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fixes

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Alagris 2021-06-22 13:02:23 +02:00
parent f74c686be0
commit 40a8e5fa5c
1 changed files with 64 additions and 54 deletions
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118
finance.py
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@ -67,7 +67,8 @@ stock_info_in_time_period_df = pd.DataFrame(columns=stock_list, index=["średnia
mean_prices_and_dividends = fullTableDf[['Close', 'Dividends']].mean(axis=0).unstack(level=0) 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_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') 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): def make_gauge(title, min_v, value, max_v):
@ -117,14 +118,14 @@ app.layout = html.Div(
html.Div( html.Div(
className='div-for-dropdown', className='div-for-dropdown',
children=[ children=[
dcc.Dropdown(id='table_selector', dcc.Dropdown(id='table_selector',
options=get_options(stock_list), options=get_options(stock_list),
multi=False, value=selected_stock_in_table, multi=False, value=selected_stock_in_table,
style={'backgroundColor': '#1E1E1E'}, style={'backgroundColor': '#1E1E1E'},
className='stockselector' 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") dcc.Download(id="download-data")
], ],
style={'color': '#1E1E1E'}), style={'color': '#1E1E1E'}),
@ -147,12 +148,13 @@ app.layout = html.Div(
html.Div(className='eight columns div-for-charts bg-grey', html.Div(className='eight columns div-for-charts bg-grey',
children=[ children=[
dcc.Tabs(id='tabs', value='tab-1', children=[ dcc.Tabs(id='tabs', value='tab-1', children=[
dcc.Tab(id='tab-1', label='Chart 1', 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.Graph(id='timeseries', config={'displayModeBar': False}, animate=True),
]), ]),
dcc.Tab(id='tab-2', label='Chart 2', value='tab-2',children=[ 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.Graph(id='price-dividends', figure=mean_prices_and_dividends_figure,
]), config={'displayModeBar': False}, animate=True),
]),
]), ]),
dash_table.DataTable( dash_table.DataTable(
id='stock_price_table', id='stock_price_table',
@ -167,6 +169,7 @@ app.layout = html.Div(
] ]
) )
# Callback for downloading file # Callback for downloading file
@app.callback( @app.callback(
Output("download-data", "data"), Output("download-data", "data"),
@ -176,7 +179,8 @@ app.layout = html.Div(
) )
def create_download_file(n_clicks, selected_table): def create_download_file(n_clicks, selected_table):
global selected_stock_in_table_df 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 # Callback for timeseries price
@app.callback(Output('timeseries', 'figure'), [Input('stockselector', 'value')]) @app.callback(Output('timeseries', 'figure'), [Input('stockselector', 'value')])
@ -190,60 +194,41 @@ def update_graph(selected_dropdown_value):
return figure return figure
@app.callback([Output("stock_price_table", "data"), Output('stock_price_table', 'columns')], @app.callback([Output("stock_price_table", "data"),
[Input('table_selector', 'value')]) Output('stock_price_table', 'columns'),
def update_table(selected_dropdown_value): Output('average_gauge', 'figure'),
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
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('volatility_gauge', 'figure'),
Output('info_in_time_period', 'data')], Output('info_in_time_period', 'data')],
Input('timeseries', 'relayoutData')) [Input('timeseries', 'relayoutData'),
def change_time_period(selectedData): 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
global from_time global from_time
global to_time global to_time
global average_gauge global average_gauge
global volatility_gauge global volatility_gauge
global stock_info_in_time_period_df global stock_info_in_time_period_df
if selectedData is not None: selected_stock_in_table_changed = False
if "xaxis.range[0]" in selectedData and "xaxis.range[1]" in selectedData: change_time_period = False
from_time = selectedData["xaxis.range[0]"] for trigger in dash.callback_context.triggered:
to_time = selectedData["xaxis.range[1]"] 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: else:
from_time = selected_stock_in_table_df.index.min() from_time = selected_stock_in_table_df.index.min()
to_time = selected_stock_in_table_df.index.max() to_time = selected_stock_in_table_df.index.max()
from_time = round_to_nearest_weekday(from_time) from_time = round_to_nearest_weekday(from_time)
to_time = round_to_nearest_weekday(to_time) to_time = round_to_nearest_weekday(to_time)
if change_time_period or selected_stock_in_table_changed:
full_table_in_time_period = fullTableDf.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 = full_table_in_time_period.mean(axis=0)
mean = mean.xs('Close', level=0) mean = mean.xs('Close', level=0)
@ -255,9 +240,34 @@ def change_time_period(selectedData):
# TODO: oblicz stock_info_in_time_period_df tutuaj !!! # TODO: oblicz stock_info_in_time_period_df tutuaj !!!
average_gauge = make_gauge('średnia', 0, mean['Close'], 400) average_gauge = make_gauge('średnia', 0, mean['Close'], 400)
volatility_gauge = make_gauge('wolatylność', 0, std['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') 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]
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)
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
if __name__ == '__main__': if __name__ == '__main__':
app.run_server(debug=True) app.run_server(debug=True)