Black Lives Matter. Please consider donating to Black Girls Code today.

# Candlestick Charts in Python

How to make interactive candlestick charts in Python with Plotly. Six examples of candlestick charts with Pandas, time series, and yahoo finance data.

If you're using Dash Enterprise's Data Science Workspaces, you can copy/paste any of these cells into a Workspace Jupyter notebook.
Find out if your company is using Dash Enterprise.

New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

The candlestick chart is a style of financial chart describing open, high, low and close for a given x coordinate (most likely time). The boxes represent the spread between the open and close values and the lines represent the spread between the low and high values. Sample points where the close value is higher (lower) then the open value are called increasing (decreasing). By default, increasing candles are drawn in green whereas decreasing are drawn in red.

#### Simple Candlestick with Pandas¶

In :
import plotly.graph_objects as go

import pandas as pd
from datetime import datetime

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

fig.show()


#### Candlestick without Rangeslider¶

In :
import plotly.graph_objects as go
import pandas as pd

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

fig.update_layout(xaxis_rangeslider_visible=False)
fig.show()


#### Candlestick in Dash¶

Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the code and run python app.py.

Get started with the official Dash docs and learn how to effortlessly style & deploy apps like this with Dash Enterprise.

Out:

#### Adding Customized Text and Annotations¶

In :
import plotly.graph_objects as go
import pandas as pd

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

fig.update_layout(
title='The Great Recession',
yaxis_title='AAPL Stock',
shapes = [dict(
x0='2016-12-09', x1='2016-12-09', y0=0, y1=1, xref='x', yref='paper',
line_width=2)],
annotations=[dict(
x='2016-12-09', y=0.05, xref='x', yref='paper',
showarrow=False, xanchor='left', text='Increase Period Begins')]
)

fig.show()


#### Custom Candlestick Colors¶

In :
import plotly.graph_objects as go
import pandas as pd

fig = go.Figure(data=[go.Candlestick(
x=df['Date'],
open=df['AAPL.Open'], high=df['AAPL.High'],
low=df['AAPL.Low'], close=df['AAPL.Close'],
increasing_line_color= 'cyan', decreasing_line_color= 'gray'
)])

fig.show()


#### Simple Example with datetime Objects¶

In :
import plotly.graph_objects as go
from datetime import datetime

open_data = [33.0, 33.3, 33.5, 33.0, 34.1]
high_data = [33.1, 33.3, 33.6, 33.2, 34.8]
low_data = [32.7, 32.7, 32.8, 32.6, 32.8]
close_data = [33.0, 32.9, 33.3, 33.1, 33.1]
dates = [datetime(year=2013, month=10, day=10),
datetime(year=2013, month=11, day=10),
datetime(year=2013, month=12, day=10),
datetime(year=2014, month=1, day=10),
datetime(year=2014, month=2, day=10)]

fig = go.Figure(data=[go.Candlestick(x=dates,
open=open_data, high=high_data,
low=low_data, close=close_data)])

fig.show()


#### Reference¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.update_layout( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()
app.layout = html.Div([
dcc.Graph(figure=fig)
]) 