Building the regression model

Now that we have a baseline to compare with, let's build our first regression model. We're going to start with a very basic model using only the stock's prior closing values to predict the next day's close, and we're going to build it using a support vector regression. With that, let's set up our model:

1. The first step is to set up a DataFrame that contains a price history for each day. We're going to include the past 20 closes in our model:

for i in range(1, 21, 1): 
    sp.loc[:,'Close Minus ' + str(i)] = sp['Close'].shift(i) 
 
sp20 = sp[[x for x in sp.columns if 'Close Minus' in x or x == 'Close']].iloc[20:,] 
 
sp20 

2. This code gives us each day's closing price, along with the previous 20, all on the same line. The result of our code is seen in the following output...