Financial Portfolio Metrics

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

np.random.seed(123)

n = 180
asset_classes = np.random.choice(['Equity', 'Bond', 'Commodity'], n)
data = {
    'Return (%)': np.where(asset_classes == 'Equity', np.random.normal(12, 8, n),
                          np.where(asset_classes == 'Bond', np.random.normal(4, 2, n), np.random.normal(8, 12, n))),
    'Volatility': np.where(asset_classes == 'Equity', np.random.normal(18, 5, n),
                          np.where(asset_classes == 'Bond', np.random.normal(5, 1.5, n), np.random.normal(22, 8, n))),
    'Sharpe': np.random.normal(0.8, 0.4, n),
    'Beta': np.where(asset_classes == 'Equity', np.random.normal(1, 0.3, n),
                    np.where(asset_classes == 'Bond', np.random.normal(0.2, 0.1, n), np.random.normal(0.5, 0.2, n))),
    'Asset': asset_classes
}
df = pd.DataFrame(data)

plt.style.use('dark_background')
sns.set_style("darkgrid", {'axes.facecolor': '#0a0a0f', 'figure.facecolor': '#0a0a0f', 'grid.color': '#333333'})

palette = {'Equity': '#27D3F5', 'Bond': '#6CF527', 'Commodity': '#F5B027'}

g = sns.pairplot(
    df,
    hue='Asset',
    palette=palette,
    height=2,
    kind='reg',
    diag_kind='kde',
    markers=['o', '^', 's'],
    plot_kws={'scatter_kws': {'alpha': 0.6, 's': 45, 'edgecolor': 'white', 'linewidths': 0.4}, 'line_kws': {'linewidth': 2}},
    diag_kws={'alpha': 0.5, 'linewidth': 2.5}
)

g.fig.set_facecolor('#0a0a0f')
for ax in g.axes.flat:
    if ax:
        ax.set_facecolor('#0a0a0f')
        for spine in ax.spines.values():
            spine.set_color('#333333')
        ax.tick_params(colors='#888888')
        ax.xaxis.label.set_color('white')
        ax.yaxis.label.set_color('white')

g.fig.suptitle('Portfolio Risk-Return Analysis', fontsize=14, fontweight='bold', color='white', y=1.02)
plt.setp(g._legend.get_title(), color='white')
plt.setp(g._legend.get_texts(), color='white')

plt.tight_layout()
plt.show()