I’m new to building Dash apps. The idea is that a user inputs as many stock tickers separated by commas as they wish. The result is a table displaying the few columns associated with the tickers.
The loop runs as designed outside the Dash framework. Below is the loop:
from yahoo_fin.stock_info import get_analysts_info, get_stats, get_live_price, get_quote_table
import pandas as pd
tickers = ['aapl', 'ayx']
stocks = pd.DataFrame(columns = ['ticker', 'yahoo_live_price', 'closing_price', '52_Week_High', '52_Week_Low','Percent_above_52_low', 'Percent_below_52_high'])
for ticker in tickers:
df = get_stats(ticker)
df['ticker'] = ticker
df = df.pivot(index = 'ticker', columns = 'Attribute', values = 'Value')
df['closing_price'] = get_quote_table(ticker)['Previous Close']
df['yahoo_live_price'] = get_live_price(ticker)
df = df[['yahoo_live_price','closing_price', '52 Week High 3', '52 Week Low 3']]
df[['52 Week High 3', '52 Week Low 3']] = df[['52 Week High 3', '52 Week Low 3']].astype('float')
df['percent_above_52_low'] = round((((df['closing_price'] - df['52 Week Low 3'])/df['closing_price']))*100,2)
df['percent_below_52_high'] = round((((df['52 Week High 3'] - df['closing_price'])/df['52 Week High 3']))*100,2)
df = df.reset_index()
df.columns = ('ticker', 'yahoo_live_price', 'closing_price', '52_Week_High', '52_Week_Low'
, 'Percent_above_52_low', 'Percent_below_52_high')
stocks = stocks.append(df, ignore_index = True)
This gives me exactly what I want. However, this does not work in the Dash environment. Currently the app will display only one ticker and once that is displayed, will not change as the user changes. Does anyone spot what I’m doing wrong? Full dash code below:
from yahoo_fin.stock_info import get_analysts_info, get_stats, get_live_price, get_quote_table
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_table
import pandas as pd
from dash.dependencies import Input, Output
app = dash.Dash()
app.layout = html.Div(children=[
html.Div(children='''
Symbols to grab:
'''),
dcc.Input(id='input', value='', type='text'),
html.Div(id='table'),
])
@app.callback(
Output(component_id='table', component_property='children'),
[Input(component_id='input', component_property='value')]
)
def update_value(input_data):
tickers = [input_data]
stocks = pd.DataFrame(columns = ['ticker', 'yahoo_live_price', 'closing_price', '52_Week_High', '52_Week_Low','Percent_above_52_low', 'Percent_below_52_high'])
for ticker in tickers:
df = get_stats(ticker)
df['ticker'] = ticker
df = df.pivot(index = 'ticker', columns = 'Attribute', values = 'Value')
df['closing_price'] = get_quote_table(ticker)['Previous Close']
df['yahoo_live_price'] = get_live_price(ticker)
df = df[['yahoo_live_price','closing_price', '52 Week High 3', '52 Week Low 3']]
df[['52 Week High 3', '52 Week Low 3']] = df[['52 Week High 3', '52 Week Low 3']].astype('float')
df['percent_above_52_low'] = round((((df['closing_price'] - df['52 Week Low 3'])/df['closing_price']))*100,2)
df['percent_below_52_high'] = round((((df['52 Week High 3'] - df['closing_price'])/df['52 Week High 3']))*100,2)
df = df.reset_index()
df.columns = ('ticker', 'yahoo_live_price', 'closing_price', '52_Week_High', '52_Week_Low'
, 'Percent_above_52_low', 'Percent_below_52_high')
stocks = stocks.append(df, ignore_index = True)
return dash_table.DataTable(
id='table',
columns=[{"name": i, "id": i} for i in stocks.columns],
data=df.to_dict('records'),
)
if __name__ == '__main__':
app.run_server(debug = False)