Simple Monte Carlo Simulation of Stock Prices with Python

In this tutorial, we will go over Monte Carlo simulations and how to apply them to generate randomized future prices within Python.

My Website: http://programmingforfinance.com/

Code:
#------------------------------------------------------------------------------------#
import pandas_datareader.data as web
import pandas as pd
import datetime as dt
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style

style.use('ggplot')

start = dt.datetime(2017, 01, 03)
end = dt.datetime(2017, 11, 20)

prices = web.DataReader('AAPL', 'google', start, end)['Close']
returns = prices.pct_change()

last_price = prices[-1]

#Number of Simulations
num_simulations = 1000
num_days = 252

simulation_df = pd.DataFrame()

for x in range(num_simulations):
count = 0
daily_vol = returns.std()

price_series = []

price = last_price * (1 + np.random.normal(0, daily_vol))
price_series.append(price)

for y in range(num_days):
if count == 251:
break
price = price_series[count] * (1 + np.random.normal(0, daily_vol))
price_series.append(price)
count += 1

simulation_df[x] = price_series

fig = plt.figure()
fig.suptitle('Monte Carlo Simulation: AAPL')
plt.plot(simulation_df)
plt.axhline(y = last_price, color = 'r', linestyle = '-')
plt.xlabel('Day')
plt.ylabel('Price')
plt.show()
#------------------------------------------------------------------------------------#

Видео Simple Monte Carlo Simulation of Stock Prices with Python канала codebliss