trackery

Reviewed-on: #2

assets/style.css

@@ -645,7 +645,7 @@ a {
   flex-grow: 1;
 }
 
-#tab-1, #tab-2{
+#tab-1, #tab-2, #tab-3{
   background-color: #1E1E1E;
   color: #fff;
   border-left: none;
@@ -655,4 +655,10 @@ a {
 
 .tab--selected{
   background-color: #111111 !important;
+}
+
+#company-desritpion{
+  padding: 3rem 1rem;
+  margin: 0;
+  background-color: #111111;
 }

finance.py

@@ -10,6 +10,9 @@ import numpy as np
 import pandas as pd
 import datetime
 import time
+from pandas_datareader import data
+import requests
+from bs4 import BeautifulSoup
 
 # Load data
 stock_list = ['MMM', 'ABT', 'ABBV', 'ABMD', 'ACN', 'ATVI', 'ADBE', 'AMD', 'AAP', 'AES', 'AFL', 'A', 'APD', 'AKAM',
@@ -50,6 +53,7 @@ stock_list = ['MMM', 'ABT', 'ABBV', 'ABMD', 'ACN', 'ATVI', 'ADBE', 'AMD', 'AAP',
 stock_list = ['WHR', 'WMB', 'WLTW', 'WYNN', 'XEL', 'XLNX', 'XYL', 'YUM', 'ZBRA', 'ZBH', 'ZION', 'ZTS']
 
 tickerData = yf.Tickers(stock_list)
+market_cap = data.get_quote_yahoo(stock_list)['marketCap']
 fullTableDf = tickerData.history(period='1d', start='2019-1-1', end='2020-1-25')
 closePrices = fullTableDf['Close']
 selected_stocks_in_graph = [stock_list[0]]
@@ -61,7 +65,13 @@ 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"])
+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
+
+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)
+mean_prices_and_dividends_figure = px.scatter(mean_prices_and_dividends_and_market_cap.reset_index(), size="marketCap",
+                                              x='Dividends', y='Close', text="index", template='plotly_dark')
 
 
 def make_gauge(title, min_v, value, max_v):
@@ -111,14 +121,14 @@ app.layout = html.Div(
                                   html.Div(
                                       className='div-for-dropdown',
                                       children=[
-                                        dcc.Dropdown(id='table_selector',
+                                          dcc.Dropdown(id='table_selector',
                                                        options=get_options(stock_list),
                                                        multi=False, value=selected_stock_in_table,
                                                        style={'backgroundColor': '#1E1E1E'},
                                                        className='stockselector'
                                                        ),
-                                        html.Button("Pobierz dane", id="btn_data",style={'margin-top': '3rem'}), 
-                                        dcc.Download(id="download-data")
+                                          html.Button("Pobierz dane", id="btn_data", style={'margin-top': '3rem'}),
+                                          dcc.Download(id="download-data")
 
                                       ],
                                       style={'color': '#1E1E1E'}),
@@ -131,22 +141,45 @@ 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',
                               children=[
                                   dcc.Tabs(id='tabs', value='tab-1', children=[
-                                    dcc.Tab(id='tab-1', label='Chart 1', value='tab-1', children=[
-                                        dcc.Graph(id='timeseries', config={'displayModeBar': False}, animate=True),
-                                    ]),
-                                    dcc.Tab(id='tab-2', label='Chart 2', value='tab-2',children=[
-                                        dcc.Graph(id='price-dividends', config={'displayModeBar': False}, animate=True),
-                                    ]),
+                                      dcc.Tab(id='tab-1', label='Chart 1', value='tab-1', children=[
+                                          dcc.Graph(id='timeseries', config={'displayModeBar': False}, animate=True),
+                                      ]),
+                                      dcc.Tab(id='tab-2', label='Chart 2', value='tab-2', children=[
+                                          dcc.Graph(id='price-dividends', figure=mean_prices_and_dividends_figure,
+                                                    config={'displayModeBar': False}, animate=True),
+                                      ]),
+                                      dcc.Tab(id='tab-3', label='Copmany description', value='tab-3', children=[
+                                          html.Div(id="company-desritpion"),
+                                      ]),
                                   ]),
                                   dash_table.DataTable(
                                       id='stock_price_table',
@@ -161,6 +194,24 @@ 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):
+    url = 'https://finance.yahoo.com/quote/{company}/profile?p={company}'.format(company=selected_dropdown_value)
+    page = requests.get(url)
+    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))
+
+
 # Callback for downloading file
 @app.callback(
     Output("download-data", "data"),
@@ -170,7 +221,8 @@ app.layout = html.Div(
 )
 def create_download_file(n_clicks, selected_table):
     global selected_stock_in_table_df
-    return dcc.send_data_frame(selected_stock_in_table_df.to_csv, "data-{table}.csv".format(table = selected_table))
+    return dcc.send_data_frame(selected_stock_in_table_df.to_csv, "data-{table}.csv".format(table=selected_table))
+
 
 # Callback for timeseries price
 @app.callback(Output('timeseries', 'figure'), [Input('stockselector', 'value')])
@@ -184,16 +236,52 @@ def update_graph(selected_dropdown_value):
     return figure
 
 
-@app.callback([Output("stock_price_table", "data"), Output('stock_price_table', 'columns')],
-              [Input('table_selector', 'value')])
-def update_table(selected_dropdown_value):
+@app.callback([Output("stock_price_table", "data"),
+               Output('stock_price_table', 'columns'),
+               Output('average_gauge', 'figure'),
+               Output('volatility_gauge', 'figure'),
+               Output('info_in_time_period', 'data')],
+              [Input('timeseries', 'relayoutData'),
+               Input('table_selector', 'value')])
+def common_table_callback(callback_data_time_period, callback_data_table_selector):
     global selected_stock_in_table
     global selected_stock_in_table_df
-    selected_stock_in_table = selected_dropdown_value
-    selected_stock_in_table_df = fullTableDf.xs(selected_stock_in_table, axis=1, level=1)
-    data = selected_stock_in_table_df.to_dict('records')
-    columns = [{"name": i, "id": i} for i in selected_stock_in_table_df.columns]
-    return data, columns
+    global from_time
+    global to_time
+    global average_gauge
+    global volatility_gauge
+    global stock_info_in_time_period_df
+    selected_stock_in_table_changed = False
+    change_time_period = False
+    for trigger in dash.callback_context.triggered:
+        if trigger['prop_id'] == 'table_selector.value':
+            selected_stock_in_table_changed = True
+        if trigger['prop_id'] == 'timeseries.relayoutData':
+            change_time_period = True
+    if selected_stock_in_table_changed:
+        selected_stock_in_table = callback_data_table_selector
+        selected_stock_in_table_df = fullTableDf.xs(selected_stock_in_table, axis=1, level=1)
+    if change_time_period:
+        if "xaxis.range[0]" in callback_data_time_period and "xaxis.range[1]" in callback_data_time_period:
+            from_time = callback_data_time_period["xaxis.range[0]"]
+            to_time = callback_data_time_period["xaxis.range[1]"]
+        else:
+            from_time = selected_stock_in_table_df.index.min()
+            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)
+    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]
+        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)
+    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
 
 
 average_gauge = make_gauge('średnia', 0, 0, 400)
@@ -201,9 +289,9 @@ volatility_gauge = make_gauge('wolatylność', 0, 0, 400)
 
 
 def round_to_nearest_weekday(date):
-    if isinstance(date,pd.Timestamp):
+    if isinstance(date, pd.Timestamp):
         if date.dayofweek > 5:
-            date += datetime.timedelta(days=8-date.dayofweek)
+            date += datetime.timedelta(days=8 - date.dayofweek)
         assert date.dayofweek <= 5
         return date
     elif isinstance(date, str):
@@ -214,44 +302,10 @@ def round_to_nearest_weekday(date):
         else:
             date = datetime.datetime.strptime(date, "%Y-%m-%d")
         if date.isoweekday() > 5:
-            date += datetime.timedelta(days=8-date.isoweekday())
+            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 "xaxis.range[0]" in selectedData and "xaxis.range[1]" in selectedData:
-            from_time = selectedData["xaxis.range[0]"]
-            to_time = selectedData["xaxis.range[1]"]
-        else:
-            from_time = selected_stock_in_table_df.index.min()
-            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')
-
-@app.callback(Output('price-dividends', 'figure'),
-              [Input('table_selector', 'value')])
-def update_point_chart(selected_dropdown_value):
-    selected_stock_in_table_df = fullTableDf.xs(selected_dropdown_value, axis=1, level=1)
-    figure = px.scatter(selected_stock_in_table_df, x='Dividends', y='Close',  template='plotly_dark')
-    return figure
-
 if __name__ == '__main__':
     app.run_server(debug=True)