Maybe try one of these, possibly can be improved, the first is a little more dramatish.

import statsmodels.api as sm

def slope_calc2(in_list):  
    if len(in_list) == 1:        return 0  
    n       = len(in_list)  # like 10  
    sum_x   = 0;     sum_y   = 0;     sum_x_y = 0;     sum_x_2 = 0  
    # Collect sums  
    for i in range(n):      # 0-based, 0-9 for list of length 10  
        x        = i + 1    # 1-based  
        y        = float(in_list[i])  
        sum_x   += x; sum_y   += y; sum_x_y += x * y; sum_x_2 += x**2

    # Calculate slope  
    that = ( n * sum_x_2 - (sum_x**2) )  
    if that != 0:        slope = ((n * sum_x_y) - (sum_x * sum_y)) / that  
    else:        slope = 0

    return slope  # pretend these are like angles

def slope_calc(in_list):  
    time = sm.add_constant(range(-len(in_list) + 1, 1))  
    return sm.OLS(in_list, time).fit().params[-1]  # slope  

And if you want to know which way a set of points are curving. Positive is counterclockwise and again one offers more extreme values.

def curve_calc(in_list):  
    vals = []  
    for i in range(len(in_list) - 1):  
        vals.append(in_list[i+1] - in_list[i])  
    return np.mean(np.array(vals))

def curve_calc2(in_list):  
    vals = []  
    vals2 = []  
    for i in range(len(in_list) - 1):  
        vals.append(in_list[i+1] - in_list[i])  
    for i in range(len(vals) - 1):  
        vals2.append(vals[i+1] - vals[i])  
    return np.mean(np.array(vals2))  

thanks!