The following are the actions that we will take:
- Read the data into arrays.
- Calculate VWAP:
from __future__ import print_function import numpy as np c,v=np.loadtxt('data.csv', delimiter=',', usecols=(6,7), unpack=True) vwap = np.average(c, weights=v) print("VWAP =", vwap)The output is as follows:
VWAP = 350.589549353
That wasn't very hard, was it? We just called the average() function and set its weights parameter to use the v array for weights. By the way, NumPy also has a function to calculate the arithmetic mean. This is an unweighted average with all the weights equal to 1.
The mean() function is quite friendly and not so mean. This function calculates the arithmetic mean of an array.
Note
The arithmetic mean is given by the following formula:
It sums the values in an array a and divides the sum by the number of elements n (see https://www.khanacademy.org/math/probability/descriptive-statistics/central_tendency/e/mean_median_and_mode).
print("mean =", np.mean(c))As a result, we get the following printout:
mean = 351.037666667
In finance, time-weighted average price (TWAP) is another average price measure. Now that we are at it, let's compute the TWAP too. It is just a variation on a theme really. The idea is that recent price quotes are more important, so we should give recent prices higher weights. The easiest way is to create an array with the arange() function of increasing values from zero to the number of elements in the close price array. This is not necessarily the correct way. In fact, most of the examples concerning stock price analysis in this book are only illustrative. The following is the TWAP code:
t = np.arange(len(c))
print("twap =", np.average(c, weights=t))It produces the following output:
twap = 352.428321839
The TWAP is even higher than the mean.