Bollinger Bands Indicator Chart — plotnine

A Bollinger Bands chart displays price data with a volatility envelope consisting of three lines: a middle band (simple moving average), an upper band (SMA plus 2 standard deviations), and a lower band (SMA minus 2 standard deviations). This technical indicator helps traders identify overbought/oversold conditions, volatility patterns, and potential price breakouts by showing how prices relate to their recent statistical range.

Bollinger Bands Indicator Chart rendered with plotnine

Python source (plotnine)

""" anyplot.ai
indicator-bollinger: Bollinger Bands Indicator Chart
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 91/100 | Updated: 2026-05-17
"""

import os

import numpy as np
import pandas as pd
from plotnine import (
    aes,
    element_line,
    element_rect,
    element_text,
    geom_line,
    geom_ribbon,
    ggplot,
    labs,
    scale_x_datetime,
    theme,
    theme_minimal,
)


# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
RULE = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"

# Okabe-Ito palette
BRAND = "#009E73"  # Close price - first series
BAND_COLOR = "#4467A3"  # Bollinger bands - second series

# Data - Generate realistic stock price with Bollinger Bands
np.random.seed(42)
n_periods = 120
dates = pd.date_range("2024-01-01", periods=n_periods, freq="B")

# Generate price with trend and volatility
returns = np.random.normal(0.001, 0.018, n_periods)
price = 100 * np.cumprod(1 + returns)

# Add volatility clusters for interesting band patterns
volatility_shock = np.zeros(n_periods)
volatility_shock[30:45] = np.random.normal(0, 0.025, 15)
volatility_shock[80:95] = np.random.normal(0, 0.02, 15)
price = price * (1 + volatility_shock)

# Calculate Bollinger Bands (20-period SMA, 2 standard deviations)
window = 20
sma = pd.Series(price).rolling(window=window).mean()
std = pd.Series(price).rolling(window=window).std()
upper_band = sma + 2 * std
lower_band = sma - 2 * std

# Create DataFrame
df = pd.DataFrame({"date": dates, "close": price, "sma": sma, "upper_band": upper_band, "lower_band": lower_band})

# Remove NaN values from rolling calculation
df = df.dropna().reset_index(drop=True)

# Plot
plot = (
    ggplot(df)
    + geom_ribbon(aes(x="date", ymin="lower_band", ymax="upper_band"), fill=BAND_COLOR, alpha=0.15)
    + geom_line(aes(x="date", y="upper_band"), color=BAND_COLOR, size=0.8, linetype="dashed")
    + geom_line(aes(x="date", y="lower_band"), color=BAND_COLOR, size=0.8, linetype="dashed")
    + geom_line(aes(x="date", y="sma"), color=BAND_COLOR, size=1.0, linetype="dotted")
    + geom_line(aes(x="date", y="close"), color=BRAND, size=1.3)
    + scale_x_datetime(date_labels="%b %Y", date_breaks="1 month")
    + labs(x="Date", y="Price (USD)", title="indicator-bollinger · plotnine · anyplot.ai")
    + theme_minimal()
    + theme(
        figure_size=(16, 9),
        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
        panel_background=element_rect(fill=PAGE_BG),
        panel_grid_major=element_line(color=INK, size=0.3, alpha=0.10),
        panel_grid_minor=element_line(color=INK, size=0.2, alpha=0.05),
        panel_border=element_rect(color=INK_SOFT, fill=None),
        axis_title=element_text(size=20, color=INK),
        axis_text=element_text(size=16, color=INK_SOFT),
        axis_text_x=element_text(angle=45, ha="right"),
        axis_line=element_line(color=INK_SOFT),
        plot_title=element_text(size=24, color=INK),
        text=element_text(size=14, color=INK),
    )
)

# Save
plot.save(f"plot-{THEME}.png", dpi=300, verbose=False)

Part of Bollinger Bands Indicator Chart on anyplot.ai.

Bollinger Bands Indicator Chart — plotnine

Python source (plotnine)

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