MakeoverMonday: S&P 500 Sector Performance

MakeoverMonday
Data Viz
Python
Finance
S&P500
Sectors
Visualizing S&P 500 sector ETF performance - Daily vs Year-to-Date returns
Author

chokotto

Published

February 3, 2026

Overview

This MakeoverMonday project visualizes S&P 500 sector performance, comparing daily returns with year-to-date (YTD) performance for each sector ETF.

Key metrics:

  • Daily Return: Single day price change (%)
  • YTD Return: Year-to-date cumulative return from January 1st (%)
  • Sectors: 11 S&P 500 sector ETFs + S&P 500 benchmark

This analysis helps identify which sectors are leading or lagging in both short-term and year-to-date performance.

Dataset

Show code 
import pandas as pd
import numpy as np
import yfinance as yf
import plotly.graph_objects as go
from datetime import datetime, timedelta
Show code 
# S&P 500 Sector ETFs mapping
SECTOR_ETFS = {
    "XLE": "Energy",
    "XLV": "Health Care",
    "XLU": "Utilities",
    "XLB": "Materials",
    "XLRE": "Real Estate",
    "XLF": "Financials",
    "XLP": "Cons. Staples",
    "XLI": "Industrials",
    "SPY": "S&P 500",
    "XLC": "Comm. Serv.",
    "XLK": "Info. Tech.",
    "XLY": "Cons. Discr.",
}

# Order for display (matching reference chart)
SECTOR_ORDER = [
    "Energy", "Health Care", "Utilities", "Materials", "Real Estate",
    "Financials", "Cons. Staples", "Industrials", "S&P 500",
    "Comm. Serv.", "Info. Tech.", "Cons. Discr."
]
Show code 
# Fetch data for all sector ETFs
tickers = list(SECTOR_ETFS.keys())

# Get current year for YTD calculation
current_year = datetime.now().year
ytd_start = f"{current_year}-01-01"

print(f"Fetching data from {ytd_start} to today...")

# Download data
data = yf.download(
    tickers,
    start=ytd_start,
    auto_adjust=True,
    progress=False
)

# Extract close prices
if isinstance(data.columns, pd.MultiIndex):
    prices = data["Close"]
else:
    prices = data

print(f"Fetched {len(prices)} trading days of data")
print(f"Latest date: {prices.index[-1].strftime('%Y-%m-%d')}")
Fetching data from 2026-01-01 to today...
Fetched 34 trading days of data
Latest date: 2026-02-20
Show code 
# Calculate daily returns (latest day)
daily_returns = prices.pct_change().iloc[-1] * 100

# Calculate YTD returns (from first day of year to latest)
ytd_returns = ((prices.iloc[-1] / prices.iloc[0]) - 1) * 100

# Create DataFrame for plotting
performance_data = pd.DataFrame({
    "ticker": tickers,
    "sector": [SECTOR_ETFS[t] for t in tickers],
    "daily": [daily_returns[t] for t in tickers],
    "ytd": [ytd_returns[t] for t in tickers]
})

# Sort by sector order
performance_data["sort_order"] = performance_data["sector"].map(
    {s: i for i, s in enumerate(SECTOR_ORDER)}
)
performance_data = performance_data.sort_values("sort_order").reset_index(drop=True)

# Display latest date for reference
latest_date = prices.index[-1].strftime("%m/%d/%Y")
print(f"\nPerformance as of {latest_date}:")
print(performance_data[["sector", "daily", "ytd"]].to_string(index=False))

Performance as of 02/20/2026:
       sector     daily       ytd
       Energy -0.543674 20.219056
  Health Care -0.279783  0.842398
    Utilities  0.477123  7.295047
    Materials  0.246067 14.830877
  Real Estate  0.833335  7.875186
   Financials  0.651966 -4.442015
Cons. Staples  0.250942 13.129099
  Industrials  0.504706 12.185087
      S&P 500  0.723179  0.916318
  Comm. Serv.  1.441726 -0.085542
  Info. Tech.  0.477853 -2.370061
 Cons. Discr.  1.040949 -0.760458

Chart: S&P 500 Sector Performance

Show code 
# Create grouped bar chart
fig = go.Figure()

# Daily returns (blue)
fig.add_trace(go.Bar(
    name=f'{latest_date}',
    x=performance_data['sector'],
    y=performance_data['daily'],
    marker_color='#1e5aa8',
    text=[f"{v:.1f}%" for v in performance_data['daily']],
    textposition='outside',
    textfont=dict(size=10, color='#1e5aa8'),
    hovertemplate='%{x}<br>Daily: %{y:.2f}%<extra></extra>'
))

# YTD returns (gold/orange)
fig.add_trace(go.Bar(
    name='Year-to-date',
    x=performance_data['sector'],
    y=performance_data['ytd'],
    marker_color='#d4a012',
    text=[f"{v:.1f}%" for v in performance_data['ytd']],
    textposition='outside',
    textfont=dict(size=10, color='#d4a012'),
    hovertemplate='%{x}<br>YTD: %{y:.2f}%<extra></extra>'
))

# Add zero line
fig.add_hline(y=0, line_color="black", line_width=1)

# Update layout
fig.update_layout(
    title=dict(
        text=f'<b>S&P 500 sector performance:</b> <span style="color:#1e5aa8">■</span> {latest_date}  <span style="color:#d4a012">■</span> Year-to-date',
        x=0.0,
        xanchor='left',
        font=dict(size=16)
    ),
    barmode='group',
    bargap=0.3,
    bargroupgap=0.1,
    xaxis=dict(
        title='',
        tickangle=0,
        tickfont=dict(size=11)
    ),
    yaxis=dict(
        title='',
        ticksuffix='%',
        gridcolor='rgba(0,0,0,0.1)',
        zeroline=True,
        zerolinecolor='black',
        zerolinewidth=1
    ),
    legend=dict(
        orientation='h',
        yanchor='bottom',
        y=1.02,
        xanchor='left',
        x=0
    ),
    template='plotly_white',
    height=550,
    margin=dict(t=80, b=60, l=60, r=30),
    font=dict(family="Arial, sans-serif")
)

fig.show()

# Save chart as static image for X post (requires kaleido)
try:
    fig.write_image("chart-1.png", width=1400, height=700, scale=2)
    print("\nChart saved as chart-1.png")
except Exception as e:
    print(f"\nNote: Could not save static image (kaleido may not be installed): {e}")

Chart saved as chart-1.png

Data Table

Show code 
# Display formatted table
display_df = performance_data[["sector", "daily", "ytd"]].copy()
display_df.columns = ["Sector", f"Daily ({latest_date})", "YTD"]
display_df[f"Daily ({latest_date})"] = display_df[f"Daily ({latest_date})"].apply(lambda x: f"{x:+.2f}%")
display_df["YTD"] = display_df["YTD"].apply(lambda x: f"{x:+.2f}%")

print("\n" + "="*50)
print("S&P 500 Sector Performance Summary")
print("="*50)
print(display_df.to_string(index=False))

==================================================
S&P 500 Sector Performance Summary
==================================================
       Sector Daily (02/20/2026)     YTD
       Energy             -0.54% +20.22%
  Health Care             -0.28%  +0.84%
    Utilities             +0.48%  +7.30%
    Materials             +0.25% +14.83%
  Real Estate             +0.83%  +7.88%
   Financials             +0.65%  -4.44%
Cons. Staples             +0.25% +13.13%
  Industrials             +0.50% +12.19%
      S&P 500             +0.72%  +0.92%
  Comm. Serv.             +1.44%  -0.09%
  Info. Tech.             +0.48%  -2.37%
 Cons. Discr.             +1.04%  -0.76%

Key Insights

  1. Sector Divergence: The spread between best and worst performing sectors reveals market rotation trends.

  2. Daily vs YTD: Sectors with strong YTD but weak daily returns may be experiencing profit-taking, while the opposite suggests momentum building.

  3. Benchmark Comparison: Comparing individual sectors against S&P 500 (SPY) shows relative strength/weakness.

  4. Defensive vs Cyclical: Utilities, Consumer Staples, and Health Care are typically defensive, while Technology, Consumer Discretionary, and Financials are more cyclical.

Design Decisions

  1. Grouped Bars: Side-by-side comparison makes it easy to compare daily vs YTD for each sector.

  2. Color Scheme: Blue for daily (short-term), Gold/Orange for YTD (cumulative) - consistent with financial data visualization conventions.

  3. Data Labels: Percentage values shown above/below bars for quick reading without hover.

  4. Zero Line: Clear reference line to distinguish positive from negative returns.

  5. Sector Order: Arranged to group similar sectors and highlight the benchmark (S&P 500) position.

Data source: Yahoo Finance (yfinance). This post is part of the MakeoverMonday weekly data visualization project.

CautionDisclaimer

This analysis is for educational and practice purposes only. Past performance is no guarantee of future results. Indexes are unmanaged, do not incur management fees, costs and expenses and cannot be invested in directly.