ai
  • Crypto News
  • Ai
  • eSports
  • Bitcoin
  • Ethereum
  • Blockchain
Home»Ai»How to Design an Interactive Dash and Plotly Dashboard with Callback Mechanisms for Local and Online Deployment?
Ai

How to Design an Interactive Dash and Plotly Dashboard with Callback Mechanisms for Local and Online Deployment?

Share
Facebook Twitter LinkedIn Pinterest Email
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])


app.layout = dbc.Container([
   dbc.Row([
       dbc.Col([
           html.H1("📊 Advanced Financial Dashboard", className="text-center mb-4"),
           html.P(f"Interactive dashboard with {len(df)} data points across {len(stock_names)} stocks",
                  className="text-center text-muted"),
           html.Hr()
       ])
   ]),
  
   dbc.Row([
       dbc.Col([
           dbc.Card([
               dbc.CardBody([
                   html.H5("🎛️ Dashboard Controls", className="card-title"),
                  
                   html.Label("Select Stocks:", className="fw-bold mt-3"),
                   dcc.Dropdown(
                       id='stock-dropdown',
                       options=[{'label': f'{stock} ({base_prices[stock]})', 'value': stock}
                               for stock in stock_names],
                       value=['AAPL', 'GOOGL'], 
                       multi=True,
                       placeholder="Choose stocks to analyze..."
                   ),
                  
                   html.Label("Date Range:", className="fw-bold mt-3"),
                   dcc.DatePickerRange(
                       id='date-picker-range',
                       start_date="2023-06-01",
                       end_date="2024-06-01",
                       display_format="YYYY-MM-DD",
                       style={'width': '100%'}
                   ),
                  
                   html.Label("Chart Style:", className="fw-bold mt-3"),
                   dcc.RadioItems(
                       id='chart-type',
                       options=[
                           {'label': ' Line Chart', 'value': 'line'},
                           {'label': ' Area Chart', 'value': 'area'},
                           {'label': ' Scatter Plot', 'value': 'scatter'}
                       ],
                       value="line",
                       labelStyle={'display': 'block', 'margin': '5px'}
                   ),
                  
                   dbc.Checklist(
                       id='show-ma',
                       options=[{'label': ' Show Moving Average', 'value': 'show'}],
                       value=[],
                       style={'margin': '10px 0'}
                   ),
               ])
           ], className="h-100")
       ], width=3),
      
       dbc.Col([
           dbc.Card([
               dbc.CardHeader("📈 Stock Price Analysis"),
               dbc.CardBody([
                   dcc.Graph(id='main-chart', style={'height': '450px'})
               ])
           ])
       ], width=9)
   ], className="mb-4"),
  
   dbc.Row([
       dbc.Col([
           dbc.Card([
               dbc.CardBody([
                   html.H4(id="avg-price", className="text-primary mb-0"),
                   html.Small("Average Price", className="text-muted")
               ])
           ])
       ], width=3),
       dbc.Col([
           dbc.Card([
               dbc.CardBody([
                   html.H4(id="total-volume", className="text-success mb-0"),
                   html.Small("Total Volume", className="text-muted")
               ])
           ])
       ], width=3),
       dbc.Col([
           dbc.Card([
               dbc.CardBody([
                   html.H4(id="price-range", className="text-info mb-0"),
                   html.Small("Price Range", className="text-muted")
               ])
           ])
       ], width=3),
       dbc.Col([
           dbc.Card([
               dbc.CardBody([
                   html.H4(id="data-points", className="text-warning mb-0"),
                   html.Small("Data Points", className="text-muted")
               ])
           ])
       ], width=3)
   ], className="mb-4"),
  
   dbc.Row([
       dbc.Col([
           dbc.Card([
               dbc.CardHeader("📊 Trading Volume"),
               dbc.CardBody([
                   dcc.Graph(id='volume-chart', style={'height': '300px'})
               ])
           ])
       ], width=6),
       dbc.Col([
           dbc.Card([
               dbc.CardHeader("📉 Returns Distribution"),
               dbc.CardBody([
                   dcc.Graph(id='returns-chart', style={'height': '300px'})
               ])
           ])
       ], width=6)
   ], className="mb-4"),
  
   dbc.Row([
       dbc.Col([
           dbc.Card([
               dbc.CardHeader("📋 Latest Stock Data"),
               dbc.CardBody([
                   dash_table.DataTable(
                       id='data-table',
                       columns=[
                           {'name': 'Stock', 'id': 'Stock'},
                           {'name': 'Date', 'id': 'Date'},
                           {'name': 'Price ($)', 'id': 'Price', 'type': 'numeric',
                            'format': {'specifier': '.2f'}},
                           {'name': 'Volume', 'id': 'Volume', 'type': 'numeric',
                            'format': {'specifier': ',.0f'}},
                           {'name': 'Daily Return (%)', 'id': 'Returns', 'type': 'numeric',
                            'format': {'specifier': '.2%'}}
                       ],
                       style_cell={'textAlign': 'center', 'fontSize': '14px', 'padding': '10px'},
                       style_header={'backgroundColor': 'rgb(230, 230, 230)', 'fontWeight': 'bold'},
                       style_data_conditional=[
                           {
                               'if': {'filter_query': '{Returns} > 0'},
                               'backgroundColor': '#d4edda'
                           },
                           {
                               'if': {'filter_query': '{Returns} 
Share. Facebook Twitter Pinterest LinkedIn Tumblr Email

Related Posts

This AI Research Proposes an AI Agent Immune System for Adaptive Cybersecurity: 3.4× Faster Containment with

septembre 28, 2025

The Latest Gemini 2.5 Flash-Lite Preview is Now the Fastest Proprietary Model (External Tests) and 50% Fewer Output Tokens

septembre 27, 2025

How to Build an Intelligent AI Desktop Automation Agent with Natural Language Commands and Interactive Simulation?

septembre 27, 2025

US investigators are using AI to detect child abuse images made by AI

septembre 26, 2025
Add A Comment

Comments are closed.

Top Posts

SwissCryptoDaily.ch delivers the latest cryptocurrency news, market insights, and expert analysis. Stay informed with daily updates from the world of blockchain and digital assets.

We're social. Connect with us:

Facebook X (Twitter) Instagram Pinterest YouTube
Top Insights

Flood of Major Altcoin ETFs Await SEC Approval in October

septembre 29, 2025

“That’s it for MK1 for me”: SonicFox left frustrated after Final Kombat 2025 tournament

septembre 29, 2025

How to Design an Interactive Dash and Plotly Dashboard with Callback Mechanisms for Local and Online Deployment?

septembre 29, 2025
Get Informed

Subscribe to Updates

Get the latest creative news from FooBar about art, design and business.

Facebook X (Twitter) Instagram Pinterest
  • About us
  • Get In Touch
  • Cookies Policy
  • Privacy-Policy
  • Terms and Conditions
© 2025 Swisscryptodaily.ch.

Type above and press Enter to search. Press Esc to cancel.