import pandas as pd
import streamlit as st
import vnquant.data as dt
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
import statsmodels.api as sm
from statsmodels.tsa.arima.model import ARIMA
from prophet import Prophet
from datetime import datetime, timedelta
import pytz



start_date = str((datetime.now(pytz.timezone('Asia/Ho_Chi_Minh')) - timedelta(days=365)).strftime("%Y-%m-%d"))
end_date = str((datetime.now(pytz.timezone('Asia/Ho_Chi_Minh')) - timedelta(days=0)).strftime("%Y-%m-%d"))

def prophet_ts(symbol, periods = 10):
    loader = dt.DataLoader(symbol, start_date, end_date)
    data = loader.download()
    data.columns = [col[0] for col in data.columns]
    m = Prophet()
    pdf = pd.DataFrame()
    pdf['ds'] = data.index 
    pdf['y'] = data.close.values
    m.fit(pdf)
    future = m.make_future_dataframe(periods=periods)
    forecast = m.predict(future)
    fig = go.Figure()
    fig.add_trace(go.Scatter(x= pdf.ds, 
                            y=pdf.y,
                            name = f"{symbol}_true"
                            ))
    fig.add_trace(go.Scatter(x= forecast.ds, 
                                 y=forecast.yhat,
                                 name = f"{symbol}_pred"
                                ))
    return fig

st.title("Vietnam Trading by Time Series")
model = st.selectbox("model", ["ARIMA", "Propphet"])
sb = st.text_input('Symbol', 'FPT')
periods = st.slider('Period', 1, 365, 28)
fig = prophet_ts(symbol=sb, periods = periods)
st.plotly_chart(fig, use_container_width=True)