):
model = TrinomialLattice(S0, K, r, T, N,
{"sigma": sigma,
"div": div,
"is_call": is_call,
"is_eu": is_eu})
return model.price()
for i in range(self.M)[1:]:
self.STs[i] = self.STs[i - 1] * self.d
def _initialize_payoffs_tree_(self):
return np.maximum(0, (self.STs - self.K) if self.is_call else
(self.K - self.STs))
def __check_early_exercise__(self, payoffs, node):
self.STs = self.STs[1:-1] # Shorten the ends of the list
early_ex_payoffs = \
(self.STs-self.K) if self.is_call \
else(self.K-self.STs)
payoffs = np.maximum(payoffs, early_ex_payoffs)
return payoffs
if __name__ == "__main__":
from TrinomialLattice import TrinomialLattice
eu_option = TrinomialLattice(50, 50, 0.05, 0.5, 2, {
"sigma": 0.3,
"is_call": False
})
print("European put: %s" % eu_option.price())
am_option = TrinomialLattice(50, 50, 0.05, 0.5, 2, {
"sigma": 0.3,
"is_call": False,
"is_eu": False
})
print("American put: %s" % am_option.price())
self.STs[0] = self.S0 * self.u**self.N
for i in range(self.M)[1:]:
self.STs[i] = self.STs[i-1]*self.d
def _initialize_payoffs_tree_(self):
return np.maximum(
0, (self.STs-self.K) if self.is_call
else(self.K-self.STs))
def __check_early_exercise__(self, payoffs, node):
self.STs = self.STs[1:-1] # Shorten the ends of the list
early_ex_payoffs = \
(self.STs-self.K) if self.is_call \
else(self.K-self.STs)
payoffs = np.maximum(payoffs, early_ex_payoffs)
return payoffs
if __name__ == "__main__":
from TrinomialLattice import TrinomialLattice
eu_option = TrinomialLattice(
50, 50, 0.05, 0.5, 2,
{"sigma": 0.3, "is_call":False})
print ("European put: %s" % eu_option.price())
am_option = TrinomialLattice(
50, 50, 0.05, 0.5, 2,
{"sigma": 0.3, "is_call": False, "is_eu": False})
print ("American put: %s" % am_option.price())