finance.py

@@ -65,8 +65,7 @@ to_time = None
 # Initialize the app
 app = dash.Dash(__name__)
 app.config.suppress_callback_exceptions = True
-stock_info_in_time_period_df = pd.DataFrame(index=stock_list, columns=["tracker", "średnia", "cena"])
-stock_info_in_time_period_df['tracker'] = stock_info_in_time_period_df.index
+stock_info_in_time_period_df = pd.DataFrame(columns=stock_list, index=["średnia", "cena", "dywidenda", "wolatylność"])
 
 mean_prices_and_dividends = fullTableDf[['Close', 'Dividends']].mean(axis=0).unstack(level=0)
 mean_prices_and_dividends_and_market_cap = pd.concat([mean_prices_and_dividends, market_cap], axis=1)
@@ -141,30 +140,11 @@ app.layout = html.Div(
                                       ]),
                                   dash_table.DataTable(
                                       id='info_in_time_period',
-                                      columns=[{"name": i, "id": i} for i in stock_info_in_time_period_df.columns],
                                       style_header={'backgroundColor': 'rgb(30, 30, 30)'},
                                       style_cell={
                                           'backgroundColor': 'rgb(50, 50, 50)',
                                           'color': 'white'
                                       },
-                                      style_data_conditional=[
-                                        {
-                                            'if': {
-                                                'filter_query': '{cena} > {średnia}',
-                                                'column_id': 'tracker'
-                                            },
-                                            'backgroundColor': 'green',
-                                            'color': 'black'
-                                        },
-                                        {
-                                            'if': {
-                                                'filter_query': '{cena} <= {średnia}',
-                                                'column_id': 'tracker'
-                                            },
-                                            'backgroundColor': 'red',
-                                            'color': 'black'
-                                        },
-                                      ]
                                   )
                               ]),
                      html.Div(className='eight columns div-for-charts bg-grey',
@@ -195,6 +175,7 @@ app.layout = html.Div(
 )
 
 
+
 # Callback for scraping company description
 @app.callback(Output('company-desritpion', 'children'), [Input('table_selector', 'value')])
 def update_graph(selected_dropdown_value):
@@ -203,14 +184,15 @@ def update_graph(selected_dropdown_value):
     soup = BeautifulSoup(page.content, 'html.parser')
     comp_name = soup.find(id="Main").h3.text
     description = soup.find_all('section', class_='quote-sub-section')[0].p.text
-
+    
     return dcc.Markdown('''
 
     # [{comp_name}]({url})
 
     {description}
-        '''.format(comp_name=comp_name, description=description, url=url))
+        '''.format(comp_name=comp_name,description=description,url=url))  
 
+   
 
 # Callback for downloading file
 @app.callback(
@@ -271,17 +253,21 @@ def common_table_callback(callback_data_time_period, callback_data_table_selecto
         from_time = round_to_nearest_weekday(from_time)
         to_time = round_to_nearest_weekday(to_time)
     if change_time_period or selected_stock_in_table_changed:
-        stock_info_in_time_period_df['średnia'] = fullTableDf['Close'].loc[from_time:to_time].mean(axis=0)
-        stock_info_in_time_period_df['cena'] = fullTableDf['Close'].loc[fullTableDf.index.max()]
-        time_period = selected_stock_in_table_df['Close'].loc[from_time:to_time]
+        full_table_in_time_period = fullTableDf.loc[from_time:to_time]
+        mean = full_table_in_time_period.mean(axis=0)
+        mean = mean.xs('Close', level=0)
+        std = full_table_in_time_period.std(axis=0)
+        std = std.xs('Close', level=0)
+        time_period = selected_stock_in_table_df.loc[from_time:to_time]
         mean = time_period.mean(axis=0)
         std = time_period.std(axis=0)
-        average_gauge = make_gauge('średnia', 0, mean, 400)
-        volatility_gauge = make_gauge('wolatylność', 0, std, 400)
+        # 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)
     stock_price_table_data = selected_stock_in_table_df.to_dict('records')
     stock_price_table_columns = [{"name": i, "id": i} for i in selected_stock_in_table_df.columns]
-    stock_info_in_time_period_data = stock_info_in_time_period_df.to_dict('records')
-    return stock_price_table_data, stock_price_table_columns, average_gauge, volatility_gauge, stock_info_in_time_period_data
+    return stock_price_table_data, stock_price_table_columns, average_gauge, volatility_gauge, stock_info_in_time_period_df.T.to_dict(
+        'records')
 
 
 average_gauge = make_gauge('średnia', 0, 0, 400)