This gist is for MC simulation, at least the basics of it. Usage: Medium article

mc.py

import numpy as np
import pandas as pd
from pandas_datareader import data as wb
import matplotlib.pyplot as plt
from scipy.stats import norm

ticker = 'BA'
data = pd.DataFrame()
data[ticker] = wb.DataReader(ticker, data_source='yahoo', start='2015-1-1')['Adj Close']
plt.figure(figsize=(10,6))
plt.plot(data[ticker])
plt.show()

log_returns = np.log(1 + data.pct_change())
u = log_returns.mean()
var = log_returns.var()
drift = u - (0.5 * var)
stdev = log_returns.std()

t_intervals = 250
scenarios = 2

daily_returns = np.exp(drift.values + stdev.values * norm.ppf(np.random.rand(t_intervals, scenarios)))
price_list = np.zeros_like(daily_returns)
S0 = data.iloc[-1]
price_list[0] = S0

for t in range(1, t_intervals):
    price_list[t] = price_list[t - 1] * daily_returns[t]

plt.figure(figsize=(10,6))
plt.plot(price_list)
plt.show()