text: import math
  from datetime import datetime, date
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  from pandas import DataFrame
  from sympy import *
  from scipy.stats import norm
  
  def blackscholes(r,S,K,t,sigma,Type):
      T=t/12
      d1=(np.log(S/K)+(r+sigma**2/2)*T)/(sigma*np.sqrt(T))
      d2=d1-sigma*np.sqrt(T)
      if Type == "c":
          price = S*norm.cdf(d1, 0, 1) - K*np.exp(-r*T)*norm.cdf(d2, 0, 1)
          delta = norm.cdf(d1, 0, 1)
          probability = norm.cdf(d2,0,1)
      if Type == "p":
          price = K*np.exp(-r*T)*norm.cdf(-d2, 0, 1) - S*norm.cdf(-d1, 0, 1)
          delta = -norm.cdf(-d1, 0, 1)
          probability = norm.cdf(-d2,0,1)
      gamma = norm.pdf(d1, 0, 1)/(S*sigma*np.sqrt(T))
      vega = S*norm.pdf(d1, 0, 1)*np.sqrt(T)
      return price, probability, delta, gamma, vega
  
  Type="c"
  S=300
  K=247
  r=0.02
  t=4
  sigma=0.18
  
  
  p,pb,d,g,v=blackscholes(r, S, K, t, sigma, Type)
  print("The price is " + str(p) + ".\n")
  print("The probability is " + str(pb) + ".\n")
  print("Delta is " + str(d) + ".\n")
  print("Gamma is " + str(g) + ".\n")
  print("Vega is " + str(v) + ".\n")
  