Time for action – balancing volume

In other words we need to multiply the sign of the close price with the volume. In this tutorial, we will go over two approaches to this problem, one using the NumPy sign function, and the other using the NumPy piecewise function.

  1. Load the BHP data into a close and volume array:
    c, v=np.loadtxt('BHP.csv', delimiter=',', usecols=(6, 7), unpack=True)

    Compute the absolute value changes. Calculate the change of the close price with the diff function. The diff function computes the difference between two sequential array elements and returns an array containing these differences:

    change = np.diff(c)
print "Change", change

    The changes of the close price are shown as follows:

    Change [ 1.92 -1.08 -1.26  0.63 -1.54 -0.28  0.25 -0.6   2.15 0.69 -1.33  1.16
  1.59 -0.26 -1.29 -0.13 -2.12 -3.91  1.28 -0.57 -2.07 -2.07 2.5   1.18
-0.88  1.31  1.24 -0.59]
  2. The NumPy sign function returns the signs for each element in an array. -1 is returned for a negative number, 1 for a positive number, and 0, otherwise. Apply the sign function to the change array:
    signs = np.sign(change)
print "Signs", signs

    The signs of the change array are as follows:

    Signs [ 1. -1. -1.  1. -1. -1.  1. -1.  1.  1. -1.  1.  1. -1. -1. -1. -1. -1.
-1. -1. -1.  1.  1.  1. -1.  1.  1. -1.]

    Alternatively, we can calculate the signs with the piecewise function. The piecewise function, as its name suggests, evaluates a function piece-by-piece. Call the function with the appropriate return values and conditions:

    pieces = np.piecewise(change, [change < 0, change > 0], [-1, 1])
print "Pieces", pieces

    The signs are shown again, as follows:

    Pieces [ 1. -1. -1.  1. -1. -1.  1. -1.  1.  1. -1.  1.  1. -1. -1. -1. -1. -1.
-1. -1. -1.  1.  1.  1. -1.  1.  1. -1.]

    Check that the outcome is the same:

    print "Arrays equal?", np.array_equal(signs, pieces)

    And the outcome is as follows:

    Arrays equal? True
  3. The on-balance volume depends on the change of the previous close, so we cannot calculate it for the first day in our sample:
    print "On balance volume", v[1:] * signs

    The on-balance volume is as follows:

    [ 2620800. -2461300. -3270900.  2650200. -4667300. -5359800. 7768400.
 -4799100.  3448300.  4719800. -3898900.  3727700.  3379400. -2463900.
 -3590900. -3805000. -3271700. -5507800.  2996800. -3434800. -5008300.
 -7809799.  3947100.  3809700.  3098200. -3500200.  4285600. 3918800.
 -3632200.]

What just happened?

We computed the on-balance volume that depends on the change of the closing price. Using the NumPy sign and piecewise functions, we went over two different methods to determine the sign of the change (see obv.py):

import numpy as np

c, v=np.loadtxt('BHP.csv', delimiter=',', usecols=(6, 7), unpack=True)

change = np.diff(c)
print "Change", change

signs = np.sign(change)
print "Signs", signs

pieces = np.piecewise(change, [change < 0, change > 0], [-1, 1])
print "Pieces", pieces

print "Arrays equal?", np.array_equal(signs, pieces)

print "On balance volume", v[1:] * signs
..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset