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| 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 | |
| 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 | |
| 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 <tr> instead of <td> | |
| '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 <tr> instead of <td> | |
| '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) | |