Market Returns by Sector

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

np.random.seed(456)

sectors = ['Tech', 'Healthcare', 'Finance', 'Energy']
data = []
for sector in sectors:
    n = 45
    volatility = np.random.uniform(5, 35, n)
    betas = {'Tech': 0.35, 'Healthcare': 0.25, 'Finance': 0.28, 'Energy': 0.22}
    returns = betas[sector] * volatility + np.random.normal(0, 3, n)
    for v, r in zip(volatility, returns):
        data.append({'Volatility (%)': v, 'Annual Return (%)': r, 'Sector': sector})

df = pd.DataFrame(data)

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

palette = ['#27D3F5', '#6CF527', '#F5B027', '#F54927']

g = sns.lmplot(
    data=df,
    x='Volatility (%)',
    y='Annual Return (%)',
    hue='Sector',
    col='Sector',
    col_wrap=2,
    height=3.5,
    aspect=1.2,
    palette=palette,
    scatter_kws={'alpha': 0.7, 's': 55, 'edgecolor': 'white', 'linewidths': 0.5},
    line_kws={'linewidth': 2.5},
    ci=95
)

g.fig.set_facecolor('#0a0a0f')
for ax in g.axes.flat:
    ax.set_facecolor('#0a0a0f')
    for spine in ax.spines.values():
        spine.set_color('#333333')

g.fig.suptitle('Risk-Return by Industry', fontsize=14, fontweight='bold', color='white', y=1.02)
plt.tight_layout()
plt.show()