Stock Price Comparison Chart — plotnine

A multi-line chart comparing multiple stock price series normalized to a common starting point (rebased to 100) for direct performance comparison over time. By rebasing all series to the same initial value, investors can easily compare relative returns regardless of absolute price differences. This visualization reveals which stocks outperformed or underperformed relative to each other, making it essential for portfolio analysis and benchmark comparisons.

Stock Price Comparison Chart rendered with plotnine

Python source (plotnine)

""" anyplot.ai
line-stock-comparison: Stock Price Comparison Chart
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 88/100 | Updated: 2026-05-23
"""

import os
import sys


_script_dir = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if not p or os.path.abspath(p) != _script_dir]

import numpy as np
import pandas as pd
from plotnine import (
    aes,
    element_blank,
    element_line,
    element_rect,
    element_text,
    geom_hline,
    geom_line,
    geom_point,
    geom_ribbon,
    geom_text,
    ggplot,
    labs,
    scale_color_manual,
    scale_x_date,
    theme,
    theme_minimal,
)


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 = ["#009E73", "#C475FD", "#AE3030", "#4467A3"]

# Data
np.random.seed(42)
n_days = 252
dates = pd.date_range("2024-01-02", periods=n_days, freq="B")

symbols = ["AAPL", "GOOGL", "MSFT", "SPY"]
params = [(0.0005, 0.013), (0.0003, 0.015), (0.0006, 0.012), (0.0004, 0.008)]

dfs = []
for symbol, (drift, vol) in zip(symbols, params, strict=True):
    daily_returns = np.random.normal(drift, vol, n_days)
    prices = 100 * np.exp(np.cumsum(daily_returns))
    dfs.append(pd.DataFrame({"date": dates, "symbol": symbol, "rebased": prices}))

df = pd.concat(dfs, ignore_index=True)

# End-of-line annotation data
last_df = df.groupby("symbol").apply(lambda g: g.iloc[-1]).reset_index(drop=True)
last_df["label"] = last_df["rebased"].apply(lambda x: f"{x:.0f}")
last_df["label_date"] = last_df["date"] + pd.Timedelta(days=7)

# SPY outperformance ribbon: shades the region where SPY beats the 100 baseline
spy_df = df[df["symbol"] == "SPY"].copy()
others_df = df[df["symbol"] != "SPY"].copy()
spy_ribbon_df = spy_df[["date", "rebased"]].rename(columns={"rebased": "ymax"}).copy()
spy_ribbon_df["ymin"] = 100.0
spy_ribbon_df = spy_ribbon_df[spy_ribbon_df["ymax"] > 100]

x_max = dates[-1] + pd.Timedelta(days=30)

# Plot
plot = (
    ggplot(df, aes(x="date", y="rebased", color="symbol"))
    + geom_ribbon(
        data=spy_ribbon_df,
        mapping=aes(x="date", ymin="ymin", ymax="ymax"),
        fill=IMPRINT[3],
        alpha=0.08,
        inherit_aes=False,
    )
    + geom_hline(yintercept=100, linetype="dashed", color=INK_SOFT, size=0.6)
    + geom_line(data=others_df, size=0.9)
    + geom_line(data=spy_df, size=1.6)
    + geom_point(data=last_df, size=2.5, show_legend=False)
    + geom_text(data=last_df, mapping=aes(x="label_date", label="label"), size=7, ha="left", show_legend=False)
    + scale_color_manual(values=IMPRINT)
    + scale_x_date(date_labels="%b '%y", date_breaks="2 months", limits=[dates[0], x_max])
    + labs(
        x="Date",
        y="Rebased Price (Start = 100)",
        title="line-stock-comparison · python · plotnine · anyplot.ai",
        color="Symbol",
    )
    + theme_minimal()
    + theme(
        figure_size=(8, 4.5),
        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
        panel_background=element_rect(fill=PAGE_BG),
        panel_border=element_blank(),
        panel_grid_major=element_line(color=INK, size=0.3, alpha=0.10),
        panel_grid_minor=element_blank(),
        axis_line=element_line(color=INK_SOFT, size=0.5),
        axis_title=element_text(color=INK, size=10),
        axis_text=element_text(color=INK_SOFT, size=8),
        axis_text_x=element_text(angle=45, ha="right"),
        plot_title=element_text(color=INK, size=12, fontweight="bold"),
        legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
        legend_text=element_text(color=INK_SOFT, size=8),
        legend_title=element_text(color=INK, size=9),
    )
)

# Save
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in")

Part of Stock Price Comparison Chart on anyplot.ai.

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