Market Sector Hierarchy Light

import matplotlib.pyplot as plt
import numpy as np
from scipy.cluster.hierarchy import dendrogram, linkage, set_link_color_palette

np.random.seed(456)

sectors = ['Technology', 'Healthcare', 'Financials', 'Energy', 'Materials',
           'Consumer Disc.', 'Consumer Staples', 'Industrials', 'Utilities',
           'Real Estate', 'Telecom', 'Basic Materials']

performance = np.random.rand(len(sectors), 6) * 100
Z = linkage(performance, method='complete')

fig, ax = plt.subplots(figsize=(13, 7), facecolor='#ffffff')
ax.set_facecolor('#ffffff')

set_link_color_palette(['#F5276C', '#6CF527', '#27D3F5', '#F5B027', '#5314E6'])

dn = dendrogram(Z, labels=sectors, leaf_rotation=35, leaf_font_size=11,
                color_threshold=0.6*max(Z[:,2]), above_threshold_color='#9ca3af', ax=ax)

ax.set_title('Market Sector Performance Clustering', fontsize=15, 
             color='#1f2937', fontweight='bold', pad=20)
ax.set_xlabel('Sector', fontsize=11, color='#374151')
ax.set_ylabel('Distance (Performance Similarity)', fontsize=11, color='#374151')

ax.tick_params(axis='both', colors='#374151', labelsize=10)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_color('#d1d5db')
ax.spines['bottom'].set_color('#d1d5db')
ax.yaxis.grid(True, color='#f9fafb', linewidth=0.8)

plt.tight_layout()
plt.show()