Stock Trading Calendar

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

np.random.seed(136)

days = 365
trades = np.zeros(days)
for i in range(days):
    if i % 7 < 5:
        trades[i] = np.random.poisson(3)
trades = np.clip(trades, 0, 15)

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

# CLAUDE.md Neon Mint palette
colors_neon = ['#ffffff', '#e0fcf0', '#7df5cd', '#27F5B0']
cmap = LinearSegmentedColormap.from_list('neon_mint', colors_neon, N=256)

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

im = ax.imshow(data, cmap=cmap, aspect='auto', vmin=0, vmax=15)

ax.set_yticks(range(7))
ax.set_yticklabels(['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'], fontsize=9, color='#374151')
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='#374151')

ax.set_title('Stock Trading - Trades Per Day', fontsize=16, color='#1f2937', fontweight='bold', pad=15)

for i in range(8):
    ax.axhline(y=i-0.5, color='#e5e7eb', linewidth=0.5)
for i in range(weeks+1):
    ax.axvline(x=i-0.5, color='#e5e7eb', linewidth=0.5)

ax.tick_params(colors='#374151', length=0)
for spine in ax.spines.values():
    spine.set_visible(False)

legend_elements = [mpatches.Patch(facecolor=c, label=l, edgecolor='#d1d5db') 
                   for c, l in zip(colors_neon, ['0', '1-4', '5-10', '11+'])]
ax.legend(handles=legend_elements, loc='upper left', bbox_to_anchor=(0, -0.15), ncol=4, 
          fontsize=8, facecolor='#f9fafb', edgecolor='#d1d5db', labelcolor='#374151', title='Trades',
          title_fontsize=9)

total = int(np.sum(trades))
ax.annotate(f'{total} trades executed', xy=(0.98, 1.1), xycoords='axes fraction',
            fontsize=11, color='#27F5B0', ha='right', fontweight='bold')

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