# Generate DASH application entitled "Momentum analysis tool" with 6 tabs.
# The first tab named "Strgy Analysis" has 2 rows.
# On the first row are 5 elements laid out horizontally
# The first element is a pulldown labeled  "universe" with items labeled "D30", "N100", "SP500", "R1000" and "FAANNG" that defaults to "FAANNG". 
# The second element is a  text field labeled "lags" that allows me to enter a list of comma delimited numbers and initialized with the string "1,3,6,12", 
# The third element is a  pulldown labeled  "member count" with 6 items labeled 1-6 that defaults to 6.
# The fourth element is a  text field labeled "months back" initialized to 12.
# The fifth element is a  check box labeled "Use Cache?" with a default value of "checked".
# On the second row are 6 elements laid out horizontally
# The first element is a  "Submit" button.
# The second element is a FinGPT markdown link which opens a new tab to https://huggingface.co/spaces/FinGPT/FinGPT-Forecaster
# The third element is an Article markdown link which opens a new tab to https://docs.google.com/document/d/1WngnKvHFAc8qZEUGt3tBUvFrNFHfeRmHMCX76iD9XMY
# The fourth element is a Log markdown link which opens  a new tab to https://docs.google.com/spreadsheets/d/19C-EUB3YiLeyC06nJwEJ0mex6PutRfGGG2_ZqkxgAvw/edit?usp=sharing
# The fifth element is a Help markdown link which opens  a new tab to https://docs.google.com/document/d/128e_RkPohXNduVFvwEY0O0xnNi46lnN8Ad9l7uNBCL8/edit?usp=sharing
# When "Submit" is pressed, the application calls strategyAnalysis passing (universe,lags,memberCount,monthsback=12,useCache) which returns a returnStd data frame , monthly Returns data frame and a recommendations data frame.
# Please print the recommendations data frame in a dataframe area called "Results".
# The second tab is labeled "Strgy rtn vs time" and displays a plotly express line chart where you pass in the monthly Returns data frame.
# The third tab is labeled "Strgy rtn vs risk" and displays a plotly express scatter chart where you pass in the returnStd data frame and x="std", y="return",text="ticker".
# The fourth tab named "Unvrse Analysis" has 2 rows.
# On the first row are 4 elements laid out horizontally
# The first element is a multiple select item labeled  "universes" with items labeled "D30", "N100", "SP500", "R1000" and "FAANNG". All items are selected.
# The second element is a  text field labeled "lags(s)" initialized with the string  "1,3,6,12;1,3,6,6;4-36".
# The third element is a pulldown labeled  "U member count" with 6 items labeled 1-6 that defaults to 6.
# The fourth element is a text field labeled "U months back" initialized to 12.
# On the second row is 
# a check box labeled "U Use Cache?" with a default value of "checked".
# a "U Submit" button.
# When "Submit" is pressed, the application calls univeseAnalysis passing (universes,lags,memberCount,monthsback=12,useCache) which returns a aggregatedReturnStd data frame, aggregatedMonthlyReturns data frame
# and a aggregatedRecommendations data frame.
# Please print the aggregatedRecommendations data frame in a dataframe area called "Results".
# The fifth tab is labeled "Unvrse rtn vs time" and displays a plotly express line chart where you pass in the aggregatedMonthlyReturns data frame.
# The sixth tab is labeled "Unvrse rtn vs risk" and displays a plotly express scatter chart where you pass in the aggregatedReturnStd data frame and x="std", y="return",text="ticker".
# Do not generate a dummy call to strategyAnalysis
# Instead create an "from momentumtiming import strategyAnalysis" import.
# Do not pass a "key" argument to st.dataframe.
# Do not pass x and y arguments to plotly.
# Do not generate a target element for the links
# And include this entire prompt as a comment in the header of the generated file.

from dash import Dash, dcc, html, Input, Output, State, dash_table
import dash_bootstrap_components as dbc
import plotly.express as px
from momentumtiming import strategyAnalysis, universeAnalysis

app = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
app.title = "Momentum analysis tool"

tab1_content = dbc.Card(
    dbc.CardBody([
        dbc.Row([
            dbc.Col(dcc.Dropdown(id='universe', options=['D30', 'N100', 'SP500', 'R1000', 'FAANNG'], value='FAANNG', clearable=False)),
            dbc.Col(dcc.Input(id='lags', type='text', value='1,3,6,12')),
            dbc.Col(dcc.Dropdown(id='member-count', options=[{'label': i, 'value': i} for i in range(1, 7)], value=6, clearable=False)),
            dbc.Col(dcc.Input(id='months-back', type='number', value=12)),
            dbc.Col(dbc.Checkbox(id='use-cache', label='Use Cache?', value=True))
        ]),
        dbc.Row([
            dbc.Col(dbc.Button('Submit', id='submit-button', color='primary')),
            dbc.Col(dcc.Link('FinGPT', href='https://huggingface.co/spaces/FinGPT/FinGPT-Forecaster', target='_blank')),
            dbc.Col(dcc.Link('Article', href='https://docs.google.com/document/d/1WngnKvHFAc8qZEUGt3tBUvFrNFHfeRmHMCX76iD9XMY', target='_blank')),
            dbc.Col(dcc.Link('Log', href='https://docs.google.com/spreadsheets/d/19C-EUB3YiLeyC06nJwEJ0mex6PutRfGGG2_ZqkxgAvw/edit?usp=sharing', target='_blank')),
            dbc.Col(dcc.Link('Help', href='https://docs.google.com/document/d/128e_RkPohXNduVFvwEY0O0xnNi46lnN8Ad9l7uNBCL8/edit?usp=sharing', target='_blank'))
        ]),
        html.Div(id='results')
    ])
)

tab2_content = dbc.Card(
    dbc.CardBody([
        dcc.Graph(id='strgy-rtn-vs-time')
    ])
)

tab3_content = dbc.Card(
    dbc.CardBody([
        dcc.Graph(id='strgy-rtn-vs-risk')
    ])
)

tab4_content = dbc.Card(
    dbc.CardBody([
        dbc.Row([
            dbc.Col(dcc.Dropdown(id='universes', options=['D30', 'N100', 'SP500', 'R1000', 'FAANNG'], value=['D30', 'N100', 'SP500', 'R1000', 'FAANNG'], multi=True)),
            dbc.Col(dcc.Input(id='u-lags', type='text', value='1,3,6,12;1,3,6,6;4-36')),
            dbc.Col(dcc.Dropdown(id='u-member-count', options=[{'label': i, 'value': i} for i in range(1, 7)], value=6, clearable=False)),
            dbc.Col(dcc.Input(id='u-months-back', type='number', value=12))
        ]),
        dbc.Row([
            dbc.Col(dbc.Checkbox(id='u-use-cache', label='U Use Cache?', value=True)),
            dbc.Col(dbc.Button('U Submit', id='u-submit-button', color='primary'))
        ]),
        html.Div(id='u-results')
    ])
)

tab5_content = dbc.Card(
    dbc.CardBody([
        dcc.Graph(id='unvrse-rtn-vs-time')
    ])
)

tab6_content = dbc.Card(
    dbc.CardBody([
        dcc.Graph(id='unvrse-rtn-vs-risk')
    ])
)

app.layout = dbc.Container([
    dbc.Tabs([
        dbc.Tab(tab1_content, label="Strgy Analysis"),
        dbc.Tab(tab2_content, label="Strgy rtn vs time"),
        dbc.Tab(tab3_content, label="Strgy rtn vs risk"),
        dbc.Tab(tab4_content, label="Unvrse Analysis"),
        dbc.Tab(tab5_content, label="Unvrse rtn vs time"),
        dbc.Tab(tab6_content, label="Unvrse rtn vs risk"),
    ])
])

@app.callback(
    [Output('results', 'children'),
     Output('strgy-rtn-vs-time', 'figure'),
     Output('strgy-rtn-vs-risk', 'figure')],
    [Input('submit-button', 'n_clicks')],
    [State('universe', 'value'),
     State('lags', 'value'),
     State('member-count', 'value'),
     State('months-back', 'value'),
     State('use-cache', 'value')]
)
def update_strategy_analysis(n_clicks, universe, lags, member_count, months_back, use_cache):
    if n_clicks is None:
        return dash.no_update, dash.no_update, dash.no_update
    
    returnStd, monthlyReturns, recommendations = strategyAnalysis(universe, lags, member_count, months_back, use_cache)
    
    results_table = dash_table.DataTable(
        id='results-table',
        columns=[{"name": i, "id": i} for i in recommendations.columns],
        data=recommendations.to_dict('records')
    )
    
    strgy_rtn_vs_time_fig = px.line(monthlyReturns)
    strgy_rtn_vs_risk_fig = px.scatter(returnStd, hover_data=['ticker'])
    
    return results_table, strgy_rtn_vs_time_fig, strgy_rtn_vs_risk_fig

@app.callback(
    [Output('u-results', 'children'),
     Output('unvrse-rtn-vs-time', 'figure'),
     Output('unvrse-rtn-vs-risk', 'figure')],
    [Input('u-submit-button', 'n_clicks')],
    [State('universes', 'value'),
     State('u-lags', 'value'),
     State('u-member-count', 'value'),
     State('u-months-back', 'value'),
     State('u-use-cache', 'value')]
)
def update_universe_analysis(n_clicks, universes, lags, member_count, months_back, use_cache):
    if n_clicks is None:
        return dash.no_update, dash.no_update, dash.no_update
    
    aggregatedReturnStd, aggregatedMonthlyReturns, aggregatedRecommendations = universeAnalysis(universes, lags, member_count, months_back, use_cache)
    
    results_table = dash_table.DataTable(
        id='u-results-table',
        columns=[{"name": i, "id": i} for i in aggregatedRecommendations.columns],
        data=aggregatedRecommendations.to_dict('records')
    )
    
    unvrse_rtn_vs_time_fig = px.line(aggregatedMonthlyReturns)
    unvrse_rtn_vs_risk_fig = px.scatter(aggregatedReturnStd, hover_data=['ticker'])
    
    return results_table, unvrse_rtn_vs_time_fig, unvrse_rtn_vs_risk_fig

if __name__ == '__main__':
    app.run_server(debug=True)