Financial Asset Correlations

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from matplotlib.colors import LinearSegmentedColormap

np.random.seed(42)

# Simulated asset returns correlation
assets = ['SPY', 'QQQ', 'IWM', 'TLT', 'GLD', 'USO', 'VNQ', 'EEM', 'FXE', 'BTC']
n = len(assets)

# Create realistic correlation structure
corr = np.eye(n)
# Equity correlations (high)
for i in range(4):
    for j in range(i+1, 4):
        c = 0.6 + np.random.rand() * 0.3
        corr[i, j] = corr[j, i] = c

# Negative bond correlation with equities
for i in range(4):
    corr[i, 3] = corr[3, i] = -0.2 - np.random.rand() * 0.2

# Other correlations
for i in range(n):
    for j in range(i+1, n):
        if corr[i, j] == 0:
            corr[i, j] = corr[j, i] = np.random.rand() * 0.4 - 0.2

df = pd.DataFrame(corr, index=assets, columns=assets)

# Asset class colors
classes = ['Equity']*3 + ['Bond'] + ['Commodity']*2 + ['REIT', 'Equity', 'Currency', 'Crypto']
class_palette = {'Equity': '#27D3F5', 'Bond': '#6CF527', 'Commodity': '#F5B027', 
                 'REIT': '#F5276C', 'Currency': '#4927F5', 'Crypto': '#F527B0'}
colors = pd.Series(classes, index=assets).map(class_palette)

# Diverging NEON colormap
neon_div = LinearSegmentedColormap.from_list('neon_div', ['#27D3F5', '#0a0a0f', '#F5276C'])

g = sns.clustermap(df, cmap=neon_div, center=0, vmin=-1, vmax=1,
                   row_colors=colors, col_colors=colors,
                   method='average', linewidths=0.5, linecolor='#1a1a2e',
                   figsize=(8, 7), dendrogram_ratio=(0.12, 0.12),
                   cbar_pos=(0.01, 0.08, 0.008, 0.12),
                   tree_kws={'linewidths': 1.5, 'colors': '#27D3F5'})

g.fig.patch.set_facecolor('#0a0a0f')
g.ax_heatmap.set_facecolor('#0a0a0f')
g.ax_heatmap.tick_params(colors='white', labelsize=9)

for ax in [g.ax_row_dendrogram, g.ax_col_dendrogram]:
    ax.set_facecolor('#0a0a0f')

g.fig.suptitle('Asset Correlation Matrix', color='white', fontsize=14, fontweight='bold', y=1.02)


plt.show()