diff --git a/finance.py b/finance.py
index 01c329a..e471fe6 100755
--- a/finance.py
+++ b/finance.py
@@ -5,7 +5,8 @@ import dash_table
 import plotly.express as px
 import yfinance as yf
 from dash.dependencies import Input, Output
-
+import plotly.graph_objects as go
+import pandas as pd
 # Load data
 stock_list = ['MMM', 'ABT', 'ABBV', 'ABMD', 'ACN', 'ATVI', 'ADBE', 'AMD', 'AAP', 'AES', 'AFL', 'A', 'APD', 'AKAM',
               'ALK', 'ALB', 'ARE', 'ALXN', 'ALGN', 'ALLE', 'LNT', 'ALL', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCR', 'AEE',
@@ -51,11 +52,29 @@ selected_stocks_in_graph = [stock_list[0]]
 selected_stocks_in_graph_df = closePrices[selected_stocks_in_graph]
 selected_stock_in_table = stock_list[0]
 selected_stock_in_table_df = fullTableDf.xs(selected_stock_in_table, axis=1, level=1)
+from_time = None
+to_time = None
 # Initialize the app
 app = dash.Dash(__name__)
 app.config.suppress_callback_exceptions = True
 
 
+def make_gauge(title, min_v, value, max_v):
+    gauge_size = 180
+    fig = go.Figure(go.Indicator(
+        mode="gauge+number",
+        value=value,
+        domain={'x': [0, 1], 'y': [0, 1]},
+        title={'text': title}))
+    fig.layout.height = gauge_size
+    fig.layout.width = gauge_size
+    fig.layout.margin = dict(l=2, r=2, t=10, b=2)
+    fig.layout.paper_bgcolor = "#1E1E1E"
+    fig.layout.font.color = "white"
+    fig.layout.font.family = "Arial"
+    return fig
+
+
 def get_options(list_stocks):
     dict_list = []
     for i in list_stocks:
@@ -76,7 +95,8 @@ app.layout = html.Div(
                                       children=[
                                           dcc.Dropdown(id='stockselector',
                                                        options=get_options(stock_list),
-                                                       multi=True, value=selected_stocks_in_graph,
+                                                       multi=True,
+                                                       value=selected_stocks_in_graph,
                                                        style={'backgroundColor': '#1E1E1E'},
                                                        className='stockselector'
                                                        )
@@ -93,10 +113,11 @@ app.layout = html.Div(
                                                        className='stockselector'
                                                        )
                                       ],
-                                      style={'color': '#1E1E1E'})
-
-                              ]
-                              ),
+                                      style={'color': '#1E1E1E'}),
+                                  html.P('Wybierz przedział czasu by policzyć średnie i wachania'),
+                                  dcc.Graph(id='average_gauge'),
+                                  dcc.Graph(id='volatility_gauge')
+                              ]),
                      html.Div(className='eight columns div-for-charts bg-grey',
                               children=[
                                   dcc.Graph(id='timeseries', config={'displayModeBar': False}, animate=True),
@@ -122,6 +143,7 @@ def update_graph(selected_dropdown_value):
     selected_stocks_in_graph = selected_dropdown_value
     selected_stocks_in_graph_df = closePrices[selected_stocks_in_graph]
     figure = px.line(selected_stocks_in_graph_df, template='plotly_dark', title='Stock Prices')
+    # figure.update_layout(clickmode='event+select')
     return figure
 
 
@@ -137,5 +159,21 @@ def update_table(selected_dropdown_value):
     return data, columns
 
 
+@app.callback([Output('average_gauge', 'figure'), Output('volatility_gauge', 'figure')],
+              Input('timeseries', 'relayoutData'))
+def change_time_period(selectedData):
+    global from_time
+    global to_time
+    if selectedData.get('autosize'):
+        from_time = selected_stock_in_table_df.index.min()
+        to_time = selected_stock_in_table_df.index.max()
+    else:
+        from_time = selectedData["xaxis.range[0]"]
+        to_time = selectedData["xaxis.range[1]"]
+    average_gauge = make_gauge('średnia', 0, 290, 400)
+    volatility_gauge = make_gauge('wolatylność', 0, 133, 400)
+    return average_gauge, volatility_gauge
+
+
 if __name__ == '__main__':
     app.run_server(debug=True)