First, we need to determine the pivot points. We shall pretend they are equal to the arithmetic mean of the high, low, and close price:

h, l, c = np.loadtxt('data.csv', delimiter=',', usecols=(4, 5, 6), unpack=True)

pivots = (h + l + c) / 3
print("Pivots", pivots)

From the pivots, we can deduce the so-called resistance and support levels. The support level is the lowest level at which the price rebounds. The resistance level is the highest level at which the price bounces back. These are not natural phenomena, they are merely estimates. Based on these estimates, it is possible to draw support and resistance trend lines. We will define the daily spread to be the difference of the high and low price.

Define a function to fit line to data to a line where y = at + b. The function should return a and b. This is another opportunity to apply the lstsq() function of the NumPy linalg package. Rewrite the line equation...