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.
This is a part of the course
“Time Series Analysis in Python”
Exercise instructions
- Generate 500 random normal "steps" with mean=0 and standard deviation=1 using
np.random.normal(), where the argument for the mean islocand the argument for the standard deviation isscale. - Simulate stock prices
P:- Cumulate the random
stepsusing the numpy.cumsum()method - Add 100 to
Pto get a starting stock price of 100.
- Cumulate the random
- Plot the simulated random walk
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Generate 500 random steps with mean=0 and standard deviation=1
steps = np.random.normal(loc=___, scale=___, size=___)
# Set first element to 0 so that the first price will be the starting stock price
steps[0]=0
# Simulate stock prices, P with a starting price of 100
P = ___ + np.cumsum(___)
# Plot the simulated stock prices
plt.plot(___)
plt.title("Simulated Random Walk")
plt.show()