Brent.py

import streamlit as st
import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt
# import numpy as np
import plotly.express as px
from st_aggrid import GridOptionsBuilder, AgGrid
import plotly.graph_objects as go


def displayBrent():
    st.header("Raw Data")
    # select time interval
    interv = st.select_slider('Select Time Series Data Interval for Prediction', options=[
        'Daily', 'Weekly', 'Monthly', 'Quarterly'], value='Weekly')

    # st.write(interv[0])

    # Function to convert time series to interval

    @st.cache(persist=True, allow_output_mutation=True)
    def getInterval(argument):
        switcher = {
            "W": "1wk",
            "M": "1mo",
            "Q": "3mo",
            "D": "1d"
        }
        return switcher.get(argument, "1wk")

    # show raw data
    # st.header("Raw Data")
    # using button
    # if st.button('Press to see Brent Crude Oil Raw Data'):

    df = yf.download('BZ=F', interval=getInterval(interv[0]), end="2022-06-30")

    # st.dataframe(df.head())
    df = df.reset_index()

    def pagination(df):
        gb = GridOptionsBuilder.from_dataframe(df)
        gb.configure_pagination(paginationAutoPageSize=True)
        return gb.build()

    # enable enterprise modules for trial only
    # raw data
    page = pagination(df)
    # AgGrid(df, enable_enterprise_modules=True,
    #        theme='streamlit', gridOptions=page, fit_columns_on_grid_load=True, key='data')
    # st.dataframe(df, width=2000, height=600)
    # st.write(df)
    st.table(df.head())
    # download full data

    @st.cache
    def convert_df(df):
        # IMPORTANT: Cache the conversion to prevent computation on every rerun
        return df.to_csv().encode('utf-8')

    csv = convert_df(df)

    st.download_button(
        label="Download data as CSV",
        data=csv,
        file_name='Brent Oil Prices.csv',
        mime='text/csv',
    )

    st.header("Standard Deviation of Raw Data")
    sd = pd.read_csv('StandardDeviation.csv')
    sd.drop("Unnamed: 0", axis=1, inplace=True)
    # sd = sd.reset_index()
    AgGrid(sd, key='SD1', enable_enterprise_modules=True,
           fit_columns_on_grid_load=True, theme='streamlit')
    st.write("Note: All entries end on 2022-06-30.")

    sd = sd.pivot(index='Start Date', columns='Interval',
                  values='Standard Deviation')
    sd = sd.reset_index()
    # table
    # AgGrid(sd, key='SD', enable_enterprise_modules=True,
    #        fit_columns_on_grid_load=True, domLayout='autoHeight', theme='streamlit')

    # visualization
    fig = px.line(sd, x=sd.index, y=['1d', '1wk', '1mo', '3mo'],
                  title="STANDARD DEVIATION OF BRENT CRUDE OIL PRICES", width=1000)
    st.plotly_chart(fig, use_container_width=True)

    # accuracy metrics
    st.header("Accuracy Metric Comparison")
    intervals = st.selectbox(
        "Select Interval:", ('Weekly', 'Monthly', 'Quarterly', 'Daily'), key='metricKey')
    with st.container():
        col1, col2 = st.columns(2)

    # LSTM METRICS
    # st.write("LSTM Metrics")

    readfile = pd.read_csv('LSTM.csv')
    # readfile = readfile[readfile['Interval'] == intervals.upper()]
    readfile = readfile[readfile['Interval']
                        == st.session_state.metricKey.upper()]
    # readfile[readfile['Interval'] == intervals.upper()]
    # readfile = updatefile(readfile)
    readfile.drop("Unnamed: 0", axis=1, inplace=True)
    with col1:
        st.write("LSTM Metrics")
        AgGrid(readfile, key=st.session_state.metricKey, fit_columns_on_grid_load=True,
               enable_enterprise_modules=True, theme='streamlit')

    # st.write(st.session_state.metricKey)

    # ARIMA METRICS
    # st.write("ARIMA Metrics")
    # intervals = st.selectbox(
    #     "Select Interval:", ('Weekly', 'Monthly', 'Quarterly', 'Daily'))

    if intervals == 'Weekly':
        file = pd.read_csv('ARIMAMetrics/ARIMA-WEEKLY.csv')
        file.drop("Unnamed: 0", axis=1, inplace=True)
        page = pagination(file)
        with col2:
            st.write("ARIMA Metrics")
            AgGrid(file, width='100%', theme='streamlit', enable_enterprise_modules=True,
                   fit_columns_on_grid_load=True, key='weeklyMetric', gridOptions=page)

    elif intervals == 'Monthly':
        file = pd.read_csv('ARIMAMetrics/ARIMA-MONTHLY.csv')
        file.drop("Unnamed: 0", axis=1, inplace=True)
        page = pagination(file)
        with col2:
            st.write("ARIMA Metrics")
            AgGrid(file, key='monthlyMetric', fit_columns_on_grid_load=True,
                   enable_enterprise_modules=True, theme='streamlit', gridOptions=page)

    elif intervals == 'Quarterly':
        file = pd.read_csv('ARIMAMetrics/ARIMA-QUARTERLY.csv')
        file.drop("Unnamed: 0", axis=1, inplace=True)
        page = pagination(file)
        with col2:
            st.write("ARIMA Metrics")
            AgGrid(file, key='quarterlyMetric', fit_columns_on_grid_load=True,
                   enable_enterprise_modules=True, theme='streamlit', gridOptions=page)

    elif intervals == 'Daily':
        file = pd.read_csv('ARIMAMetrics/ARIMA-DAILY.csv')
        file.drop("Unnamed: 0", axis=1, inplace=True)
        page = pagination(file)
        with col2:
            st.write("ARIMA Metrics")
            AgGrid(file, key='dailyMetric', width='100%', fit_columns_on_grid_load=True,
                   enable_enterprise_modules=True, theme='streamlit', gridOptions=page)

    # MODEL OUTPUT TABLE
    st.header("Model Output (Close Prices vs. Predicted Prices)")

    interval = st.selectbox("Select Interval:", ('Weekly',
                            'Monthly', 'Quarterly', 'Daily'), key='bestmodels')

    if interval == 'Weekly':
        file = pd.read_csv('bestWeekly.csv')
        page = pagination(file)
        AgGrid(file, key='weeklycombined', fit_columns_on_grid_load=True,
               enable_enterprise_modules=True, theme='streamlit', gridOptions=page)

        # Visualization
        st.header("Visualization")
        fig = px.line(file, x=file["Date"], y=["Close Prices", "ARIMA_50.0_(1, 0, 0)_Predictions",
                                               "LSTM_80.0_Predictions"], title="BOTH PREDICTED BRENT CRUDE OIL PRICES", width=1000)
        st.plotly_chart(fig, use_container_width=True)

    elif interval == 'Monthly':
        file = pd.read_csv('bestMonthly.csv')
        page = pagination(file)
        AgGrid(file, key='monthlyCombined', fit_columns_on_grid_load=True,
               enable_enterprise_modules=True, theme='streamlit', gridOptions=page)
        # Visualization
        st.header("Visualization")
        fig = px.line(file, x=file["Date"], y=["Close Prices", "ARIMA_60.0_(0, 1, 1)_Predictions",  # find file
                                               "LSTM_80.0_Predictions"], title="BOTH PREDICTED BRENT CRUDE OIL PRICES", width=1000)
        st.plotly_chart(fig, use_container_width=True)

    elif interval == 'Quarterly':
        file = pd.read_csv('bestQuarterly.csv')
        page = pagination(file)
        AgGrid(file, key='quarterlyCombined', fit_columns_on_grid_load=True,
               enable_enterprise_modules=True, theme='streamlit', gridOptions=page)
        # Visualization
        st.header("Visualization")
        fig = px.line(file, x=file["Date"], y=["Close Prices", "ARIMA_50.0_(0, 1, 0)_Predictions",  # find file
                                               "LSTM_80.0_Predictions"], title="BOTH PREDICTED BRENT CRUDE OIL PRICES", width=1000)
        st.plotly_chart(fig, use_container_width=True)

    elif interval == 'Daily':
        file = pd.read_csv('bestDaily.csv')
        page = pagination(file)
        AgGrid(file, key='dailyCombined', fit_columns_on_grid_load=True,
               enable_enterprise_modules=True, theme='streamlit', gridOptions=page)
        # Visualization
        st.header("Visualization")
        fig = px.line(file, x=file["Date"], y=["Close Prices", "ARIMA_50.0_(0, 1, 0)_Predictions",  # find file
                                               "LSTM_60.0_Predictions"], title="BOTH PREDICTED BRENT CRUDE OIL PRICES", width=1000)
        st.plotly_chart(fig, use_container_width=True)