We have already imported pandas as pd, choice and seed from numpy.random, seaborn as sns, and matplotlib.pyplot as plt. We have also imported the FB stock price series since IPO in May 2012 as the variable fb. Inspect this using .head().

Set seed to 42.
Apply .pct_change() to generate daily Facebook returns, drop missing values, and assign to daily_returns.
Create a variable n_obs that contains the .count() of Facebook daily_returns.
Use choice() to randomly select n_obs samples from daily_returns, and assign to random_walk.
Convert random_walk to a pd.Series and reassign it to itself.
Use sns.distplot() to plot the distribution of random_walk.

Case study: S&P500 price simulation

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