import matplotlib as mpl from enum import auto 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 PIL import Image import numpy as np def displayWTI(): st.header("Raw Data") # select time interval interv = st.select_slider('Select Time Series Data Interval for Prediction', options=[ 'Daily', 'Weekly', 'Monthly'], 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": "WTI/Weekly-WTI.csv", "M": "WTI/Monthly-WTI.csv", "D": "WTI/Daily-WTI.csv" } return switcher.get(argument, "WTI/Weekly-WTI.csv") df = pd.read_csv(getInterval(interv[0])) def pagination(df): gb = GridOptionsBuilder.from_dataframe(df) gb.configure_pagination(paginationAutoPageSize=True) return gb.build() page = pagination(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='WTI 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() # # 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:", ('Daily', 'Weekly', 'Monthly'), key='metricKey') with st.container(): col1, col2 = st.columns(2) # LSTM METRICS # st.write("LSTM Metrics") readfile = pd.read_csv('WTI/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('WTI/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('WTI/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 == 'Daily': file = pd.read_csv('WTI/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) # TABLES df2 = pd.DataFrame([[0.8, (0, 1, 0), 2.427, 0.017, 0.8, (0, 1, 0), 5.211, 0.023], [np.nan, np.nan, np.nan, np.nan, 0.5, (0, 1, 0), 9.498, 0.042], [0.5, (1, 0, 0), 9.366, 0.039, 0.500000, (1, 0, 0), 9.530000, 0.042000], [np.nan, np.nan, np.nan, np.nan, 0.500000, (0, 1, 0), 41.668000, 0.097000], [0.600000, (0, 1, 1), 46.308000, 0.091000, 0.600000, (0, 1, 1), 45.242000, 0.099000]], index=pd.Index( ['Daily', 'Weekly*', 'Weekly', 'Monthly*', "Monthly"], name='Actual Label:'), # columns=pd.MultiIndex.from_product([['Brent', 'WTI'], ['Train Split', 'Order', 'MSE', 'MAPE']], names=['Model:', 'Predicted:'])) # columns=pd.MultiIndex.from_tuples([("Brent", "Train Split"), ("Brent", "Order"), ("Brent", "MSE"), ("Brent", "MAPE"), # ("WTI ", "Train Split"), ("WTI", "Order"), ("WTI", "MSE"), ("WTI", "MAPE")])) columns=(["Brent Train Split", "Brent Order", "Brent MSE", "Brent MAPE", "WTI Train Split", "WTI Order", "WTI MSE", "WTI MAPE"])) # df2 = pd.DataFrame([[0.8, (0, 1, 0), 2.427, 0.017, 0.8, (0, 1, 0), 5.211, 0.023], [0.5, (1, 0, 0), 9.366, 0.039, 0.5, (0, 1, 0), 9.498, 0.042], [np.nan, np.nan, np.nan, np.nan, 0.5, (0, 1, 0), 9.498, 0.042], [0.5, (1, 0, 0), 9.366, 0.039, 0.5, (0, 1, 0), 9.498, 0.042]], index=pd.Index( # ['Daily', 'Weekly', '', 'Monthly'], name='Actual Label:'), # columns=pd.MultiIndex.from_product([['', '1'], ['Train Split', 'Order', 'MSE', 'MAPE']], names=['Model:', 'Predicted:'])) st.table(df2) # multi_index = pd.MultiIndex.from_tuples( # [('Daily'), ('Weekly'), ('Hello World'), ('Monthly')], names=['Courses', 'Courses1', 'Courses2', 'Courses3']) # col = pd.MultiIndex.from_tuples([("Brent", "Train Split"), ("Brent", "Order"), ("Brent", "MSE"), ( # "Brent", "MAPE"), ("WTI ", "Train Split"), ("WTI", "Order"), ("WTI", "MSE"), ("WTI", "MAPE")]) # data = [[0.8, (0, 1, 0), 2.427, 0.017, 0.8, (0, 1, 0), 5.211, 0.023], [0.5, (1, 0, 0), 9.366, 0.039, 0.5, (0, 1, 0), 9.498, 0.042], [ # 0, 0, 0, 0, 0.5, (0, 1, 0), 9.498, 0.042], [0.5, (1, 0, 0), 9.366, 0.039, 0.5, (0, 1, 0), 9.498, 0.042]] # df2 = pd.DataFrame(data, columns=col, index=multi_index) # multi_index = pd.MultiIndex.from_tuples([("r0", "rA"), # ("r1", "rB")], # names=['Courses', 'Fee']) # cols = pd.MultiIndex.from_tuples([("Gasoline", "Toyoto"), # ("Gasoline", "Ford"), # ("Electric", "Tesla"), # ("Electric", "Nio")]) # data = [[100, 300, 900, 400], [200, 500, 300, 600]] # df2 = pd.DataFrame(data, columns=cols, index=multi_index) cell_hover = { # for row hover use instead of 'selector': 'tr:hover', 'props': [('background-color', '#ff4c4c')] } index_names = { 'selector': '.index_name', 'props': 'font-style: italic; color: darkgrey; font-weight:normal;' } headers = { # 'selector': 'th:not(.index_name)', 'selector': 'th:not(.index_name)', 'props': 'background-color: #f0f2f6; color: black;' } df2 = df2.style df2 = df2.set_table_styles( [cell_hover, index_names, headers]).highlight_null(props="color: transparent;") df2 = df2.set_table_styles([ {'selector': 'th.col_heading', 'props': 'text-align: center;'}, {'selector': 'th.col_heading.level0', 'props': 'font-size: 1em;'}, {'selector': 'td', 'props': 'text-align: center; font-weight: bold;'}, ], overwrite=False) # df2 = df2.replace(np.nan, '', regex=True) st.table(df2) # st.table(sss) sss = pd.read_csv('WTI/CopBook1.csv') # sss = sss.replace(np.nan, '', regex=True) sss.rename(columns={'Unnamed: 0': ' '}, inplace=True) sss.fillna("") # sss = sss.style # AgGrid(sss, key='WTI/CopBook1.csv', fit_columns_on_grid_load=True, # enable_enterprise_modules=True, theme='streamlit') cell_hover = { # for row hover use instead of 'selector': 'td:hover', 'props': [('background-color', '#ffffb3')] } # sss = sss.style.set_properties(**{'background-color': 'black', # 'color': 'green'}) # sss = sss.style.set_properties(**{'background-color': 'yellow' if v == # sss.loc[0] else "" for v in sss}, axis=1).highlight_null(props="color: transparent;") # sss = sss.style.apply(lambda x: ["background: red" if v == # (x.iloc[1,3]) else "" for v in x], axis=1).highlight_null(props="color: transparent;") # sss = sss.style.apply(lambda x: ["background: red"( # (x.iloc[1:3]))]).highlight_null(props="color: transparent;") # sss.style.apply(lambda x: ["background: red" if v == # x.loc[0] else "" for v in x], axis=1) sss = sss.style sss = sss.set_table_styles( [cell_hover, index_names, headers]).highlight_null(props="color: transparent;") sss = sss.set_table_styles([ {'selector': 'th.col_heading', 'props': 'text-align: center;'}, {'selector': 'th.col_heading.level0', 'props': 'font-size: 1em;'}, {'selector': 'td', 'props': 'text-align: center; font-weight: bold;'}, ], overwrite=False) # sss = sss.style.highlight_null(props="color: transparent;") # sss = sss.set_table_styles([cell_hover]) # def highlight_max(x): # return ['font-weight: bold' if v == x.loc[0] else '' # for v in x] # sss = sss.style.apply(highlight_max) st.table(sss) # BRENT WTI st.header("Brent vs. WTI Accuracy Metrics & Best Models") # arima = Image.open('assets/images/ARIMA23.png') # st.image(arima, caption='Table of Comparisons: ARIMA', # use_column_width='auto') col1, col2, col3 = st.columns([1, 6, 1]) with col2: arima = Image.open('assets/images/ARIMA3111.png') st.image(arima, caption='Table of Comparisons: ARIMA', use_column_width='auto') lstm = Image.open('assets/images/LSTM2.png') st.image(lstm, caption='Table of Comparisons: LSTM', use_column_width='auto') # MODEL OUTPUT TABLE st.header("Model Output (Close Prices vs. Predicted Prices)") interval = st.selectbox("Select Interval:", ('Daily', 'Weekly', 'Monthly'), key='bestmodels') if interval == 'Weekly': file = pd.read_csv('WTI/BestWTI/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_(0, 1, 0)_Predictions", "ARIMA_50.0_(1, 0, 0)_Predictions", "LSTM_80.0_Predictions"], title="BOTH PREDICTED WTI CRUDE OIL PRICES", width=1000) st.plotly_chart(fig, use_container_width=True) elif interval == 'Monthly': file = pd.read_csv('WTI/BestWTI/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_50.0_(0, 1, 0)_Predictions", "ARIMA_60.0_(0, 1, 1)_Predictions", "LSTM_80.0_Predictions"], title="BOTH PREDICTED WTI CRUDE OIL PRICES", width=1000) st.plotly_chart(fig, use_container_width=True) elif interval == 'Daily': file = pd.read_csv('WTI/BestWTI/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_80.0_(0, 1, 0)_Predictions", # find file "LSTM_60.0_DAILY", "LSTM_80.0_DAILY", ], title="BOTH PREDICTED WTI CRUDE OIL PRICES", width=1000) st.plotly_chart(fig, use_container_width=True)