How to Create a Candlestick Chart in Python ?

Introduction

Creating a candlestick chart in Python is straightforward using libraries like Plotly, Matplotlib, or mplfinance (which is built specifically for financial data visualization).

A candlestick chart is a type of financial visualization used to show the price movement of an asset—such as a stock, cryptocurrency, or commodity—over a specific period of time.
Each candlestick represents one time interval (for example, one day or one hour) and displays four key values:

Open – the price at the beginning of the interval
High – the highest price reached
Low – the lowest price reached
Close – the price at the end of the interval

These values are drawn as a “candle”:

The body (a rectangle) shows the range between the open and close prices.
The wicks (thin lines) show the high and low prices.
A green (or white) candle usually indicates the price increased (close > open), while a red (or black) candle indicates a decrease (close < open).

Candlestick charts are widely used in financial analysis and trading because they provide an intuitive way to see market sentiment, trends, and volatility. They help traders identify patterns such as bullish or bearish reversals, momentum shifts, and support/resistance levels.

Table of contents

Introduction
Option 1: Using Plotly (Interactive & Modern)
Option 2: Using mplfinance (Matplotlib-based for financial data)
Option 3: Using Matplotlib (manual version, educational)
Improved Candlestick Chart
References

In Python, several libraries—like Matplotlib, Plotly, and mplfinance—allow you to easily create and customize candlestick charts for both research and presentation purposes.

Option 1: Using Plotly (Interactive & Modern)

import plotly.graph_objects as go
import pandas as pd

# Example dataset
data = {
    'Date': pd.date_range(start='2024-01-01', periods=10, freq='D'),
    'Open': [100, 102, 101, 105, 107, 108, 110, 112, 111, 113],
    'High': [103, 104, 106, 108, 109, 111, 113, 114, 115, 116],
    'Low': [99, 100, 100, 102, 105, 106, 107, 109, 110, 111],
    'Close': [102, 101, 105, 107, 108, 110, 112, 111, 113, 115],
}
df = pd.DataFrame(data)

# Create the candlestick chart
fig = go.Figure(data=[go.Candlestick(
    x=df['Date'],
    open=df['Open'],
    high=df['High'],
    low=df['Low'],
    close=df['Close']
)])

fig.update_layout(
    title='Candlestick Chart Example',
    xaxis_title='Date',
    yaxis_title='Price',
    xaxis_rangeslider_visible=False  # Hide the range slider
)

fig.show()

How to Create a Candlestick Chart in Python ?

Best for: interactive dashboards, hover tooltips, zooming
Libraries needed: plotly (pip install plotly)

Option 2: Using `mplfinance` (Matplotlib-based for financial data)

import pandas as pd
import mplfinance as mpf

# Example dataset
data = {
    'Date': pd.date_range(start='2024-01-01', periods=10, freq='D'),
    'Open': [100, 102, 101, 105, 107, 108, 110, 112, 111, 113],
    'High': [103, 104, 106, 108, 109, 111, 113, 114, 115, 116],
    'Low': [99, 100, 100, 102, 105, 106, 107, 109, 110, 111],
    'Close': [102, 101, 105, 107, 108, 110, 112, 111, 113, 115],
}
df = pd.DataFrame(data).set_index('Date')

# Plot using mplfinance
mpf.plot(df, type='candle', style='charles', title='Candlestick Chart', ylabel='Price')

Best for: static publication-quality plots
Libraries needed: mplfinance (pip install mplfinance)

Option 3: Using Matplotlib (manual version, educational)

If you don’t want extra dependencies, you can build it yourself using Matplotlib rectangles:

import matplotlib.pyplot as plt
import pandas as pd

# Example dataset
df = pd.DataFrame({
    'Date': pd.date_range(start='2024-01-01', periods=10),
    'Open': [100, 102, 101, 105, 107, 108, 110, 112, 111, 113],
    'High': [103, 104, 106, 108, 109, 111, 113, 114, 115, 116],
    'Low': [99, 100, 100, 102, 105, 106, 107, 109, 110, 111],
    'Close': [102, 101, 105, 107, 108, 110, 112, 111, 113, 115],
})

fig, ax = plt.subplots(figsize=(10, 5))
width = 0.6

for i, row in df.iterrows():
    color = 'green' if row['Close'] >= row['Open'] else 'red'
    ax.plot([i, i], [row['Low'], row['High']], color='black')  # wick
    ax.add_patch(plt.Rectangle(
        (i - width/2, min(row['Open'], row['Close'])),
        width,
        abs(row['Close'] - row['Open']),
        color=color
    ))

ax.set_xticks(range(len(df)))
ax.set_xticklabels(df['Date'].dt.strftime('%Y-%m-%d'), rotation=45)
ax.set_title('Manual Candlestick Chart')
ax.set_ylabel('Price')
plt.tight_layout()
plt.show()

Best for: learning how candlesticks work under the hood
No external libraries needed

Improved Candlestick Chart

Generate Fake Stock Data

import numpy as np
import pandas as pd

# Generate reproducible fake stock data
np.random.seed(42)
n_days = 60
dates = pd.date_range(start="2025-01-01", periods=n_days, freq="D")

# Simulate prices with random walk
price = np.cumsum(np.random.normal(0, 1, n_days)) + 100

# Create OHLC data
open_ = price + np.random.normal(0, 0.5, n_days)
close = open_ + np.random.normal(0, 1, n_days)
high = np.maximum(open_, close) + np.random.uniform(0.5, 1.5, n_days)
low = np.minimum(open_, close) - np.random.uniform(0.5, 1.5, n_days)
volume = np.random.randint(1000, 5000, n_days)

df = pd.DataFrame({
    "Date": dates,
    "Open": open_,
    "High": high,
    "Low": low,
    "Close": close,
    "Volume": volume
}).set_index("Date")

# Add moving averages
df["MA5"] = df["Close"].rolling(5).mean()
df["MA10"] = df["Close"].rolling(10).mean()

df.head()

Matplotlib Version

fig, ax = plt.subplots(figsize=(12, 6))

width = 0.6
colors = ['green' if c >= o else 'red' for c, o in zip(df['Close'], df['Open'])]

for i, (o, h, l, c, color) in enumerate(zip(df['Open'], df['High'], df['Low'], df['Close'], colors)):
    # Draw wick
    ax.plot([i, i], [l, h], color='black', linewidth=1)
    # Draw candle body
    ax.add_patch(plt.Rectangle((i - width / 2, min(o, c)),
                               width,
                               abs(o - c),
                               color=color, alpha=0.8, linewidth=0.5))

# Plot moving averages
ax.plot(df.index, df["MA5"], label="MA5", linewidth=1.5, color="blue")
ax.plot(df.index, df["MA10"], label="MA10", linewidth=1.5, color="orange")


tick_positions = np.arange(0, len(df), 5)  # every 5th day
tick_labels = df['Date'].dt.strftime('%b %d').iloc[tick_positions]

ax.set_xticks(tick_positions)
ax.set_xticklabels(tick_labels, rotation=45, ha='right')

ax.set_title("Simulated Stock Price Candlestick Chart", fontsize=16, fontweight='bold', pad=15)
ax.set_ylabel("Price ($)")
ax.legend()
ax.grid(alpha=0.3)

plt.tight_layout()
plt.show()

Plotly Version (Interactive)

import plotly.graph_objects as go

fig = go.Figure()

# Candlestick trace
fig.add_trace(go.Candlestick(
    x=df.index,
    open=df["Open"],
    high=df["High"],
    low=df["Low"],
    close=df["Close"],
    name="Price"
))

# Moving averages
fig.add_trace(go.Scatter(
    x=df.index, y=df["MA5"], mode='lines', name='MA5', line=dict(color='blue', width=1.5)
))
fig.add_trace(go.Scatter(
    x=df.index, y=df["MA10"], mode='lines', name='MA10', line=dict(color='orange', width=1.5)
))

# Layout customization
fig.update_layout(
    title="📈 Simulated Stock Price (Plotly Candlestick)",
    yaxis_title="Price ($)",
    xaxis_title="Date",
    xaxis_rangeslider_visible=False,
    template="plotly_white",
    legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
)

fig.show()

Features:

Interactive zoom, pan, and hover
Moving averages overlaid
Clean background and modern style

mplfinance Version

import mplfinance as mpf

# Define moving averages to display
mav = (5, 10)

# Plot with custom style
mpf.plot(
    df,
    type='candle',
    mav=mav,
    volume=True,
    title="📊 Simulated Stock Price (mplfinance)",
    ylabel="Price ($)",
    ylabel_lower="Volume",
    style='yahoo',
    figratio=(14, 8),
    figscale=1.2
)

Features:

Auto-calculates moving averages
Volume subplot included
Clean, finance-friendly layout
Several built-in styles available (charles, yahoo, nightclouds, default, etc.)

Summary Comparison

Library	Strengths	Interactivity	Volume Support	Custom Styling
Plotly	Interactive, modern web charts	✅	Manual	High
mplfinance	Tailored for finance charts, easy to use	❌	Built-in	Medium
Matplotlib	Full control, educational	❌	Manual	Very High

References

Links	Site
https://matplotlib.org/stable/gallery/lines_bars_and_markers/candlestick_chart.html	Matplotlib Official Gallery – Candlestick chart example
https://plotly.com/python/candlestick-charts/	Plotly Official Documentation – Candlestick charts in Python
https://github.com/matplotlib/mplfinance	mplfinance GitHub Repository – Official source and documentation for financial plotting

Developer in my spare time, I’m interested in Earth observation and satellite-based fire detection. Passionate about Python, machine learning, and open science.

How to Create a Candlestick Chart in Python ?

Introduction

Option 1: Using Plotly (Interactive & Modern)

Option 2: Using `mplfinance` (Matplotlib-based for financial data)

Option 3: Using Matplotlib (manual version, educational)

Improved Candlestick Chart

Generate Fake Stock Data

Matplotlib Version

Plotly Version (Interactive)

mplfinance Version

Summary Comparison

References

Benjamin

Skills

How to Create a Candlestick Chart in Python ?

Introduction

Option 1: Using Plotly (Interactive & Modern)

Option 2: Using mplfinance (Matplotlib-based for financial data)

Option 3: Using Matplotlib (manual version, educational)

Improved Candlestick Chart

Generate Fake Stock Data

Matplotlib Version

Plotly Version (Interactive)

mplfinance Version

Summary Comparison

References

Benjamin

Skills

Option 2: Using `mplfinance` (Matplotlib-based for financial data)