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  3. Time Series Analysis in Python

Exercise

Generate a Random Walk

Whereas stock returns are often modeled as white noise, stock prices closely follow a random walk. In other words, today's price is yesterday's price plus some random noise.

You will simulate the price of a stock over time that has a starting price of 100 and every day goes up or down by a random amount. Then, plot the simulated stock price. If you hit the "Run Code" code button multiple times, you'll see several realizations.

Instructions

100 XP
  • Generate 500 random normal "steps" with mean=0 and standard deviation=1 using np.random.normal(), where the argument for the mean is loc and the argument for the standard deviation is scale.
  • Simulate stock prices P:
    • Cumulate the random steps using the numpy .cumsum() method
    • Add 100 to P to get a starting stock price of 100.
  • Plot the simulated random walk