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:

3. This will form the basis of the X array we will feed our model. But before we're ready for that, there are a few additional steps.
4. First, we'll reverse our columns so that time runs from left to right:

sp20 = sp20.iloc[:,::-1] 
 
sp20 

This generates the following output:

5. Now, let's import our support vector machine and set our our training and test matrices and vectors:

from sklearn.svm import SVR 
clf = SVR(kernel='linear') 
 
X_train = sp20[:-2000] 
y_train = sp20['Close'].shift(-1)[:-2000] 
 
X_test = sp20[-2000:] 
y_test = sp20['Close'].shift(-1)[-2000:] 

6. We had just 5,000 data points to work with, so I chose to use the last 2,000 for testing. Let's now fit our model and use it to check out-of-sample data:

model = clf.fit(X_train, y_train) 
 
preds = model.predict(X_test) 

7. Now that we have our predictions, let's compare them to our actual data:

tf = pd.DataFrame(list(zip(y_test, preds)), columns=['Next Day Close', 'Predicted Next Close'], index=y_test.index) 
 
tf 

The preceding code generates the following output: