Yield Curve (Interest Rate Term Structure) — Matplotlib

A yield curve plots interest rates (yields) of bonds against their maturities, from short-term (e.g., 1 month) to long-term (e.g., 30 years). It is one of the most iconic charts in macroeconomics and finance, used to assess market expectations for future interest rates, economic growth, and inflation. An inverted yield curve (where short-term rates exceed long-term rates) is a widely followed recession indicator. The plot should support displaying multiple curves (e.g., different dates) on the same axes to show how the term structure evolves over time.

Yield Curve (Interest Rate Term Structure) rendered with Matplotlib

Python source (Matplotlib)

""" anyplot.ai
line-yield-curve: Yield Curve (Interest Rate Term Structure)
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 89/100 | Updated: 2026-06-10
"""

import os

import matplotlib.pyplot as plt
import numpy as np


# Theme tokens — see default-style-guide.md "Theme-adaptive Chrome"
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"

# Imprint palette — semantic exception applied:
# #009E73 (brand green) = normal/healthy curve; #AE3030 (matte red) = inverted/recession signal
BRAND = "#009E73"  # Jan 2022 normal curve — Imprint position 1
INVERTED_COLOR = "#AE3030"  # Oct 2023 inverted curve — semantic red (recession indicator)
NORM_COLOR = "#C475FD"  # Jan 2025 normalizing curve — Imprint position 2

# Data — U.S. Treasury yield curves across three monetary-policy regimes
maturities = ["1M", "3M", "6M", "1Y", "2Y", "3Y", "5Y", "7Y", "10Y", "20Y", "30Y"]
maturity_years = np.array([1 / 12, 0.25, 0.5, 1, 2, 3, 5, 7, 10, 20, 30])

# Normal upward-sloping curve (Jan 2022) — pre-hiking cycle
yields_normal = np.array([0.08, 0.21, 0.47, 0.78, 1.18, 1.42, 1.72, 1.90, 1.93, 2.28, 2.25])

# Inverted curve (Oct 2023) — peak-rate environment, recession signal
yields_inverted = np.array([5.54, 5.55, 5.52, 5.46, 5.05, 4.80, 4.62, 4.65, 4.73, 5.07, 4.95])

# Normalizing curve (Jan 2025) — Fed pivoting, curve re-steepening
yields_normalizing = np.array([4.36, 4.34, 4.32, 4.22, 4.20, 4.23, 4.38, 4.47, 4.58, 4.85, 4.84])

# Plot
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)

curves = [
    (yields_normal, BRAND, "Jan 2022 (Normal)"),
    (yields_inverted, INVERTED_COLOR, "Oct 2023 (Inverted)"),
    (yields_normalizing, NORM_COLOR, "Jan 2025 (Normalizing)"),
]

for yields, color, label in curves:
    ax.plot(
        maturity_years,
        yields,
        color=color,
        linewidth=2.5,
        label=label,
        marker="o",
        markersize=7,
        markeredgecolor=PAGE_BG,
        markeredgewidth=1.0,
    )

# Shade inversion region: from 3M peak (5.55%) down to 5Y trough
trough_idx = 6  # index of 5Y maturity
ax.fill_between(
    maturity_years[: trough_idx + 1],
    yields_inverted[: trough_idx + 1],
    yields_inverted[1],  # 3M peak = 5.55%
    alpha=0.10,
    color=INVERTED_COLOR,
)

# Annotation highlighting the inversion
ax.annotate(
    "Yield curve inversion",
    xy=(3, 4.80),
    xytext=(8, 5.50),
    fontsize=8,
    color=INVERTED_COLOR,
    fontweight="medium",
    arrowprops={"arrowstyle": "->", "color": INVERTED_COLOR, "lw": 1.2},
)

# Style
title = "line-yield-curve · python · matplotlib · anyplot.ai"
title_fontsize = max(8, round(12 * 67 / len(title))) if len(title) > 67 else 12

ax.set_xlabel("Maturity", fontsize=10, color=INK)
ax.set_ylabel("Yield (%)", fontsize=10, color=INK)
ax.set_title(title, fontsize=title_fontsize, fontweight="medium", color=INK)

ax.set_xscale("log")
ax.set_xticks(maturity_years)
ax.set_xticklabels(maturities)
ax.minorticks_off()
ax.tick_params(axis="x", labelsize=8, colors=INK_SOFT, length=0)
ax.tick_params(axis="y", labelsize=8, colors=INK_SOFT, length=0)

ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
    ax.spines[s].set_color(INK_SOFT)

ax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color=INK)

leg = ax.legend(fontsize=8, frameon=True, loc="lower right")
leg.get_frame().set_facecolor(ELEVATED_BG)
leg.get_frame().set_edgecolor(INK_SOFT)
plt.setp(leg.get_texts(), color=INK_SOFT)

fig.subplots_adjust(left=0.09, right=0.97, top=0.93, bottom=0.12)

# Save
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
plt.close()

Part of Yield Curve (Interest Rate Term Structure) on anyplot.ai.

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