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s434749:comp-descriptions
s434749:file-styles-new-graph
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s434749:master
s434749:comp-descriptions
s434749:file-styles-new-graph

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master ... file-style

2 changed files with 57 additions and 117 deletions
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8
assets/style.css
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@ -645,7 +645,7 @@ a {
flex-grow: 1;
}
#tab-1, #tab-2, #tab-3{
#tab-1, #tab-2{
background-color: #1E1E1E;
color: #fff;
border-left: none;
@ -656,9 +656,3 @@ a {
.tab--selected{
background-color: #111111 !important;
}
#company-desritpion{
padding: 3rem 1rem;
margin: 0;
background-color: #111111;
}

152
finance.py
View File

@ -10,9 +10,6 @@ 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',
@ -53,7 +50,6 @@ 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]]
@ -65,13 +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(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')
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):
@ -127,7 +117,7 @@ app.layout = html.Div(
style={'backgroundColor': '#1E1E1E'},
className='stockselector'
),
html.Button("Pobierz dane", id="btn_data", style={'margin-top': '3rem'}),
html.Button("Pobierz dane", id="btn_data",style={'margin-top': '3rem'}),
dcc.Download(id="download-data")
],
@ -141,30 +131,11 @@ 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',
@ -173,12 +144,8 @@ app.layout = html.Div(
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"),
dcc.Tab(id='tab-2', label='Chart 2', value='tab-2',children=[
dcc.Graph(id='price-dividends', config={'displayModeBar': False}, animate=True),
]),
]),
dash_table.DataTable(
@ -194,24 +161,6 @@ 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"),
@ -221,8 +170,7 @@ def update_graph(selected_dropdown_value):
)
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')])
@ -236,52 +184,16 @@ def update_graph(selected_dropdown_value):
return figure
@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):
@app.callback([Output("stock_price_table", "data"), Output('stock_price_table', 'columns')],
[Input('table_selector', 'value')])
def update_table(selected_dropdown_value):
global selected_stock_in_table
global selected_stock_in_table_df
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 = selected_dropdown_value
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
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
average_gauge = make_gauge('średnia', 0, 0, 400)
@ -289,9 +201,9 @@ 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):
@ -302,10 +214,44 @@ 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)