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| 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 | |
| from style import add_logo | |
| from Brent import displayBrent | |
| from WTI import displayWTI | |
| from st_btn_select import st_btn_select | |
| from streamlit_option_menu import option_menu | |
| hide_menu_style = """ | |
| <style> | |
| #MainMenu{visibility: hidden;} | |
| footer{visibility:hidden;} | |
| </style> | |
| """ | |
| # page expands to full width | |
| st.set_page_config(page_title="Predicta.oil | Home", | |
| layout='wide', page_icon="⛽") | |
| st.markdown(hide_menu_style, unsafe_allow_html=True) | |
| add_logo() | |
| # PAGE LAYOUT | |
| # heading | |
| st.title("Crude Oil Benchmark Stock Price Prediction LSTM and ARIMA Models") | |
| selection = option_menu(None, ["Brent", "WTI"], | |
| icons=['droplet', 'droplet'], | |
| menu_icon="cast", default_index=0, orientation="horizontal") | |
| if selection == 'Brent': | |
| displayBrent() | |
| if selection == 'WTI': | |
| displayWTI() | |
| # selection = st_btn_select(('Brent', 'WTI'), index=0) | |
| # st.write(f'Selected option: {selection}') | |
| # if selection == 'Brent': | |
| # displayBrent() | |
| # if selection == 'WTI': | |
| # st.header("Raw Data") | |
| # with st.container(): | |
| # col1, col2 = st.columns(2) | |
| # with col1: | |
| # brent = st.button("Brent Crude Oil") | |
| # st.write(brent) | |
| # with col2: | |
| # wti = st.button("WTI Crude Oil") | |
| # st.write(wti) | |
| # brent, wti = st.columns([.5, 1]) | |
| # brent, wti = st.columns([2]) | |
| # with brent: | |
| # st.button('1') | |
| # with wti: | |
| # st.button('2') | |
| # RawData = st.selectbox( | |
| # "Select Data Set:", ('Brent', 'WTI'), key='dataTypeKey') | |
| # st.write(RawData) | |
| # if RawData == 'Brent': | |
| # displayBrent() | |
| # elif RawData == 'WTI': | |
| # # 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 Brent Crude Oil") | |
| # 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 2022-6-30.") | |
| # 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) | |