fixes
This commit is contained in:
Alagris
parent
f74c686be0
commit
40a8e5fa5c
118
finance.py
118
finance.py
@ -67,7 +67,8 @@ stock_info_in_time_period_df = pd.DataFrame(columns=stock_list, index=["średnia
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mean_prices_and_dividends = fullTableDf[['Close', 'Dividends']].mean(axis=0).unstack(level=0)
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mean_prices_and_dividends_and_market_cap = pd.concat([mean_prices_and_dividends, market_cap], axis=1)
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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')
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mean_prices_and_dividends_figure = px.scatter(mean_prices_and_dividends_and_market_cap.reset_index(), size="marketCap",
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x='Dividends', y='Close', text="index", template='plotly_dark')
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def make_gauge(title, min_v, value, max_v):
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@ -117,14 +118,14 @@ app.layout = html.Div(
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html.Div(
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className='div-for-dropdown',
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children=[
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dcc.Dropdown(id='table_selector',
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dcc.Dropdown(id='table_selector',
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options=get_options(stock_list),
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multi=False, value=selected_stock_in_table,
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style={'backgroundColor': '#1E1E1E'},
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className='stockselector'
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),
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html.Button("Pobierz dane", id="btn_data",style={'margin-top': '3rem'}),
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dcc.Download(id="download-data")
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html.Button("Pobierz dane", id="btn_data", style={'margin-top': '3rem'}),
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dcc.Download(id="download-data")
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],
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style={'color': '#1E1E1E'}),
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@ -147,12 +148,13 @@ app.layout = html.Div(
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html.Div(className='eight columns div-for-charts bg-grey',
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children=[
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dcc.Tabs(id='tabs', value='tab-1', children=[
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dcc.Tab(id='tab-1', label='Chart 1', value='tab-1', children=[
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dcc.Graph(id='timeseries', config={'displayModeBar': False}, animate=True),
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]),
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dcc.Tab(id='tab-2', label='Chart 2', value='tab-2',children=[
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dcc.Graph(id='price-dividends', figure=mean_prices_and_dividends_figure, config={'displayModeBar': False}, animate=True),
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]),
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dcc.Tab(id='tab-1', label='Chart 1', value='tab-1', children=[
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dcc.Graph(id='timeseries', config={'displayModeBar': False}, animate=True),
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]),
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dcc.Tab(id='tab-2', label='Chart 2', value='tab-2', children=[
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dcc.Graph(id='price-dividends', figure=mean_prices_and_dividends_figure,
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config={'displayModeBar': False}, animate=True),
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]),
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]),
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dash_table.DataTable(
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id='stock_price_table',
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@ -167,6 +169,7 @@ app.layout = html.Div(
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]
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)
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# Callback for downloading file
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@app.callback(
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Output("download-data", "data"),
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@ -176,7 +179,8 @@ app.layout = html.Div(
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)
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def create_download_file(n_clicks, selected_table):
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global selected_stock_in_table_df
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return dcc.send_data_frame(selected_stock_in_table_df.to_csv, "data-{table}.csv".format(table = selected_table))
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return dcc.send_data_frame(selected_stock_in_table_df.to_csv, "data-{table}.csv".format(table=selected_table))
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# Callback for timeseries price
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@app.callback(Output('timeseries', 'figure'), [Input('stockselector', 'value')])
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@ -190,60 +194,41 @@ def update_graph(selected_dropdown_value):
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return figure
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@app.callback([Output("stock_price_table", "data"), Output('stock_price_table', 'columns')],
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[Input('table_selector', 'value')])
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def update_table(selected_dropdown_value):
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global selected_stock_in_table
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global selected_stock_in_table_df
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selected_stock_in_table = selected_dropdown_value
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selected_stock_in_table_df = fullTableDf.xs(selected_stock_in_table, axis=1, level=1)
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data = selected_stock_in_table_df.to_dict('records')
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columns = [{"name": i, "id": i} for i in selected_stock_in_table_df.columns]
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return data, columns
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average_gauge = make_gauge('średnia', 0, 0, 400)
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volatility_gauge = make_gauge('wolatylność', 0, 0, 400)
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def round_to_nearest_weekday(date):
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if isinstance(date,pd.Timestamp):
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if date.dayofweek > 5:
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date += datetime.timedelta(days=8-date.dayofweek)
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assert date.dayofweek <= 5
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return date
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elif isinstance(date, str):
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if '.' in date:
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date = datetime.datetime.strptime(date, "%Y-%m-%d %H:%M:%S.%f")
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elif ':' in date:
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date = datetime.datetime.strptime(date, "%Y-%m-%d %H:%M:%S")
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else:
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date = datetime.datetime.strptime(date, "%Y-%m-%d")
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if date.isoweekday() > 5:
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date += datetime.timedelta(days=8-date.isoweekday())
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assert date.isoweekday() <= 5
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return date
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@app.callback([Output('average_gauge', 'figure'),
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@app.callback([Output("stock_price_table", "data"),
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Output('stock_price_table', 'columns'),
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Output('average_gauge', 'figure'),
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Output('volatility_gauge', 'figure'),
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Output('info_in_time_period', 'data')],
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Input('timeseries', 'relayoutData'))
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def change_time_period(selectedData):
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[Input('timeseries', 'relayoutData'),
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Input('table_selector', 'value')])
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def common_table_callback(callback_data_time_period, callback_data_table_selector):
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global selected_stock_in_table
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global selected_stock_in_table_df
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global from_time
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global to_time
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global average_gauge
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global volatility_gauge
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global stock_info_in_time_period_df
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if selectedData is not None:
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if "xaxis.range[0]" in selectedData and "xaxis.range[1]" in selectedData:
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from_time = selectedData["xaxis.range[0]"]
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to_time = selectedData["xaxis.range[1]"]
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selected_stock_in_table_changed = False
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change_time_period = False
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for trigger in dash.callback_context.triggered:
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if trigger['prop_id'] == 'table_selector.value':
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selected_stock_in_table_changed = True
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if trigger['prop_id'] == 'timeseries.relayoutData':
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change_time_period = True
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if selected_stock_in_table_changed:
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selected_stock_in_table = callback_data_table_selector
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selected_stock_in_table_df = fullTableDf.xs(selected_stock_in_table, axis=1, level=1)
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if change_time_period:
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if "xaxis.range[0]" in callback_data_time_period and "xaxis.range[1]" in callback_data_time_period:
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from_time = callback_data_time_period["xaxis.range[0]"]
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to_time = callback_data_time_period["xaxis.range[1]"]
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else:
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from_time = selected_stock_in_table_df.index.min()
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to_time = selected_stock_in_table_df.index.max()
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from_time = round_to_nearest_weekday(from_time)
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to_time = round_to_nearest_weekday(to_time)
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if change_time_period or selected_stock_in_table_changed:
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full_table_in_time_period = fullTableDf.loc[from_time:to_time]
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mean = full_table_in_time_period.mean(axis=0)
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mean = mean.xs('Close', level=0)
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@ -255,9 +240,34 @@ def change_time_period(selectedData):
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# TODO: oblicz stock_info_in_time_period_df tutuaj !!!
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average_gauge = make_gauge('średnia', 0, mean['Close'], 400)
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volatility_gauge = make_gauge('wolatylność', 0, std['Close'], 400)
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return average_gauge, volatility_gauge, stock_info_in_time_period_df.T.to_dict('records')
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stock_price_table_data = selected_stock_in_table_df.to_dict('records')
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stock_price_table_columns = [{"name": i, "id": i} for i in selected_stock_in_table_df.columns]
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return stock_price_table_data, stock_price_table_columns, average_gauge, volatility_gauge, stock_info_in_time_period_df.T.to_dict(
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'records')
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average_gauge = make_gauge('średnia', 0, 0, 400)
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volatility_gauge = make_gauge('wolatylność', 0, 0, 400)
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def round_to_nearest_weekday(date):
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if isinstance(date, pd.Timestamp):
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if date.dayofweek > 5:
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date += datetime.timedelta(days=8 - date.dayofweek)
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assert date.dayofweek <= 5
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return date
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elif isinstance(date, str):
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if '.' in date:
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date = datetime.datetime.strptime(date, "%Y-%m-%d %H:%M:%S.%f")
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elif ':' in date:
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date = datetime.datetime.strptime(date, "%Y-%m-%d %H:%M:%S")
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else:
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date = datetime.datetime.strptime(date, "%Y-%m-%d")
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if date.isoweekday() > 5:
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date += datetime.timedelta(days=8 - date.isoweekday())
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assert date.isoweekday() <= 5
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return date
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if __name__ == '__main__':
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app.run_server(debug=True)
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