I'm trying to replicate the results of "identifying support and resistance " blog on my local machine , but it keeps on giving, name error sayiing "line_mse is not defined"
Hi Mphahlela!
To fix this, you can first run the following code snippet:
# The line_mse() function computes and returns the mean squared error for
# the distance of all points from the line passing through points pt1 and pt2.
norm = np.linalg.norm
# Function takes two points pt1, pt2 and the minima/maxima dataset as input.
def line_mse(pt1, pt2, data):
dist = 0
dist_sq = 0
sum_dist_sq = 0
mean_sq_err = 0
length = len(data)
for pt3 in range(0, length):
p1 = data[pt1]
p2 = data[pt2]
p3 = data[pt3]
# To calculate perpendicular distance of a point from line passing through pt1 and pt2
dist = np.abs(norm(np.cross(p2-p1, p1-p3)))/norm(p2-p1)
# Squared error of a point from line passing through pt1 and pt2
dist_sq = dist**2
# Sum of squared errors of all points from line passing through pt1 and pt2
sum_dist_sq = sum_dist_sq + dist_sq
# Mean squared error formula applied
mean_sq_err = sum_dist_sq / length
return (mean_sq_err)Now that the 'line_mse' function is defined, you will be able to run the below lines of code without any error:
# The s_r_lines() function identifies and returns all potential support and
# resistance line coordinates along with its corresponding mean squared error value.
def s_r_lines(data):
lines = []
for pt1 in range(0, len(data) - 1):
for pt2 in range(0, len(data)):
if pt1 != pt2:
mse_val = line_mse(pt1, pt2, data)
lines.append((data[pt1], data[pt2], mse_val))
return lines
# Compute and store data of potential support and resistance lines
support_lines = s_r_lines(minima_pts)
resistance_lines = s_r_lines(maxima_pts)Thank you for letting us know about this as well.
To avoid any confusion, we will soon update the order of these two code snippets on the blog.
I hope you found the blog useful. Thanks.