A candlestick chart displays open, high, low, and close (OHLC) price data for financial instruments over time. Each candlestick shows the price range within a specific period, with the body indicating the open-close range and the wicks (shadows) showing the high-low range. Color coding distinguishes bullish (price increase) from bearish (price decrease) periods, making it easy to identify trends and price patterns at a glance.
""" anyplot.ai
candlestick-basic: Basic Candlestick Chart
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-30
"""
import os
import sys
# Prevent self-import: script filename matches the library name, so remove script dir from path
if sys.path and sys.path[0] not in (None,):
del sys.path[0]
import numpy as np
import pandas as pd
from plotnine import (
aes,
annotate,
coord_cartesian,
element_blank,
element_line,
element_rect,
element_text,
geom_hline,
geom_rect,
geom_segment,
ggplot,
labs,
scale_color_manual,
scale_fill_manual,
scale_x_continuous,
scale_y_continuous,
theme,
theme_minimal,
)
# Theme tokens (Imprint palette + 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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# Imprint palette — semantic exception: green=bullish (gain/profit), red=bearish (loss/decline)
BULL_COLOR = "#009E73" # Imprint pos 1 — profit / gain / up
BEAR_COLOR = "#AE3030" # Imprint pos 5 — loss / decline (deferred semantic red anchor)
palette = {"Bullish": BULL_COLOR, "Bearish": BEAR_COLOR}
# Data — 30 trading days, random walk starting at $150
np.random.seed(42)
n_days = 30
dates = pd.date_range(start="2024-01-02", periods=n_days, freq="B")
price = 150.0
opens, highs, lows, closes = [], [], [], []
for _ in range(n_days):
open_price = price
change = np.random.randn() * 3
close_price = open_price + change
high_price = max(open_price, close_price) + abs(np.random.randn() * 1.5)
low_price = min(open_price, close_price) - abs(np.random.randn() * 1.5)
opens.append(open_price)
highs.append(high_price)
lows.append(low_price)
closes.append(close_price)
price = close_price + np.random.randn() * 0.5
df = pd.DataFrame({"date": dates, "open": opens, "high": highs, "low": lows, "close": closes})
df["day"] = np.arange(len(df))
df["direction"] = pd.Categorical(
np.where(df["close"] >= df["open"], "Bullish", "Bearish"), categories=["Bullish", "Bearish"]
)
df["body_top"] = df[["open", "close"]].max(axis=1)
df["body_bottom"] = df[["open", "close"]].min(axis=1)
# X-axis tick labels (every 5th trading day)
tick_indices = list(range(0, n_days, 5))
tick_labels = [dates[i].strftime("%b %d") for i in tick_indices]
# Reference prices for storytelling annotations
open_first = df["open"].iloc[0]
close_last = df["close"].iloc[-1]
close_dir_color = BULL_COLOR if close_last >= open_first else BEAR_COLOR
net_pct = (close_last - open_first) / open_first * 100
# Title — 50 chars < 67 baseline, so default 12pt applies
title = "candlestick-basic · python · plotnine · anyplot.ai"
title_size = max(8, round(12 * 67 / len(title)))
# Plot
plot = (
ggplot(df)
# Reference line at period opening price
+ geom_hline(yintercept=open_first, linetype="dashed", color=INK_SOFT, size=0.5)
# Wicks colored by direction for visual coherence
+ geom_segment(aes(x="day", xend="day", y="low", yend="high", color="direction"), size=0.8)
# Candle bodies — fill by direction, static INK_SOFT edge for definition
+ geom_rect(
aes(xmin="day - 0.35", xmax="day + 0.35", ymin="body_bottom", ymax="body_top", fill="direction"),
color=INK_SOFT,
size=0.3,
)
+ scale_fill_manual(values=palette, name="Direction")
+ scale_color_manual(values=palette, guide=None)
# Annotate reference line label
+ annotate(
"text", x=n_days - 0.5, y=open_first + 0.8, label=f"Open ${open_first:.0f}", size=3, color=INK_MUTED, ha="right"
)
# Annotate net change at bottom
+ annotate(
"text",
x=n_days - 0.5,
y=df["low"].min() - 1.2,
label=f"Close ${close_last:.0f} ({net_pct:+.1f}%)",
size=3,
color=close_dir_color,
ha="right",
)
+ scale_x_continuous(breaks=tick_indices, labels=tick_labels, expand=(0.02, 0.5))
+ scale_y_continuous(labels=lambda vals: [f"${v:,.0f}" for v in vals])
+ coord_cartesian(ylim=(df["low"].min() - 2, df["high"].max() + 2))
+ labs(x="", y="Price ($)", title=title)
+ theme_minimal()
+ theme(
figure_size=(8, 4.5),
text=element_text(size=7),
plot_title=element_text(size=title_size, color=INK),
axis_title=element_text(size=10, color=INK),
axis_text=element_text(size=8, color=INK_SOFT),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid_major_x=element_blank(),
panel_grid_minor_x=element_blank(),
panel_grid_major_y=element_line(color=INK_SOFT, size=0.3, alpha=0.15),
panel_grid_minor_y=element_blank(),
axis_line=element_line(color=INK_SOFT),
legend_position="top",
legend_title=element_text(size=9, color=INK),
legend_text=element_text(size=8, color=INK_SOFT),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
)
)
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)
Part of Basic Candlestick Chart on anyplot.ai.