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Marquee_Analysis_FrontEnd.py
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Marquee_Analysis_FrontEnd.py
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import pandas as pd
import numpy as np
import sys
import dash
from dash.dependencies import Input, Output
import dash_core_components as dcc
import dash_html_components as html
from pandas_datareader import data as web
from datetime import datetime as dt
import plotly.graph_objs as go
df = pd.read_csv('GS_all_GSIDs_Company_names.csv')
df.drop(['Unnamed: 0'],axis = 1,inplace = True)
company_groups = df.groupby(['company_name']).groups#['userid'].value_counts()
key1 = 'Apple Inc'
date = df.iloc[company_groups[key1]]['date']
df['year'] = pd.DatetimeIndex(df['date']).year
company_2017_growthscores = df.groupby(['year','company_name'])['growthScore'].mean()
cg = company_2017_growthscores[2017]. sort_values(ascending = False)[:5]
cgg = pd.DataFrame(cg)
cgg['company_name'] = cgg.index
cgg = cgg[['company_name','growthScore']]
cgg = cgg.round(3)
company_2017_multiplescore = df.groupby(['year','company_name'])['multipleScore'].mean()
cm = company_2017_multiplescore[2017]. sort_values(ascending = True)[:5]
cmm = pd.DataFrame(cm)
cmm['company_name'] = cmm.index
cmm = cmm[['company_name','multipleScore']]
cmm = cmm.round(3)
company_2017_financialreturnscore = df.groupby(['year','company_name'])['financialReturnsScore'].mean()
cf = company_2017_financialreturnscore[2017]. sort_values(ascending = False)[:5]
cff = pd.DataFrame(cf)
cff['company_name'] = cff.index
cff = cff[['company_name','financialReturnsScore']]
cff = cff.round(3)
app = dash.Dash()
colors = {
'background': '#111111',
'text': 'Teal'
}
def generate_table(dataframe, max_rows=5):
return html.Table(
# Header
[html.Tr([html.Th(col) for col in dataframe.columns])] +
# Body
[html.Tr([
html.Td(dataframe.iloc[i][col]) for col in dataframe.columns
]) for i in range(min(len(dataframe), max_rows))]
)
app.layout = html.Div([
dcc.Tabs(id="tabs", children=[
dcc.Tab(label='Explore', children=[
html.Div([
html.H1('Examine Stock',style={
'textAlign': 'center',
'color': colors['text']
}),
dcc.Dropdown(
id='my-dropdown',
options=[{'label': i, 'value': i} for i in list(df['company_name'].drop_duplicates())],
value='Carnival Corp'
),
dcc.Graph(id='my-graph1',
figure={
'layout': {
'height': 600
}
}),
])
]),
dcc.Tab(label='Analyze', children=[
html.Div([
html.Div([
html.H1('Compare Stocks',style={
'textAlign': 'center',
'color': colors['text'],
'font' : 'Helvetica Light'
}),
dcc.Dropdown(
id='company_1',
options=[{'label': i, 'value': i} for i in list(df['company_name'].drop_duplicates())],
value='Carnival Corp'
),
dcc.Dropdown(
id='company_2',
options=[{'label': i, 'value': i} for i in list(df['company_name'].drop_duplicates())],
value='Tesla Inc',
),
dcc.Dropdown(
id='company_3',
options=[{'label': i, 'value': i} for i in list(df['company_name'].drop_duplicates())],
value='Apple Inc',
),
dcc.Dropdown(
id='company_4',
options=[{'label': i, 'value': i} for i in list(df['company_name'].drop_duplicates())],
value='Target Corp',
),
dcc.RadioItems(
id='my_button',
options = [{'label': 'Integrated Score', 'value': 'integratedScore'},
{'label': 'Growth Score', 'value': 'growthScore'},
{'label': 'Multiple Score', 'value': 'multipleScore'},
{'label': 'Financial Returns Score', 'value': 'financialReturnsScore'}],
labelStyle={'display': 'inline-block', 'font' : 'Helvetica Light'},
value = 'integratedScore'
),
]),
dcc.Graph(id='my-graph2',
figure={
'layout': {
'height': 600
}
}),
])
]),
dcc.Tab(label='Our Picks', children=[
html.Div([
html.Div([
html.H1('Recommended Stocks',style={
'textAlign': 'center',
'color': colors['text'],
'font' : 'Times New Roman'
}),
html.H2('High Growth',style={
'textAlign': 'Center',
'color': colors['text'],
'font' : 'Times New Roman'
}),
generate_table(cgg),
html.P("The first category focuses on investments that have high upside in the near future,\
but also come with a substantial amount of risk. We placed more importance on growth and overlooked \
the multiple factor when developing our algorithm to choose the set of investments that could have\
rapid progress in the short term."
,style={
'textAlign': 'Center',
'font' : 'Times New Roman'
}
),
html.P(cg.index.values + " " + " . " + " "
,style={
'textAlign': 'Center',
'font' : 'Times New Roman'
}),
html.H2('Great Value',style={
'textAlign': 'Center',
'color': colors['text'],
'font' : 'Times New Roman'
}),
generate_table(cmm),
html.P("These stocks are diamonds in the rough. They are not valued at the price they deserve",
style={
'textAlign': 'Center',
'font' : 'Times New Roman'
}),
html.P(cm.index.values + " " + " . " + " ",
style={
'textAlign': 'Center',
'font' : 'Times New Roman'
}),
html.H2('Consistent Returns',style={
'textAlign': 'Center',
'color': colors['text'],
'font' : 'Times New Roman'
}),
generate_table(cff),
html.P("These stocks are reliable blue chips. You can expect them to keep paying dividends. Pensioners or people making a living off of capital gains should take note of these stocks",
style={
'textAlign': 'Center',
'font' : 'Times New Roman'
}),
html.P(cf.index.values + " " + " . " + " "
,style={
'textAlign': 'Center',
'font' : 'Times New Roman'
})
],className='container', style={'maxWidth': '1000px'}),
],className='container', style={'maxWidth': '1000px'})
], style={'maxWidth': '1500px','font': 'Times New Roman'})
], style={'maxWidth': '1500px','font': 'Times New Roman'})
], style={'maxWidth': '1500px','font': 'Times New Roman'})
@app.callback(Output('my-graph1', 'figure'), [Input('my-dropdown', 'value')])
def update_graph(selected_dropdown_value):
trace0 = go.Scatter(
x = df.iloc[company_groups[key1]]['date'],
y = df.iloc[company_groups[selected_dropdown_value]]['growthScore'],
mode = 'lines',
name = 'growthScore'
)
trace1 = go.Scatter(
x = df.iloc[company_groups[key1]]['date'],
y = df.iloc[company_groups[selected_dropdown_value]]['multipleScore'],
mode = 'lines',
name = 'multipleScore'
)
trace2 = go.Scatter(
x = df.iloc[company_groups[key1]]['date'],
y = df.iloc[company_groups[selected_dropdown_value]]['financialReturnsScore'],
mode = 'lines',
name = 'financialReturnsScore'
)
trace3 = go.Scatter(
x = df.iloc[company_groups[key1]]['date'],
y = df.iloc[company_groups[selected_dropdown_value]]['integratedScore'],
mode = 'lines',
name = 'integratedScore'
)
data = [trace0, trace1, trace2, trace3]
return {
'data': data
}
@app.callback(dash.dependencies.Output('my-graph2', 'figure'),
[dash.dependencies.Input('company_1', 'value'),
dash.dependencies.Input('company_2', 'value'),
dash.dependencies.Input('company_3', 'value'),
dash.dependencies.Input('company_4', 'value'),
dash.dependencies.Input('my_button', 'value')])
def update_graph2(company_1_name, company_2_name,
company_3_name, company_4_name,
my_button_value):
trace0 = go.Scatter(
x = df.iloc[company_groups[key1]]['date'],
y = df.iloc[company_groups[company_1_name]][my_button_value],
mode = 'lines',
name = company_1_name
)
trace1 = go.Scatter(
x = df.iloc[company_groups[key1]]['date'],
y = df.iloc[company_groups[company_2_name]][my_button_value],
mode = 'lines',
name = company_2_name
)
trace2 = go.Scatter(
x = df.iloc[company_groups[key1]]['date'],
y = df.iloc[company_groups[company_3_name]][my_button_value],
mode = 'lines',
name = company_3_name
)
trace3 = go.Scatter(
x = df.iloc[company_groups[key1]]['date'],
y = df.iloc[company_groups[company_4_name]][my_button_value],
mode = 'lines',
name = company_4_name
)
data = [trace0, trace1, trace2, trace3]
return {
'data': data
}
if __name__ == '__main__':
app.run_server()