A histogram showing the distribution of financial returns (daily, weekly, or monthly) with a normal distribution overlay for comparison. This visualization is essential for risk analysis, allowing analysts to assess whether returns follow a normal distribution, identify fat tails indicating higher-than-expected extreme events, and measure asymmetry through skewness. Key statistics are displayed directly on the plot for quick interpretation.
""" anyplot.ai
histogram-returns-distribution: Returns Distribution Histogram
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-20
"""
import os
import sys
import time
from pathlib import Path
# Remove the current directory from sys.path to avoid shadowing the bokeh package
sys.path = [p for p in sys.path if p not in ("", ".", os.getcwd(), os.path.dirname(__file__))]
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import BoxAnnotation, ColumnDataSource, HoverTool, Label
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# Theme tokens
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"
# Data
np.random.seed(42)
n_days = 252
daily_returns = np.random.normal(loc=0.0005, scale=0.015, size=n_days) * 100
mean_return = np.mean(daily_returns)
std_return = np.std(daily_returns)
n = len(daily_returns)
skewness = np.sum(((daily_returns - mean_return) / std_return) ** 3) / n
kurtosis = np.sum(((daily_returns - mean_return) / std_return) ** 4) / n - 3
n_bins = 30
hist, edges = np.histogram(daily_returns, bins=n_bins, density=True)
x_norm = np.linspace(daily_returns.min() - std_return, daily_returns.max() + std_return, 200)
y_norm = (1 / (std_return * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((x_norm - mean_return) / std_return) ** 2)
lower_tail = mean_return - 2 * std_return
upper_tail = mean_return + 2 * std_return
bar_colors = [
"#C475FD" if (edges[i] < lower_tail or edges[i + 1] > upper_tail) else "#009E73" for i in range(len(hist))
]
hist_source = ColumnDataSource(
data={
"top": hist,
"left": edges[:-1],
"right": edges[1:],
"bottom": [0] * len(hist),
"color": bar_colors,
"bin_left": [f"{edges[i]:.2f}" for i in range(len(hist))],
"bin_right": [f"{edges[i + 1]:.2f}" for i in range(len(hist))],
"density": [f"{h:.4f}" for h in hist],
}
)
norm_source = ColumnDataSource(data={"x": x_norm, "y": y_norm})
# Plot
p = figure(
width=3200,
height=1800,
title="histogram-returns-distribution · python · bokeh · anyplot.ai",
x_axis_label="Daily Returns (%)",
y_axis_label="Density",
toolbar_location=None,
min_border_bottom=160,
min_border_left=180,
min_border_top=110,
min_border_right=50,
)
p.add_layout(BoxAnnotation(right=lower_tail, fill_alpha=0.10, fill_color="#C475FD"))
p.add_layout(BoxAnnotation(left=upper_tail, fill_alpha=0.10, fill_color="#C475FD"))
bars = p.quad(
top="top",
bottom="bottom",
left="left",
right="right",
fill_color="color",
line_color=PAGE_BG,
fill_alpha=0.80,
line_width=1,
source=hist_source,
)
hover = HoverTool(renderers=[bars], tooltips=[("Range", "@bin_left% – @bin_right%"), ("Density", "@density")])
p.add_tools(hover)
p.line(x="x", y="y", source=norm_source, line_color="#4467A3", line_width=5, legend_label="Normal Distribution")
stats_text = f"Mean: {mean_return:.3f}%\nStd Dev: {std_return:.3f}%\nSkewness: {skewness:.3f}\nKurtosis: {kurtosis:.3f}"
stats_label = Label(
x=210,
y=1180,
x_units="screen",
y_units="screen",
text=stats_text,
text_font_size="30pt",
text_color=INK,
background_fill_color=ELEVATED_BG,
background_fill_alpha=0.90,
border_line_color=INK_SOFT,
border_line_width=2,
)
p.add_layout(stats_label)
# Style
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = None
p.title.text_font_size = "50pt"
p.title.text_color = INK
p.xaxis.axis_label_text_font_size = "42pt"
p.yaxis.axis_label_text_font_size = "42pt"
p.xaxis.axis_label_text_color = INK
p.yaxis.axis_label_text_color = INK
p.xaxis.major_label_text_font_size = "34pt"
p.yaxis.major_label_text_font_size = "34pt"
p.xaxis.major_label_text_color = INK_SOFT
p.yaxis.major_label_text_color = INK_SOFT
p.xaxis.axis_line_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.xaxis.major_tick_line_color = INK_SOFT
p.yaxis.major_tick_line_color = INK_SOFT
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = INK
p.ygrid.grid_line_alpha = 0.10
p.legend.location = "top_right"
p.legend.label_text_font_size = "34pt"
p.legend.background_fill_color = ELEVATED_BG
p.legend.border_line_color = None
p.legend.label_text_color = INK_SOFT
# Save
output_file(f"plot-{THEME}.html")
save(p)
W, H = 3200, 1800
opts = Options()
for arg in (
"--headless=new",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
f"--window-size={W},{H}",
"--hide-scrollbars",
):
opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
driver.execute_cdp_cmd(
"Emulation.setDeviceMetricsOverride", {"width": W, "height": H, "deviceScaleFactor": 1, "mobile": False}
)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Part of Returns Distribution Histogram on anyplot.ai.