Random walk III

In this exercise, you'll complete your random walk simulation using Facebook stock returns over the last five years. You'll start off with a random sample of returns like the one you've generated during the last exercise and use it to create a random stock price path.

This exercise is part of the course

Manipulating Time Series Data in Python

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Exercise instructions

We have already imported pandas as pd, choice and seed from numpy.random, and matplotlib.pyplot as plt. We have loaded the Facebook price as a pd.DataFrame in the variable fb and a random sample of daily FB returns as pd.Series in the variable random_walk.

Select the first Facebook price by applying .first('D') to fb.price, and assign to start.
Add 1 to random_walk and reassign it to itself, then .append() random_walk to start and assign this to random_price.
Apply .cumprod() to random_price and reassign it to itself.
Insert random_price as new column labeled random into fb and plot the result.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Select fb start price here
start = ____

# Add 1 to random walk and append to start
random_walk = ____
random_price = ____

# Calculate cumulative product here
random_price = ____

# Insert into fb and plot
fb['random'] = ____

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