PyMPDATA_examples.Arabas_and_Farhat_2020.setup2_american_put 

 1import numpy as np
 2import PyMPDATA_examples.Arabas_and_Farhat_2020.Bjerksund_and_Stensland_1993 as BS93
 3import PyMPDATA_examples.Arabas_and_Farhat_2020.Black_Scholes_1973 as BS73
 4from pystrict import strict
 5
 6
 7@strict
 8class Settings:
 9    amer = True
10    l2_opt = 2.05
11    S_min = 0.05
12    S_max = 500
13    K = 100
14    r = 0.08
15    sigma = 0.2
16    n_iters = 2
17
18    def __init__(self, *, T: float, C_opt: float, S0: float):
19        self.T = T
20        self.C_opt = C_opt
21        self.S0 = S0
22        self.S_match = S0
23
24    def payoff(self, S: np.ndarray):
25        return np.exp(-self.r * self.T) * (np.maximum(0, self.K - S))
26
27    def analytical_solution(self, S: [np.ndarray, float], amer=True):
28        if not amer:
29            return BS73.p_euro(
30                S, K=self.K, T=self.T, r=self.r, b=self.r, sgma=self.sigma
31            )
32        return BS93.p_amer(S, K=self.K, T=self.T, r=self.r, b=self.r, sgma=self.sigma)
@strict
class Settings: 
 8@strict
 9class Settings:
10    amer = True
11    l2_opt = 2.05
12    S_min = 0.05
13    S_max = 500
14    K = 100
15    r = 0.08
16    sigma = 0.2
17    n_iters = 2
18
19    def __init__(self, *, T: float, C_opt: float, S0: float):
20        self.T = T
21        self.C_opt = C_opt
22        self.S0 = S0
23        self.S_match = S0
24
25    def payoff(self, S: np.ndarray):
26        return np.exp(-self.r * self.T) * (np.maximum(0, self.K - S))
27
28    def analytical_solution(self, S: [np.ndarray, float], amer=True):
29        if not amer:
30            return BS73.p_euro(
31                S, K=self.K, T=self.T, r=self.r, b=self.r, sgma=self.sigma
32            )
33        return BS93.p_amer(S, K=self.K, T=self.T, r=self.r, b=self.r, sgma=self.sigma)
Settings(*, T: float, C_opt: float, S0: float) 
19    def __init__(self, *, T: float, C_opt: float, S0: float):
20        self.T = T
21        self.C_opt = C_opt
22        self.S0 = S0
23        self.S_match = S0
amer = True
l2_opt = 2.05
S_min = 0.05
S_max = 500
K = 100
r = 0.08
sigma = 0.2
n_iters = 2
T
C_opt
S0
S_match
def payoff(self, S: numpy.ndarray): 
25    def payoff(self, S: np.ndarray):
26        return np.exp(-self.r * self.T) * (np.maximum(0, self.K - S))
def analytical_solution(self, S: [<class 'numpy.ndarray'>, <class 'float'>], amer=True): 
28    def analytical_solution(self, S: [np.ndarray, float], amer=True):
29        if not amer:
30            return BS73.p_euro(
31                S, K=self.K, T=self.T, r=self.r, b=self.r, sgma=self.sigma
32            )
33        return BS93.p_amer(S, K=self.K, T=self.T, r=self.r, b=self.r, sgma=self.sigma)