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() #------------------------------------------------------------------------------------#