409 lines
18 KiB
Python
409 lines
18 KiB
Python
from datetime import datetime
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import time
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import flask
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import pandas as pd
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import dash_table
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import plotly.graph_objects as go
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import dash_core_components as dcc
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import dash_html_components as html
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from dash import Dash
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from dash_table.Format import Format
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from dash.dependencies import Input, Output
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from plotly.subplots import make_subplots
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import stockdash_loader as sdl
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colorway = ["lightslategray", '#FF4F00', '#375CB1', '#FF7400', '#FFF400', '#FF0056']
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used_columns = ['symbol', 'shortName', 'sector', 'industry', 'country', 'marketCap', 'enterpriseValue', 'dividendRate',
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'trailingPE', 'forwardPE', 'enterpriseToEbitda', 'shortRatio']
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PAGE_SIZE = 20
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app = Dash(__name__)
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server = app.server
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start_time = time.time()
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print("----- Starting STOCKDASH @ %s -----" % datetime.fromtimestamp(start_time))
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kpi_data = sdl.load_from_db(sdl.db_kpi, orderby="marketCap DESC")
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print("Data loaded after %ss" % (time.time()-start_time))
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comp_kpi = kpi_data[used_columns]
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app.layout = html.Div(children=[
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html.Div(className='row',
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children=[html.Div(className='three columns div-user-controls',
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children=[
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html.H2('STOCKDASH'),
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html.P('''Visualising data with Plotly - Dash'''),
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html.P('''Pick one or more KPIs from the dropdown below.'''),
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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='stockselector',
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options=[{'label': i, 'value': i} for i in
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comp_kpi._get_numeric_data().columns],
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multi=True,
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value=[comp_kpi._get_numeric_data().columns[0]],
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style={'backgroundColor': '#1E1E1E'},
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className='stockselector')
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],
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style={'color': '#1E1E1E'}),
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html.P(id="total-stocks"),
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dcc.Markdown(
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children=[
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"Source: [thiessen.io](https://www.thiessen.io)"
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])
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]),
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html.Div(className='nine columns div-for-charts bg-grey',
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style={'padding': 0},
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children=[
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dash_table.DataTable(
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id='company-kpi-data',
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columns=
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[{"name": i, "id": i, 'deletable': True, 'type': 'numeric',
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'format': Format(group=',')} if i in comp_kpi._get_numeric_data().columns
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else {"name": i, "id": i, 'deletable': True} for i in comp_kpi.columns],
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style_as_list_view=True,
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style_data_conditional=[{
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'if': {'column_editable': False},
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'backgroundColor': 'rgba(50, 50, 50, 0.5)',
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'textAlign': 'left',
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'color': 'white',
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'padding': 7
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}],
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style_filter_conditional=[{
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'if': {'column_editable': False},
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'backgroundColor': 'rgba(40, 40, 40,0.5)',
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'textAlign': 'left',
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'color': 'white'
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}],
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style_header_conditional=[{
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'if': {'column_editable': False},
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'backgroundColor': 'rgba(30, 30, 30,0.5)',
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'textAlign': 'left',
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'fontWeight': 'bold',
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'color': 'white'
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}],
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page_current=0,
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page_size=PAGE_SIZE,
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page_action='custom',
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filter_action='custom',
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filter_query='',
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sort_action='custom',
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sort_mode='multi',
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sort_by=[]
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),
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dcc.Graph(
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id='bar-chart-marketcap',
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className='bg-grey',
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hoverData={'points': [{'x': 'AAPL'}]},
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animate=False),
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dcc.Graph(
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id='timeseries-chart-price',
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className='bg-grey',
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config={'displayModeBar': False},
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animate=False),
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dcc.Graph(
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id='recom-bar-chart',
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className='bg-grey',
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config={'displayModeBar': False},
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animate=False)
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])
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])
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])
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@app.callback(Output('bar-chart-marketcap', 'figure'),
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[Input('company-kpi-data', 'data'),
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Input('stockselector', 'value'),
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Input('bar-chart-marketcap', 'clickData')])
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def update_graph(data, selected_columns, clickData):
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used_symbols = [x['symbol'] for x in data]
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figure = go.Figure(
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layout=go.Layout(
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colorway=colorway,
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template='plotly_dark',
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paper_bgcolor='rgba(0, 0, 0, 0)',
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plot_bgcolor='rgba(0, 0, 0, 0)',
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margin={'b': 15},
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hovermode='x',
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autosize=True,
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title={'text': 'Market Data', 'font': {'color': 'white'}, 'x': 0.5}
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))
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val = dict()
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val["xaxis"] = dict(domain=[0.15, 0.85])
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for i, column in enumerate(selected_columns):
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i += 1
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figure.add_trace(go.Bar(name=column,
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x=used_symbols,
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y=[x[column] for x in data],
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marker_color=['lightslategray',] * len(data),
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yaxis='y' + str(i), offsetgroup=i))
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val["yaxis%s" % i] = dict(
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title=column,
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titlefont=dict(color=colorway[i - 1]),
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tickfont=dict(color=colorway[i - 1]),
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)
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if i == 2:
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val["yaxis2"].update(dict(
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anchor="x",
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overlaying="y",
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side="right"
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))
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elif i == 3:
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val["yaxis3"].update(dict(
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anchor="free",
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overlaying="y",
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side="left",
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position=0.10
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))
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elif i == 4:
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val["yaxis4"].update(dict(
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anchor="free",
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overlaying="y",
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side="right",
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position=0.90
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))
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figure.update_layout(val)
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figure.update_yaxes(
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showgrid=True, zeroline=True, zerolinewidth=1, zerolinecolor='White',
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)
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if clickData is not None:
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i = 0
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for subFig in figure['data']:
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color=[colorway[i],] * len(data)
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color[clickData['points'][0]['pointNumber']] = 'crimson'
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subFig['marker']['color'] = color
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i = i +1
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return figure
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@app.callback(Output("total-stocks", "children"),
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[Input('company-kpi-data', 'data')])
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def update_total_stocks(data):
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stocks_picked = len(comp_kpi)
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return "Total Number of Stocks loaded: %s" % stocks_picked
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@app.callback(Output('recom-bar-chart', 'figure'),
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[Input('company-kpi-data', 'data')])
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def update_graph(data):
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used_symbols = [x['symbol'] for x in data]
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# Modify Recommendation Data
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where_clause = "symbol IN ('"+"','".join(used_symbols)+"')"
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rec_data = sdl.load_from_db(sdl.db_rec, where=where_clause)
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rec_data_mod = pd.concat([rec_data, pd.get_dummies(rec_data['To Grade'], prefix='grade')], axis=1)
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rec_data_mod.drop(['To Grade', 'From Grade', 'Action'], axis=1, inplace=True)
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rec_data_mod['Date'] = pd.to_datetime(rec_data_mod['Date'])
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df2 = rec_data_mod.groupby([pd.Grouper(key='Date', freq='Y'), pd.Grouper('symbol')]).agg(['sum'])
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df2['Positive'] = df2['grade_Buy'] + df2['grade_Outperform'] + df2['grade_Market Outperform'] + df2[
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'grade_Overweight'] + df2['grade_Positive'] + df2['grade_Strong Buy']
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df2['Neutral'] = df2['grade_Equal-Weight'] + df2['grade_Hold'] + df2['grade_Neutral']
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df2['Negative'] = df2['grade_Market Underperform'] + df2['grade_Reduce'] + df2['grade_Sell'] + df2[
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'grade_Underweight']
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columns = ['Positive', 'Neutral', 'Negative']
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rec_data_mod = df2[columns]
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df = rec_data_mod.loc['2020-12-31'].reset_index()
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df_tmp = df.loc[df['symbol'].isin(used_symbols)]
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figure = go.Figure(layout=go.Layout(
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colorway=colorway,
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template='plotly_dark',
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paper_bgcolor='rgba(0, 0, 0, 0)',
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plot_bgcolor='rgba(0, 0, 0, 0)',
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margin={'b': 15},
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hovermode='x',
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autosize=True,
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title={'text': 'Recommendation Data', 'font': {'color': 'white'}, 'x': 0.5},
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barmode='stack'
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))
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figure.add_trace(
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go.Bar(x=used_symbols, y=df_tmp['Positive'].tolist(), name='Positive Outlook', marker_color='#41B3A3'))
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figure.add_trace(
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go.Bar(x=used_symbols, y=df_tmp['Neutral'].tolist(), name='Neutral Outlook', marker_color='#E8A87C'))
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figure.add_trace(
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go.Bar(x=used_symbols, y=df_tmp['Negative'].tolist(), name='Negative Outlook', marker_color='#E27D60'))
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return figure
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@app.callback(Output('timeseries-chart-price', 'figure'),
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Input('bar-chart-marketcap', 'clickData'),
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Input('company-kpi-data', 'data'))
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def update_graph(clickData, kpi_data):
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if clickData is None:
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used_symbol = kpi_data[0]['symbol']
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else:
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used_symbol = clickData['points'][0]['x']
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where_clause = "symbol = '%s'" % used_symbol
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his_data = sdl.load_from_db(sdl.db_his, where=where_clause, limit=1000, orderby="Date DESC")
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where_clause = "symbol = '%s' and Date >= '%s'" % (used_symbol, his_data['Date'].min())
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div_data = sdl.load_from_db(sdl.db_div, where=where_clause, orderby="Date DESC")
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# Calculate rolling window
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his_data['priceMA50'] = his_data['Close'].rolling(window=50, min_periods=1).mean()
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std_dev = his_data['Close'].rolling(window=50, min_periods=1).std()
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his_data['priceMA50_lstd'] = his_data['priceMA50'] - 2*std_dev
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his_data['priceMA50_hstd'] = his_data['priceMA50'] + 2*std_dev
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his_data['priceMA200'] = his_data['Close'].rolling(window=200, min_periods=1).mean()
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his_data['diffMA50_200'] = his_data['priceMA50'] - his_data['priceMA200']
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fig = make_subplots(rows=3, cols=1, row_heights=[0.7, 0.2, 0.1], shared_xaxes=True, vertical_spacing=0.07)
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fig.add_trace(go.Candlestick(x=his_data['Date'], open=his_data['Open'], high=his_data['High'], low=his_data['Low'],
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close=his_data['Close'], name=used_symbol), row=1, col=1)
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columns = ['priceMA50', 'priceMA200']
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for column in columns:
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fig.add_trace(go.Scatter(x=his_data['Date'], y=his_data[column], mode='lines', opacity=0.7,
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name=used_symbol + "-" + column, textposition='bottom center'),
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row=1, col=1)
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fig.add_trace(go.Scatter(x=his_data['Date'], y=his_data['priceMA50_lstd'], mode='lines', opacity=0.7, line=dict(color='#ffdd70', width=1, dash='dash'),
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name=used_symbol + "-" + 'Lower Band', textposition='bottom center'),
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row=1, col=1)
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fig.add_trace(go.Scatter(x=his_data['Date'], y=his_data['priceMA50_hstd'], mode='lines', opacity=0.7, line=dict(color='#ffdd70', width=1, dash='dash'),
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name=used_symbol + "-" + 'Higher Band', textposition='bottom center'),
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row=1, col=1)
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fig.update_yaxes(showgrid=True, zeroline=False,
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showspikes=True, spikemode='across', spikesnap='cursor', showline=False, spikedash='solid')
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fig.update_xaxes(showgrid=True, zeroline=False, rangeslider_visible=False, showticklabels=False,
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showspikes=True, spikemode='across', spikesnap='cursor', showline=False, spikedash='solid',
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rangebreaks=[
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dict(bounds=["sat", "mon"]), #hide weekends
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#dict(values=["2015-12-25", "2016-01-01"]) # hide Christmas and New Year's
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])
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fig.update_layout(
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colorway=colorway,
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template='plotly_dark',
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paper_bgcolor='rgba(0, 0, 0, 0)',
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plot_bgcolor='rgba(0, 0, 0, 0)',
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autosize=True,
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height=800,
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hovermode='x unified',
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hoverlabel=dict(font=dict(color='black')),
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title={'text': 'Stock Prices', 'font': {'color': 'white'}, 'x': 0.5},
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xaxis={'range': [his_data['Date'].min(), his_data['Date'].max()],
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'showticklabels': True,
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'rangeselector_bgcolor':'rgba(0, 22, 0, 0)',
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'rangeselector': dict(
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buttons=list([
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dict(count=1,
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label="1m",
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step="month",
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stepmode="backward"),
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dict(count=6,
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label="6m",
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step="month",
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stepmode="backward"),
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dict(count=1,
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label="YTD",
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step="year",
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stepmode="todate"),
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dict(count=1,
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label="1y",
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step="year",
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stepmode="backward"),
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dict(step="all")
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]))},
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yaxis1={'autorange': True, 'fixedrange': False},
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legend=dict(y=1, x=0),
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dragmode='pan')
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fig.add_trace(
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go.Bar(x=his_data['Date'], y=his_data['Volume'], marker_color='#3399ff', name='Volume'),
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row=2, col=1)
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fig.add_trace(
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go.Scatter(x=div_data['Date'], y=div_data['Dividends'], marker_color='#fae823', name='Dividends', line=dict(
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shape='hv'
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)),
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row=3, col=1)
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return fig
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def split_filter_part(filter_part):
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operators = [['ge ', '>='],
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['le ', '<='],
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['lt ', '<'],
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['gt ', '>'],
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['ne ', '!='],
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['eq ', '='],
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['contains '],
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['datestartswith ']]
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for operator_type in operators:
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for operator in operator_type:
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if operator in filter_part:
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name_part, value_part = filter_part.split(operator, 1)
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name = name_part[name_part.find('{') + 1: name_part.rfind('}')]
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value_part = value_part.strip()
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v0 = value_part[0]
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if v0 == value_part[-1] and v0 in ("'", '"', '`'):
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value = value_part[1: -1].replace('\\' + v0, v0)
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else:
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try:
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value = float(value_part)
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except ValueError:
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value = value_part
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return name, operator_type[0].strip(), value
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return [None] * 3
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@app.callback(
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Output('company-kpi-data', "data"),
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Input('company-kpi-data', "page_current"),
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Input('company-kpi-data', "page_size"),
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Input('company-kpi-data', "sort_by"),
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Input('company-kpi-data', 'filter_query'))
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def update_table(page_current, page_size, sort_by, filter):
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filtering_expressions = filter.split(' && ')
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dff = comp_kpi
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for filter_part in filtering_expressions:
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col_name, operator, filter_value = split_filter_part(filter_part)
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if operator in ('eq', 'ne', 'lt', 'le', 'gt', 'ge'):
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dff = dff.loc[getattr(dff[col_name], operator)(filter_value)]
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elif operator == 'contains':
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dff = dff.loc[dff[col_name].str.contains(filter_value)]
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elif operator == 'datestartswith':
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dff = dff.loc[dff[col_name].str.startswith(filter_value)]
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if len(sort_by):
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dff = dff.sort_values(
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[col['column_id'] for col in sort_by],
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ascending=[
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col['direction'] == 'asc'
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for col in sort_by
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],
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inplace=False
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)
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page = page_current
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size = page_size
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return dff.iloc[page * size: (page + 1) * size].to_dict('records')
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app.run_server(debug=True, port=18051, threaded=True)
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