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.
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¶
import plotly.graph_objects as go
import pandas as pd
from datetime import datetime
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
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¶
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
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.
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Adding Customized Text and Annotations¶
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
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¶
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
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¶
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¶
For more information on candlestick attributes, see: https://plotly.com/python/reference/candlestick/
What About Dash?¶
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.add_trace( ... )
# fig.update_layout( ... )
from dash import Dash, dcc, html
app = Dash()
app.layout = html.Div([
dcc.Graph(figure=fig)
])
app.run_server(debug=True, use_reloader=False) # Turn off reloader if inside Jupyter