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Building a Dash App with Plotly Graph Updates and Live Web Socket Streaming

As a trader, you want to stay up-to-date with real-time market data. One effective way to do this is to create a live streaming dashboard using Dash, a popular Python library for building web applications. In this article, we will show you how to build a Dash app that feeds live data from the Ethereum blockchain and updates a Plotly graph in real time.

Prerequisites

Before you begin, make sure you have the following installed:

• Dash (“pip install dash”)

• Plotly (“pip install plotly”)

• Python 3.x

Set up the environment

Create a new Python file (e.g. “app.py”) and add the following code to set up the environment:

import dash
from dash import dcc, html
import plotly.graph_objects as go
import websocket





Initialize the Dash application
application = dash.Dash(__name__)

Set up the Plotly graph
fig = go.Figure(data=[go.Scatter(x=[], y=[])])

Create the Plotly component
plotly_component = dcc.Graph(id='live-websocket-graph')

Create the trading bot

Then create a new file (e.g. “trading_bot.py”) that contains the logic for your trading bot. In this example, we will use a simple Ethereum blockchain node to get live data:

import eth_utils
from eth_positioning import BlockNumber

Set up an Ethereum blockchain node
node_url = '
eth_node = ' + node_url
def get_block_number():
block_number = BlockNumber.from_string('1')
return block_number.to_int()
app.layout = html.Div([
html.H1 (Ethereum Live Stream Dashboard),
dcc.Graph(id='live-websocket-graph'),
dcc.LiveSocket(url=node_url, id='live-websocket-socket', stream=True),
])
def update_graph():

Get live data from an Ethereum blockchain node
block_number = get_block_number()
block_data = eth_utils.get_block(block_number).get_value()

Update the Plotly graph with new data
fig.data[0].x.extend(block_number)
fig.data[0].y.extend([block_data])

Redraw the graph
app.update_graph(id='live-websocket-graph', layout=fig.layout)
@app.callback(
[html.Button('Refresh data', id='refresh-data-button')], 
[dash.dependencies.Output('live-websocket-socket-stream', 'data'), dash.dependencies.Output('live-websocket-socket-stream', 'figure')]
)
def refresh_data_button():

Get new data from Ethereum blockchain node
block_number = get_block_number()

Update the Plotly graph with the new data
fig.update_layout(x=[block_number], y=[])
return html.P('Data updated successfully!'), fig
if __name__ == '__main__':
app.run_server(debug=True)

Run the application

To run the application, run the following command:

python app.py

This will start the Dash server and open a web browser with the dashboard.

Live Streaming Data

Now that you have set up the live streaming data, update the graph by clicking the 'Refresh Data' button. This will update the Plotly graph with the new data received from the Ethereum blockchain node.

To visualize this in more detail, you can use Dash’s built-in dash_core_components library to create a dashboard with several interactive elements:

“ python

import dash_core_components as dcc

from dash import html

Create the dashboard

app = dash.Dash(__name__)

Add widgets to the dashboard

app.layout = html.Div([

html.H1 (Ethereum Live Stream Dashboard),

dcc.Graph(id=’live-websocket-graph’),

dcc.LiveSocket(id=’live-websocket-socket’, stream=True),

])

@app.callback(

[html.Button(‘Refresh Data’, id=’refresh-data-button’)],

[dash.dependencies.Output(‘live-websocket-socket-stream’, ‘data’), dash.dependencies.