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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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96
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_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):
@ -151,7 +152,8 @@ app.layout = html.Div(
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.Graph(id='price-dividends', figure=mean_prices_and_dividends_figure,
config={'displayModeBar': False}, animate=True),
]),
]),
dash_table.DataTable(
@ -167,6 +169,7 @@ app.layout = html.Div(
]
)
# Callback for downloading file
@app.callback(
Output("download-data", "data"),
@ -178,6 +181,7 @@ 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))
# Callback for timeseries price
@app.callback(Output('timeseries', 'figure'), [Input('stockselector', 'value')])
def update_graph(selected_dropdown_value):
@ -190,16 +194,56 @@ 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
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)
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
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:
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)
# 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]
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)
@ -225,39 +269,5 @@ def round_to_nearest_weekday(date):
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)
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)
# 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')
if __name__ == '__main__':
app.run_server(debug=True)