Stock Volatility Calendar

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.patches as mpatches

np.random.seed(205)

days = 365
# Simulate VIX-like volatility
volatility = np.random.exponential(15, days)
# Add some spikes for market events
spike_days = np.random.choice(days, size=20, replace=False)
volatility[spike_days] *= 2.5
volatility = np.clip(volatility, 5, 60)
# Weekends no trading
for i in range(days):
    if i % 7 >= 5:
        volatility[i] = 0

vol_levels = np.digitize(volatility, bins=[0, 12, 18, 25, 35, 50]) - 1

weeks = 53
data = np.zeros((7, weeks))
for i, val in enumerate(vol_levels):
    week = i // 7
    day = i % 7
    if week < weeks:
        data[day, week] = val

# Temperature palette
colors = ['#1e3a8a', '#3b82f6', '#22d3ee', '#fbbf24', '#f97316', '#dc2626']
cmap = LinearSegmentedColormap.from_list('volatility', colors, N=256)

fig, ax = plt.subplots(figsize=(16, 4), facecolor='#0a0a0f')
ax.set_facecolor('#0a0a0f')

im = ax.imshow(data, cmap=cmap, aspect='auto', vmin=0, vmax=5)

ax.set_yticks(range(7))
ax.set_yticklabels(['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'], fontsize=9, color='#e2e8f0')
ax.set_xticks(range(0, 52, 4))
ax.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec', ''], 
                   fontsize=9, color='#e2e8f0')

ax.set_title('Market Volatility Index - Daily VIX Levels', fontsize=16, color='white', fontweight='bold', pad=15)

for i in range(8):
    ax.axhline(y=i-0.5, color='#1e293b', linewidth=0.5)
for i in range(weeks+1):
    ax.axvline(x=i-0.5, color='#1e293b', linewidth=0.5)

ax.tick_params(colors='#e2e8f0', length=0)
for spine in ax.spines.values():
    spine.set_visible(False)

legend_elements = [mpatches.Patch(facecolor=c, label=l, edgecolor='#334155') 
                   for c, l in zip(colors, ['Low <12', 'Normal', 'Elevated', 'High', 'V.High', 'Extreme'])]
ax.legend(handles=legend_elements, loc='upper left', bbox_to_anchor=(0, -0.15), ncol=6, 
          fontsize=8, facecolor='#1e293b', edgecolor='#334155', labelcolor='white')

high_vol_days = int(np.sum(volatility >= 25))
ax.annotate(f'{high_vol_days} high volatility trading days', xy=(0.98, 1.1), xycoords='axes fraction',
            fontsize=11, color='#dc2626', ha='right', fontweight='bold')

plt.tight_layout()
plt.subplots_adjust(bottom=0.25)
plt.show()