Time for action – balancing volume

In other words, we need to multiply the sign of the close price and the volume. In this section, we look at 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 closing 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) as follows:

from __future__ import print_function
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)
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