I have the

rs = np.random.RandomState(0)
x = 50 * rs.rand(20)   50
y = 200 * rs.rand(20)
plt.plot(x, y, 'o')

# save ax and fig scales
xmin, xmax = plt.xlim()
ymin, ymax = plt.ylim()
xfig, yfig = plt.gcf().get_size_inches()

k, n, r, _, _ = scipy.stats.linregress(x, y)
plt.axline((0, n), slope=k, color='blue')

# restore x and y limits after axline
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)

# find text coordinates at midpoint of regression line
xtext = (xmin   xmax) // 2
ytext = k*xtext   n

# find run and rise of (xtext, ytext) vs (0, n)
dx = xtext
dy = ytext - n

# normalize to ax and fig scales
xnorm = dx * xfig / (xmax - xmin)
ynorm = dy * yfig / (ymax - ymin)

# find normalized annotation angle in radians
rotation = np.rad2deg(np.arctan2(ynorm, xnorm))

plt.annotate(
    f'$R^2={r**2:.3f}$',
    (xtext, ytext), xycoords='data',
    ha='center', va='bottom',
    rotation=rotation, rotation_mode='anchor',
)