Stock Price Comparison Chart — Pygal

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 Pygal

Python source (Pygal)

""" anyplot.ai
line-stock-comparison: Stock Price Comparison Chart
Library: pygal 3.1.0 | Python 3.13.13
Quality: 84/100 | Updated: 2026-05-23
"""

import datetime
import os
import sys


# Remove script dir from sys.path to avoid shadowing the pygal package
_script_dir = sys.path[0] if sys.path else ""
if _script_dir in sys.path:
    sys.path.remove(_script_dir)

import numpy as np
import pygal
from pygal.style import Style


if _script_dir:
    sys.path.insert(0, _script_dir)

# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"

IMPRINT = ("#009E73", "#C475FD", "#AE3030", "#4467A3", "#99B314", "#954477", "#BD8233")

# Extended palette: data series use positions 1-4, INK_MUTED reserved for the reference line
PALETTE_WITH_REF = IMPRINT[:4] + (INK_MUTED,)

# Data
np.random.seed(42)
n_days = 252

start_date = datetime.date(2024, 1, 2)
dates = []
current_date = start_date
while len(dates) < n_days:
    if current_date.weekday() < 5:
        dates.append(current_date)
    current_date += datetime.timedelta(days=1)

returns_aapl = np.random.normal(0.0012, 0.018, n_days)
returns_googl = np.random.normal(0.0015, 0.022, n_days)
returns_msft = np.random.normal(-0.0002, 0.016, n_days)
returns_spy = np.random.normal(0.0004, 0.010, n_days)

price_aapl = 100 * np.cumprod(1 + returns_aapl)
price_googl = 100 * np.cumprod(1 + returns_googl)
price_msft = 100 * np.cumprod(1 + returns_msft)
price_spy = 100 * np.cumprod(1 + returns_spy)

rebased_aapl = price_aapl / price_aapl[0] * 100
rebased_googl = price_googl / price_googl[0] * 100
rebased_msft = price_msft / price_msft[0] * 100
rebased_spy = price_spy / price_spy[0] * 100

# Year-end returns for legend labels (data storytelling)
final_returns = {
    "AAPL": rebased_aapl[-1] - 100,
    "GOOGL": rebased_googl[-1] - 100,
    "MSFT": rebased_msft[-1] - 100,
    "SPY": rebased_spy[-1] - 100,
}


def fmt_ret(r):
    sign = "+" if r >= 0 else ""
    return f"{sign}{r:.1f}%"


# Best performer gets a thicker stroke for visual emphasis
best = max(final_returns, key=final_returns.get)


def series_stroke(name):
    return {"width": 6} if name == best else {"width": 3}


# Style
custom_style = Style(
    background=PAGE_BG,
    plot_background=PAGE_BG,
    foreground=INK,
    foreground_strong=INK,
    foreground_subtle=INK_MUTED,
    colors=PALETTE_WITH_REF,
    title_font_size=66,
    label_font_size=56,
    major_label_font_size=44,
    legend_font_size=44,
    value_font_size=36,
    stroke_width=4,
    font_family="sans-serif",
)

# X-axis labels — monthly markers only
x_labels_all = [d.strftime("%b %Y") if i == 0 or d.month != dates[i - 1].month else "" for i, d in enumerate(dates)]
x_labels_major = [label for label in x_labels_all if label]

# Chart
chart = pygal.Line(
    width=3200,
    height=1800,
    style=custom_style,
    title="line-stock-comparison · python · pygal · anyplot.ai",
    x_title="Date",
    y_title="Rebased Price (Start = 100)",
    show_x_guides=False,
    show_y_guides=True,
    dots_size=2,
    legend_at_bottom=True,
    legend_at_bottom_columns=5,
    x_label_rotation=45,
    show_minor_x_labels=False,
    x_labels_major=x_labels_major,
    margin_bottom=120,
)

chart.x_labels = x_labels_all

chart.add(
    f"AAPL ({fmt_ret(final_returns['AAPL'])})",
    [
        {"value": val, "label": f"AAPL | {dates[i].strftime('%Y-%m-%d')} | {val:.1f}"}
        for i, val in enumerate(rebased_aapl.tolist())
    ],
    stroke_style=series_stroke("AAPL"),
)
chart.add(
    f"GOOGL ({fmt_ret(final_returns['GOOGL'])})",
    [
        {"value": val, "label": f"GOOGL | {dates[i].strftime('%Y-%m-%d')} | {val:.1f}"}
        for i, val in enumerate(rebased_googl.tolist())
    ],
    stroke_style=series_stroke("GOOGL"),
)
chart.add(
    f"MSFT ({fmt_ret(final_returns['MSFT'])})",
    [
        {"value": val, "label": f"MSFT | {dates[i].strftime('%Y-%m-%d')} | {val:.1f}"}
        for i, val in enumerate(rebased_msft.tolist())
    ],
    stroke_style=series_stroke("MSFT"),
)
chart.add(
    f"SPY ({fmt_ret(final_returns['SPY'])})",
    [
        {"value": val, "label": f"SPY | {dates[i].strftime('%Y-%m-%d')} | {val:.1f}"}
        for i, val in enumerate(rebased_spy.tolist())
    ],
    stroke_style=series_stroke("SPY"),
)

# Horizontal reference line at y=100 anchors the starting baseline visually
chart.add("Baseline (100)", [100.0] * n_days, show_dots=False, stroke_style={"width": 2, "dasharray": "6,4"})

# Save
chart.render_to_png(f"plot-{THEME}.png")
with open(f"plot-{THEME}.html", "wb") as f:
    f.write(chart.render())

Part of Stock Price Comparison Chart on anyplot.ai.

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