Portfolio Returns ANOVA Analysis

import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats

np.random.seed(456)

# Annual returns (%) for different strategies over 50 years simulation
conservative = np.random.normal(5.2, 3.5, 50)
balanced = np.random.normal(7.8, 6.2, 50)
aggressive = np.random.normal(10.5, 12.0, 50)
crypto_heavy = np.random.normal(15.0, 35.0, 50)

F_stat, p_value = stats.f_oneway(conservative, balanced, aggressive, crypto_heavy)

fig, ax = plt.subplots(figsize=(12, 7), facecolor='#0d1117')
ax.set_facecolor('#0d1117')

colors = ['#27F5B0', '#27D3F5', '#F5B027', '#F5276C']

parts = ax.violinplot([conservative, balanced, aggressive, crypto_heavy], 
                       positions=[1, 2, 3, 4], showmeans=True, showmedians=True, widths=0.8)

for i, pc in enumerate(parts['bodies']):
    pc.set_facecolor(colors[i])
    pc.set_alpha(0.6)
    pc.set_edgecolor(colors[i])
    pc.set_linewidth(2)

parts['cmeans'].set_color('#F5D327')
parts['cmeans'].set_linewidth(2)
parts['cmedians'].set_color('white')
for partname in ['cbars', 'cmins', 'cmaxes']:
    parts[partname].set_color('#444444')

# Risk-adjusted stats
sharpe_ratios = [1.49, 1.26, 0.88, 0.43]
labels = ['Conservative', 'Balanced', 'Aggressive', 'Crypto-Heavy']

# Annotate Sharpe ratios at bottom
for i, (sr, color) in enumerate(zip(sharpe_ratios, colors)):
    ax.text(i+1, -55, f'Sharpe: {sr:.2f}', ha='center', fontsize=9, color=color, fontweight='bold')

# Stats annotation - bottom right corner
stats_box = f"ANOVA: F={F_stat:.2f}, p={p_value:.4f}"
bbox = dict(boxstyle="round,pad=0.3", facecolor='#1a1a2e', edgecolor='#27D3F5', alpha=0.95)
ax.text(0.98, 0.02, stats_box, transform=ax.transAxes, fontsize=10, color='white',
        ha='right', va='bottom', fontfamily='monospace', bbox=bbox)

ax.axhline(y=0, color='#ef4444', linestyle='--', alpha=0.5, linewidth=1)
ax.text(0.5, 2, 'Break-even', fontsize=9, color='#ef4444', va='bottom')

ax.set_xticks([1, 2, 3, 4])
ax.set_xticklabels(labels, fontsize=11, color='white')
ax.set_ylabel('Annual Return (%)', fontsize=12, color='white', fontweight='500')
ax.set_title('Investment Strategy Performance Comparison\n50-Year Monte Carlo Simulation', 
             fontsize=14, color='white', fontweight='bold', pad=15)

ax.tick_params(colors='#888888')
for spine in ax.spines.values():
    spine.set_color('#333333')
ax.yaxis.grid(True, color='#1e293b', linewidth=0.5)
ax.set_axisbelow(True)
ax.set_ylim(-65, 90)

plt.tight_layout()
plt.show()