Plot a return histogram
As a trader, it is important to analyze an asset's return profile, such as ranges of price changes, return distributions, etc. Over the years, Tesla fans and short sellers have been either betting on or against Tesla stocks aggressively, leading to volatile stock prices. You have some Tesla historical daily price data and want to verify this phenomenon.
The stock data has been preloaded in tsla_data, and matplotlib.pyplot has been imported as plt. Additional customizations to the plot such as a title and axis labels have already been provided for you.
This exercise is part of the course
Financial Trading in Python
Exercise instructions
- Calculate the daily percent change using the
Closeprice and save it in a new column nameddaily_return. - Plot a histogram of
daily_return, setting the bin size to 100.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate daily returns
tsla_data['daily_return'] = tsla_data['____'].____() * 100
# Plot the histogram
tsla_data['____'].____(____, color='red')
plt.ylabel('Frequency')
plt.xlabel('Daily return')
plt.title('Daily return histogram')
plt.show()