# Generate streamlit 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.

import streamlit as st
import plotly.express as px
#from momentumtiming import strategyAnalysis, universeAnalysis
from momentumtiming20240716 import strategyAnalysis, universeAnalysis

st.set_page_config(page_title="Momentum analysis tool", layout="wide")

tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(["Strgy Analysis", "Strgy rtn vs time", "Strgy rtn vs risk", "Unvrse Analysis", "Unvrse rtn vs time", "Unvrse rtn vs risk"])

with tab1:
    col1, col2, col3, col4, col5 = st.columns(5)
    with col1:
        universe = st.selectbox("universe", ["D30", "N100", "SP500", "R1000", "FAANNG"], index=4)
    with col2:
        lags = st.text_input("lags", value="1,3,6,12")
    with col3:
        member_count = st.selectbox("member count", range(1, 7), index=5)
    with col4:
        months_back = st.text_input("months back", value="12")
    with col5:
        use_cache = st.checkbox("Use Cache?", value=True)

    col1, col2, col3, col4, col5 = st.columns(5)
    with col1:
        submit = st.button("Submit")
    with col2:
        st.markdown("[FinGPT](https://huggingface.co/spaces/FinGPT/FinGPT-Forecaster)")
    with col3:
        st.markdown("[Article](https://docs.google.com/document/d/1WngnKvHFAc8qZEUGt3tBUvFrNFHfeRmHMCX76iD9XMY)")
    with col4:
        st.markdown("[Log](https://docs.google.com/spreadsheets/d/19C-EUB3YiLeyC06nJwEJ0mex6PutRfGGG2_ZqkxgAvw/edit?usp=sharing)")
    with col5:
        st.markdown("[Help](https://docs.google.com/document/d/128e_RkPohXNduVFvwEY0O0xnNi46lnN8Ad9l7uNBCL8/edit?usp=sharing)")

    if submit:
        returnStd, monthlyReturns, recommendations = strategyAnalysis(universe, lags, member_count, int(months_back), use_cache)
        st.subheader("Results")
        st.dataframe(recommendations)

with tab2:
    if 'monthlyReturns' in locals():
        fig = px.line(monthlyReturns)
        st.plotly_chart(fig)

with tab3:
    if 'returnStd' in locals():
        #WGP fig = px.scatter(returnStd, hover_data=['ticker'])
        fig = px.scatter(returnStd, x="std", y="return", text="ticker")
        st.plotly_chart(fig)

with tab4:
    col1, col2, col3, col4 = st.columns(4)
    with col1:
        universes = st.multiselect("universes", ["D30", "N100", "SP500", "R1000", "FAANNG"], default=["D30", "N100", "SP500", "R1000", "FAANNG"])
    with col2:
        u_lags = st.text_input("lags(s)", value="1,3,6,12;1,3,6,6;4-36")
    with col3:
        u_member_count = st.selectbox("U member count", range(1, 7), index=5)
    with col4:
        u_months_back = st.text_input("U months back", value="12")

    col1, col2 = st.columns(2)
    with col1:
        u_use_cache = st.checkbox("U Use Cache?", value=True)
    with col2:
        u_submit = st.button("U Submit")

    if u_submit:
        aggregatedReturnStd, aggregatedMonthlyReturns, aggregatedRecommendations = universeAnalysis(universes, u_lags, u_member_count, int(u_months_back), u_use_cache)
        st.subheader("Results")
        st.dataframe(aggregatedRecommendations)

with tab5:
    if 'aggregatedMonthlyReturns' in locals():
        fig = px.line(aggregatedMonthlyReturns)
        st.plotly_chart(fig)

with tab6:
    if 'aggregatedReturnStd' in locals():
        #WGP fig = px.scatter(aggregatedReturnStd, hover_data=['ticker'])
        fig = px.scatter(aggregatedReturnStd, x="std", y="return", text="ticker")
        st.plotly_chart(fig)