Time for action – predicting price with a linear model

Keeping an open mind, let's assume that we can express a stock price as a linear combination of previous values, that is, a sum of those values multiplied by certain coefficients we need to determine. In linear algebra terms, this boils down to finding a least squares solution. This recipe goes as follows.

1. First, form a vector bbx containing N price values.

   bbx = c[-N:]
   bbx = b[::-1]
   print "bbx", x

   The result is as follows:

   bbx [ 351.99  346.67  352.47  355.76  355.36]

2. Second, pre-initialize the matrix A to be N x N and containing zeroes.

   A = np.zeros((N, N), float)
   print "Zeros N by N", A
   Zeros N by N [[ 0.  0.  0.  0.  0.]
    [ 0.  0.  0.  0.  0.]
    [ 0.  0.  0.  0.  0.]
    [ 0.  0.  0.  0.  0.]
    [ 0.  0.  0.  0.  0.]]

3. Third, fill the matrix A with N preceding price values for each value in bbx.

   for i in range(N):
      A[i, ] = c[-N - 1 - i: - 1 - i]
   print "A", A

   Now, A looks like this:

   A [[ 360.    355.36  355.76  352.47  346.67]
    [ 359.56  360. ...