gauges working

finance.py

@@ -6,7 +6,11 @@ import plotly.express as px
 import yfinance as yf
 from dash.dependencies import Input, Output
 import plotly.graph_objects as go
+import numpy as np
 import pandas as pd
+import datetime
+import time
+
 # 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',
@@ -57,6 +61,7 @@ to_time = None
 # Initialize the app
 app = dash.Dash(__name__)
 app.config.suppress_callback_exceptions = True
+stock_info_in_time_period_df = pd.DataFrame(columns=stock_list, index=["średnia", "cena", "dywidenda", "wolatylność"])
 
 
 def make_gauge(title, min_v, value, max_v):
@@ -116,7 +121,15 @@ app.layout = html.Div(
                                       style={'color': '#1E1E1E'}),
                                   html.P('Wybierz przedział czasu by policzyć średnie i wachania'),
                                   dcc.Graph(id='average_gauge'),
-                                  dcc.Graph(id='volatility_gauge')
+                                  dcc.Graph(id='volatility_gauge'),
+                                  dash_table.DataTable(
+                                      id='info_in_time_period',
+                                      style_header={'backgroundColor': 'rgb(30, 30, 30)'},
+                                      style_cell={
+                                          'backgroundColor': 'rgb(50, 50, 50)',
+                                          'color': 'white'
+                                      },
+                                  )
                               ]),
                      html.Div(className='eight columns div-for-charts bg-grey',
                               children=[
@@ -159,21 +172,55 @@ def update_table(selected_dropdown_value):
     return data, columns
 
 
-@app.callback([Output('average_gauge', 'figure'), Output('volatility_gauge', 'figure')],
+average_gauge = make_gauge('średnia', 0, 0, 400)
+volatility_gauge = make_gauge('wolatylność', 0, 0, 400)
+
+
+def round_to_nearest_weekday(date):
+    if isinstance(date,pd.Timestamp):
+        if date.dayofweek > 5:
+            date += datetime.timedelta(days=8-date.dayofweek)
+        assert date.dayofweek <= 5
+        return date
+    elif isinstance(date, str):
+        if '.' in date:
+            date = datetime.datetime.strptime(date, "%Y-%m-%d %H:%M:%S.%f")
+        elif ':' in date:
+            date = datetime.datetime.strptime(date, "%Y-%m-%d %H:%M:%S")
+        else:
+            date = datetime.datetime.strptime(date, "%Y-%m-%d")
+        if date.isoweekday() > 5:
+            date += datetime.timedelta(days=8-date.isoweekday())
+        assert date.isoweekday() <= 5
+        return date
+
+
+@app.callback([Output('average_gauge', 'figure'),
+               Output('volatility_gauge', 'figure'),
+               Output('info_in_time_period', 'data')],
               Input('timeseries', 'relayoutData'))
 def change_time_period(selectedData):
     global from_time
     global to_time
+    global average_gauge
+    global volatility_gauge
+    global stock_info_in_time_period_df
     if selectedData is not None:
-        if selectedData.get('autosize'):
+        if "xaxis.range[0]" in selectedData and "xaxis.range[1]" in selectedData:
-            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)
+        else:
-    volatility_gauge = make_gauge('wolatylność', 0, 133, 400)
+            from_time = selected_stock_in_table_df.index.min()
-    return average_gauge, volatility_gauge
+            to_time = selected_stock_in_table_df.index.max()
+        from_time = round_to_nearest_weekday(from_time)
+        to_time = round_to_nearest_weekday(to_time)
+        time_period = selected_stock_in_table_df.loc[from_time:to_time]
+        mean = time_period.mean(axis=0)
+        std = time_period.std(axis=0)
+        # TODO: oblicz stock_info_in_time_period_df tutuaj !!!
+        average_gauge = make_gauge('średnia', 0, mean['Close'], 400)
+        volatility_gauge = make_gauge('wolatylność', 0, std['Close'], 400)
+    return average_gauge, volatility_gauge, stock_info_in_time_period_df.T.to_dict('records')
 
 
 if __name__ == '__main__':