Replicating a Black-Scholes-Merton call using simulation

After knowing the terminal prices, we can estimate the payoff for a call if the exercise price is given. The mean of those discounted payoffs using the risk-free rate as our discount rate will be our call price. The following code helps us estimate the call price:

import scipy as sp 
from scipy import zeros, sqrt, shape 
#
S0 = 40.              # stock price at time zero 
X= 40.                # exercise price 
T =0.5                # years 
r =0.05               # risk-free rate 
sigma = 0.2           # annualized volatility 
n_steps=100          # number of steps 
#
sp.random.seed(12345) # fix those random numbers 
n_simulation = 5000   # number of simulation 
dt =T/n_steps 
call = sp.zeros([n_simulation], dtype=float) 
x = range(0, int(n_steps), 1) 
for j in range(0, n_simulation): 
    sT=S0 
    for i in x[:-1]: 
        e=sp.random.normal() 
        sT*=sp.exp((r-0.5*sigma*sigma)*dt+sigma*e*sqrt(dt)) 
        call[j]=max(sT-X...