added wti

Brent.py

@@ -0,0 +1,204 @@
+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)

WTI.py

@@ -0,0 +1,184 @@
+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
+
+
+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 aaaaaaa
+    # sss = pd.read_csv('WTI/CopBook1.csv')
+    # st.table(sss)
+    st.header("Brent vs. WTI Accuracy Metrics & Best Models")
+
+    arima = Image.open('assets/images/ARIMA2.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/ARIMA2.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)

WTI/ARIMAMetrics/ARIMA-DAILY.csv

@@ -0,0 +1,73 @@
+,Train Split,Order,MSE,MAPE,Time(s),Mean
+0,0.8,"(0, 0, 0)",673.382,0.486,8.149,-10.408
+1,0.8,"(0, 0, 1)",214.838,0.26,129.151,-5.359
+2,0.8,"(0, 0, 2)",108.937,0.177,203.909,-3.128
+3,0.8,"(0, 1, 0)",11.046,0.031,8.024,0.069
+4,0.8,"(0, 1, 1)",11.078,0.033,29.873,0.114
+5,0.8,"(0, 1, 2)",11.171,0.033,45.638,0.121
+6,0.8,"(0, 2, 0)",28.444,0.052,8.041,-0.005
+7,0.8,"(0, 2, 1)",11.179,0.031,446.272,0.077
+8,0.8,"(0, 2, 2)",11.164,0.033,977.369,0.123
+9,0.8,"(1, 0, 0)",11.037,0.031,67.176,0.035
+10,0.8,"(1, 1, 0)",11.2,0.033,28.965,0.106
+11,0.8,"(1, 1, 1)",11.152,0.033,475.886,0.121
+12,0.8,"(1, 2, 0)",19.819,0.045,31.264,0.014
+13,0.8,"(2, 0, 0)",11.188,0.033,194.506,0.075
+14,0.8,"(2, 1, 0)",11.188,0.033,43.022,0.118
+15,0.8,"(2, 2, 0)",16.77,0.042,93.219,0.019
+16,0.8,"(2, 2, 1)",11.271,0.033,1369.555,0.127
+17,0.8,"(3, 0, 0)",11.177,0.033,295.799,0.089
+18,0.8,"(3, 1, 0)",11.2,0.033,58.214,0.124
+19,0.8,"(3, 1, 1)",11.286,0.034,931.179,0.137
+20,0.8,"(3, 1, 2)",11.299,0.034,1386.637,0.122
+21,0.8,"(3, 2, 0)",15.151,0.041,314.322,0.022
+22,0.8,"(3, 2, 1)",11.292,0.033,2474.986,0.134
+23,0.8,"(3, 2, 2)",11.324,0.034,3400.309,0.128
+24,0.8,"(4, 0, 0)",11.189,0.033,395.455,0.095
+25,0.8,"(4, 1, 0)",11.249,0.034,77.841,0.127
+26,0.8,"(4, 1, 1)",11.293,0.034,1166.389,0.09
+27,0.8,"(4, 1, 2)",11.3,0.034,1876.302,0.091
+28,0.8,"(4, 2, 0)",14.285,0.04,514.003,0.022
+29,0.8,"(4, 2, 1)",11.306,0.034,3339.296,0.134
+30,0.8,"(4, 2, 2)",11.323,0.034,4225.794,0.136
+31,0.5,"(0, 0, 0)",806.447,0.544,22.504,-20.519
+32,0.5,"(0, 0, 1)",231.074,0.284,332.445,-10.577
+33,0.5,"(0, 0, 2)",93.682,0.175,498.967,-6.096
+34,0.5,"(0, 1, 0)",5.211,0.023,22.78,0.04
+35,0.5,"(0, 1, 1)",5.219,0.024,79.698,0.059
+36,0.5,"(0, 1, 2)",5.259,0.024,111.149,0.062
+37,0.5,"(0, 2, 0)",13.095,0.036,23.955,0
+38,0.5,"(0, 2, 1)",5.267,0.023,1068.157,0.067
+39,0.5,"(0, 2, 2)",5.26,0.024,2316.139,0.09
+40,0.5,"(1, 0, 0)",5.209,0.023,177.312,-0.025
+41,0.5,"(1, 1, 0)",5.268,0.024,82.649,0.055
+42,0.5,"(1, 2, 0)",9.114,0.031,88.51,0.007
+43,0.5,"(2, 0, 0)",5.265,0.024,542.238,-0.005
+44,0.5,"(2, 1, 0)",5.266,0.024,116.765,0.061
+45,0.5,"(2, 2, 0)",7.789,0.029,187.285,0.009
+46,0.5,"(2, 2, 1)",5.308,0.024,3750.614,0.097
+47,0.5,"(3, 0, 0)",5.262,0.024,838.678,0.002
+48,0.5,"(3, 1, 0)",5.272,0.024,159.086,0.063
+49,0.5,"(3, 2, 0)",7.044,0.028,782.18,0.01
+50,0.6,"(0, 0, 0)",611.993,0.451,14.023,-15.864
+51,0.6,"(0, 0, 1)",181.888,0.237,243.666,-8.157
+52,0.6,"(0, 0, 2)",80.025,0.15,376.322,-4.681
+53,0.6,"(0, 1, 0)",6.058,0.023,15.695,0.047
+54,0.6,"(0, 1, 1)",6.072,0.023,59.189,0.07
+55,0.6,"(0, 1, 2)",6.12,0.024,84.159,0.074
+56,0.6,"(0, 2, 0)",15.357,0.037,15.988,-0.002
+57,0.6,"(0, 2, 1)",6.126,0.023,801.846,0.064
+58,0.6,"(0, 2, 2)",6.117,0.024,1774.865,0.089
+59,0.6,"(1, 0, 0)",6.052,0.023,129.654,0
+60,0.6,"(1, 1, 0)",6.133,0.023,59.157,0.066
+61,0.6,"(1, 1, 1)",6.11,0.024,1142.548,0.074
+62,0.6,"(1, 2, 0)",10.688,0.032,64.162,0.007
+63,0.6,"(2, 0, 0)",6.126,0.023,403.522,0.023
+64,0.6,"(2, 1, 0)",6.129,0.024,85.455,0.073
+65,0.6,"(2, 2, 0)",9.092,0.03,146.127,0.011
+66,0.6,"(2, 2, 1)",6.173,0.024,3247.553,0.091
+67,0.6,"(3, 0, 0)",6.122,0.023,632.908,0.03
+68,0.6,"(3, 1, 0)",6.135,0.024,120.828,0.076
+69,0.6,"(3, 1, 2)",6.189,0.024,2835.966,0.075
+70,0.6,"(3, 2, 0)",8.227,0.029,587.831,0.012
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WTI/ARIMAMetrics/ARIMA-MONTHLY.csv

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WTI/ARIMAMetrics/ARIMA-WEEKLY.csv

@@ -0,0 +1,55 @@
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WTI/BestWTI/bestDaily.csv

@@ -0,0 +1,753 @@
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WTI/BestWTI/bestMonthly.csv

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WTI/CopBook1.csv

@@ -0,0 +1,8 @@
+ARIMA Best Models,,,,,,,,
+,Brent,,,,WTI,,,
+,Train Split,Order,MSE,MAPE,Train Split,Order,MSE,MAPE
+Daily,0.8,"(0, 1, 0)",2.427,0.017,0.8,"(0, 1, 0)",5.211,0.023
+Weekly,0.5,"(1, 0, 0)",9.366,0.039,0.5,"(0, 1, 0)",9.498,0.042
+,,,,,0.5,"(1, 0, 0)",9.53,0.042
+Monthly,0.6,"(0, 1, 1)",46.308,0.091,0.5,"(0, 1, 0)",41.668,0.097
+,,,,,0.6,"(0, 1, 1)",45.242,0.099

WTI/LSTM.csv

@@ -0,0 +1,10 @@
+,Interval,Train Split,MSE,MAPE,Time(s)
+0,DAILY,0.800,5.904,0.020,71.011
+1,DAILY,0.500,10.041,0.036,70.472
+2,DAILY,0.600,9.876,0.031,103.463
+3,WEEKLY,0.800,25.012,0.039,46.887
+4,WEEKLY,0.500,29.646,0.081,64.873
+5,WEEKLY,0.600,16.999,0.053,58.346
+6,MONTHLY,0.800,80.147,0.096,105.243
+7,MONTHLY,0.500,69.405,0.132,173.290
+8,MONTHLY,0.600,71.132,0.134,124.536

WTI/Monthly-WTI.csv

@@ -0,0 +1,180 @@
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+2021-04-01,63.58,59.49,65.47,57.63,7.39M,7.47%
+2021-05-01,66.32,63.64,67.52,61.56,7.51M,4.31%
+2021-06-01,73.47,66.68,74.45,66.41,7.79M,10.78%
+2021-07-01,73.95,73.5,76.98,65.21,8.11M,0.65%
+2021-08-01,68.5,73.91,73.95,61.74,7.93M,-7.37%
+2021-09-01,75.03,68.55,76.67,67.12,7.75M,9.53%
+2021-10-01,83.57,75.12,85.41,74.23,9.22M,11.38%
+2021-11-01,66.18,83.36,84.97,64.43,9.44M,-20.81%
+2021-12-01,75.21,67.01,77.44,62.43,7.40M,13.64%
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+2022-02-01,95.72,88.15,100.54,86.55,8.21M,8.59%
+2022-03-01,100.28,96.09,130.5,93.53,8.83M,4.76%
+2022-04-01,104.69,101.23,109.81,92.93,5.39M,4.40%
+2022-05-01,114.67,104.0,119.98,98.2,5.32M,9.53%
+2022-06-01,106.22,115.1,123.66,101.58,5.15M,-7.37%

WTI/Weekly-WTI.csv

@@ -0,0 +1,779 @@
+Date,Close,Open,High,Low,Vol.,Change %
+2007-08-05,71.47,75.04,75.1,70.1,1.34M,-5.31%
+2007-08-12,71.98,71.47,74.23,70.1,1.22M,0.71%
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+2007-09-09,79.1,76.46,80.36,75.52,1.39M,3.13%
+2007-09-16,81.62,79.03,83.9,78.25,1.01M,3.19%
+2007-09-23,81.66,81.3,83.76,78.44,1.38M,0.05%
+2007-09-30,81.22,81.75,82.02,78.87,1.20M,-0.54%
+2007-10-07,83.69,81.11,84.05,78.35,1.34M,3.04%
+2007-10-14,88.6,83.91,90.07,83.5,1.28M,5.87%
+2007-10-21,91.86,88.86,92.22,84.68,1.22M,3.68%
+2007-10-28,95.93,91.72,96.24,88.92,1.59M,4.43%
+2007-11-04,96.32,95.93,98.62,93.72,1.71M,0.41%
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+2007-12-02,88.28,88.79,90.73,85.82,1.45M,-0.48%
+2007-12-09,91.27,88.27,94.85,87.09,1.48M,3.39%
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+2008-05-18,132.19,126.35,135.09,125.28,1.38M,4.67%
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+2020-12-13,49.1,46.73,49.28,45.69,1.17M,5.43%
+2020-12-20,48.23,48.54,48.62,46.16,835.39K,-1.77%
+2020-12-27,48.52,48.23,48.96,47.5,901.09K,0.60%
+2021-01-03,52.24,48.4,52.75,47.18,2.55M,7.67%
+2021-01-10,52.36,52.58,53.93,51.5,1.78M,0.23%
+2021-01-17,52.27,52.0,53.79,51.44,953.69K,-0.17%
+2021-01-24,52.2,52.17,53.58,51.82,1.93M,-0.13%
+2021-01-31,56.85,51.99,57.29,51.64,2.11M,8.91%
+2021-02-07,59.47,57.06,59.82,57.0,2.23M,4.61%
+2021-02-14,59.24,59.98,62.26,58.59,1.17M,-0.39%
+2021-02-21,61.5,58.88,63.81,58.82,2.08M,3.81%
+2021-02-28,66.09,61.95,66.42,59.24,2.65M,7.46%
+2021-03-07,65.61,66.68,67.98,63.13,2.35M,-0.73%
+2021-03-14,61.42,65.56,66.4,58.2,1.18M,-6.39%
+2021-03-21,60.97,61.55,61.9,57.25,2.36M,-0.73%
+2021-03-28,61.45,60.93,62.27,58.85,1.86M,0.79%
+2021-04-04,59.32,61.5,61.5,57.63,2.14M,-3.47%
+2021-04-11,63.13,59.35,63.88,58.73,1.51M,6.42%
+2021-04-18,62.14,62.98,64.25,60.61,1.23M,-1.57%
+2021-04-25,63.58,62.06,65.47,60.66,1.90M,2.32%
+2021-05-02,64.9,63.64,66.76,62.91,1.94M,2.08%
+2021-05-09,65.37,65.57,66.63,63.09,2.40M,0.72%
+2021-05-16,63.58,65.5,67.01,61.56,1.10M,-2.74%
+2021-05-23,66.32,63.87,67.52,63.63,2.07M,4.31%
+2021-05-30,69.62,66.68,69.76,66.41,1.58M,4.98%
+2021-06-06,70.91,69.52,71.24,68.47,2.27M,1.85%
+2021-06-13,71.64,70.65,72.99,69.77,1.60M,1.03%
+2021-06-20,74.05,71.52,74.25,71.15,1.28M,3.36%
+2021-06-27,75.16,73.99,76.22,71.97,2.05M,1.50%
+2021-07-04,74.56,75.35,76.98,70.76,2.27M,-0.80%
+2021-07-11,71.81,74.74,75.52,70.41,1.87M,-3.69%
+2021-07-18,72.07,71.49,72.21,65.21,1.33M,0.36%
+2021-07-25,73.95,72.18,74.23,70.56,1.65M,2.61%
+2021-08-01,68.28,73.91,73.95,67.61,2.49M,-7.67%
+2021-08-08,68.44,67.88,69.62,65.15,2.19M,0.23%
+2021-08-15,62.32,67.71,68.27,62.11,804.21K,-8.94%
+2021-08-22,68.74,61.96,69.05,61.74,1.76M,10.30%
+2021-08-29,69.29,69.3,70.61,67.12,1.80M,0.80%
+2021-09-05,69.72,69.11,69.96,67.56,1.84M,0.62%
+2021-09-12,71.97,69.74,73.14,69.51,1.64M,3.23%
+2021-09-19,73.98,71.92,74.27,69.67,1.25M,2.79%
+2021-09-26,75.88,74.19,76.67,73.14,2.29M,2.57%
+2021-10-03,79.35,75.9,80.11,74.96,2.57M,4.57%
+2021-10-10,82.28,79.59,82.66,79.42,2.42M,3.69%
+2021-10-17,83.76,82.6,84.25,80.79,1.28M,1.80%
+2021-10-24,83.57,83.98,85.41,80.58,2.57M,-0.23%
+2021-10-31,81.27,83.36,84.88,78.25,2.62M,-2.75%
+2021-11-07,80.79,81.13,84.97,79.78,2.32M,-0.59%
+2021-11-14,76.1,80.66,81.81,75.37,958.31K,-5.81%
+2021-11-21,68.15,75.75,79.23,67.4,2.15M,-10.45%
+2021-11-28,66.26,69.23,72.93,62.43,3.12M,-2.77%
+2021-12-05,71.67,67.02,73.34,66.72,2.15M,8.16%
+2021-12-12,70.86,72.04,73.0,69.39,1.28M,-1.13%
+2021-12-19,73.79,70.07,73.95,66.04,972.44K,4.13%
+2021-12-26,75.21,73.38,77.44,72.57,1.27M,1.92%
+2022-01-02,78.9,75.69,80.47,74.27,1.98M,4.91%
+2022-01-09,83.82,78.88,84.45,77.83,2.02M,6.24%
+2022-01-16,85.14,84.32,87.91,82.78,816.28K,1.57%
+2022-01-23,86.82,84.91,88.84,81.9,2.34M,1.97%
+2022-01-30,92.31,87.45,93.17,86.34,2.07M,6.32%
+2022-02-06,93.1,91.82,94.66,88.41,2.46M,0.86%
+2022-02-13,91.07,93.91,95.82,89.03,1.67M,-2.18%
+2022-02-20,91.59,91.75,100.54,90.06,1.88M,0.57%
+2022-02-27,115.68,94.99,116.57,94.43,2.93M,26.30%
+2022-03-06,109.33,121.33,130.5,103.63,2.56M,-5.49%
+2022-03-13,104.7,109.42,109.72,93.53,1.32M,-4.23%
+2022-03-20,113.9,105.13,116.64,104.08,1.02M,8.79%
+2022-03-27,99.27,112.92,112.93,97.78,1.82M,-12.84%
+2022-04-03,98.26,98.95,105.59,93.81,1.61M,-1.02%
+2022-04-10,106.95,98.4,107.64,92.93,1.20M,8.84%
+2022-04-17,102.07,107.03,109.81,100.7,683.73K,-4.56%
+2022-04-24,104.69,101.38,107.99,95.28,1.57M,2.57%
+2022-05-01,109.77,104.0,111.37,100.28,1.36M,4.85%
+2022-05-08,110.49,110.43,110.64,98.2,1.71M,0.66%
+2022-05-15,113.23,110.98,115.56,105.13,733.94K,2.48%
+2022-05-22,115.07,110.56,115.3,108.61,1.08M,1.63%
+2022-05-29,118.87,114.96,120.46,111.2,1.30M,3.30%
+2022-06-05,120.67,120.82,123.18,117.14,1.58M,1.51%
+2022-06-12,109.56,120.19,123.68,108.25,1.31M,-9.21%
+2022-06-19,107.62,110.58,112.47,101.53,1.13M,-1.77%
+2022-06-26,108.43,107.22,114.05,104.56,1.58M,0.75%

bakHome.py

@@ -0,0 +1,233 @@
+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
+
+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")
+
+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 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)

pages/1_🔎_About.py

@@ -1,8 +1,7 @@
 import streamlit as st
 from PIL import Image
 import base64
-
-# function play gif
+from style import add_logo
 
 
 def gif(location):
@@ -29,10 +28,11 @@ footer{visibility:hidden;}
 </style>
 """
 # page expands to full width
-st.set_page_config(page_title="Predicta.oil | About", layout='wide', page_icon="⛽")
+st.set_page_config(page_title="Predicta.oil | About",
+                   layout='wide', page_icon="⛽")
 st.markdown(hide_menu_style, unsafe_allow_html=True)
+add_logo()
 st.title('About the Research')
-
 # a. how the thing works
 st.header('How It Works')
 st.markdown('<strong>Welcome!</strong> This is a fully interactive, multi-page web app through the Python library Streamlit that allows users to explore the same models used in the study. Aside from learning about study findings, play with parameters, create your own models, conduct your own comparisions and make your own analyses! Read further to learn how to use the <em>Explore</em> and <em>Make Your Own Model</em> tabs.', unsafe_allow_html=True)
@@ -79,7 +79,7 @@ st.image(accuracy, caption='Visualization')
 
 # b. snippets of the paper
 
-st.markdown('<h1>Conclusions and Recommendations</h1> <p>The full documentation of this project can be accessed through this link: [] </p><h2>Conclusions</h2> <h3>Price Movement Volatility Trends</h3> <p> Price movement volatility refers to how much a set of prices changes over time and how erratic those changes are. In crude oil prices, unless there are spikes or drops due to unforeseen or anomalous circumstances, these trends tend to stray away from erratic highs and lows especially over short periods of time. It must be reiterated that this study does not take into account these anomalies, but focuses on what would be the natural, steady trend of Brent crude oil prices. That being said, the conduction of the study simply paints a clear picture of the behavior of asset prices and how the value of volatility changes over spans of time. </p>  <p> In order to quantify volatility, the standard deviation between the actual close prices (prices from the yfinance dataset) and the predicted prices are computed. From this part of the experiment, it was found that volatility is more likely to be present over longer periods of time. Additionally, it can be observed that volatility is also contingent on anomalous or external factors.</p> <p>It was found that Brent crude oil in particular shows the highest volatility trend over quarterly prices.</p>', unsafe_allow_html=True)
+st.markdown('<h1>Conclusions and Recommendations</h1> <p>The full documentation of this project can be accessed through this link: [https://bit.ly/PredictaPaper] </p><h2>Conclusions</h2> <h3>Price Movement Volatility Trends</h3> <p> Price movement volatility refers to how much a set of prices changes over time and how erratic those changes are. In crude oil prices, unless there are spikes or drops due to unforeseen or anomalous circumstances, these trends tend to stray away from erratic highs and lows especially over short periods of time. It must be reiterated that this study does not take into account these anomalies, but focuses on what would be the natural, steady trend of Brent crude oil prices. That being said, the conduction of the study simply paints a clear picture of the behavior of asset prices and how the value of volatility changes over spans of time. </p>  <p> In order to quantify volatility, the standard deviation between the actual close prices (prices from the yfinance dataset) and the predicted prices are computed. From this part of the experiment, it was found that volatility is more likely to be present over longer periods of time. Additionally, it can be observed that volatility is also contingent on anomalous or external factors.</p> <p>It was found that Brent crude oil in particular shows the highest volatility trend over quarterly prices.</p>', unsafe_allow_html=True)
 
 st.markdown('<h3>Model Accuracy</h3> <p> Model accuracy is quantified through the use of accuracy metrics, specifically MAPE and MSE. These subsections are partitioned in accordance to the time interval of the raw data used, that being daily, weekly, monthly, and quarterly close prices.</p> <h4>Daily Interval Data</h4> <p>It was found that 96 of 102 or 94.12% ARIMA models and 3 of 3 or 100.00% LSTM models were able to attain a MAPE percentage below 10% and 96 of 102 or 94.12% ARIMA models and 3 of 3 or 100.00% LSTM models were able to attain a MSE percentage close to 0 or less than 0.1 using daily interval data.</p> <h4>Weekly Interval Data</h4> <p>It was found that 42 of 48 or 87.50% ARIMA models and 0 of 3 or 0.00% LSTM models were able to attain a MAPE percentage below 10% and 22 of 48 or 45.83%  ARIMA models and 0 of 3 or 0.00% LSTM models were able to attain a MSE percentage close to 0 or less than 0.1 using weekly interval data.</p> <h4>Monthly Interval Data</h4> <p>It was found that 62 of 160 or 38.75% ARIMA model and 1 of 3 or 33.33% LSTM models were able to attain a MAPE percentage below 10% and 0 of 160 or 0.00% ARIMA models and 0 of 3 or 0.00% LSTM models were able to attain a MSE percentage close to 0 or less than 0.1 using monthly interval data. </p> <h4>Quarterly Interval Data</h4> <p>It was found that 0 of 77 or 0.00% ARIMA models and 0 of 3 or 0.00% LSTM models were able to attain a MAPE percentage below 10% and 0 of 77 or 0.00%  ARIMA models and 0 of 3 or 0.00% LSTM models were able to attain a MSE percentage close to 0 or less than 0.1 using quarterly interval data.</p>', unsafe_allow_html=True)
 

pages/2_📈_Explore.py

@@ -1,5 +1,3 @@
-# TODO: add descriptions on how to use
-
 import streamlit as st
 import pandas as pd
 import plotly.graph_objects as go
@@ -8,6 +6,8 @@ import matplotlib.pyplot as plt
 # import numpy as np
 import plotly.express as px
 from st_aggrid import GridOptionsBuilder, AgGrid
+from style import add_logo
+
 hide_menu_style = """
 <style>
 #MainMenu{visibility: hidden;}
@@ -15,14 +15,15 @@ footer{visibility:hidden;}
 </style>
 """
 # page expands to full width
-st.set_page_config(page_title="Predicta.oil | Explore", layout='wide', page_icon="⛽")
+st.set_page_config(page_title="Predicta.oil | Explore",
+                   layout='wide', page_icon="⛽")
 st.markdown(hide_menu_style, unsafe_allow_html=True)
 st.title("Explore Models")
-
+add_logo()
 # ARIMA
 # slider interval
 interv = st.select_slider('Select Time Series Data Interval for Prediction', options=[
-                          'Weekly', 'Monthly', 'Quarterly', 'Daily'], value='Weekly')
+                          'Daily', 'Weekly', 'Monthly', 'Quarterly'], value='Weekly')
 
 # dropdown 50 60 80
 st.write("Select Split")

pages/3_📊_Make_a_Model.py

@@ -17,6 +17,7 @@ from keras import wrappers
 from tensorflow.keras.optimizers import Adam
 from tensorflow.keras.callbacks import ModelCheckpoint
 from st_aggrid import GridOptionsBuilder, AgGrid
+from style import add_logo
 
 hide_menu_style = """
 <style>
@@ -28,7 +29,7 @@ footer{visibility:hidden;}
 st.set_page_config(page_title="Predicta.oil | Make a Model", layout='wide', page_icon="⛽")
 st.markdown(hide_menu_style, unsafe_allow_html=True)
 # ag grid pagination
-
+add_logo()
 
 def pagination(df):
     gb = GridOptionsBuilder.from_dataframe(df)

requirements.txt

@@ -1,20 +1,12 @@
 streamlit==1.11.0
-pandas==1.3.5
-base58==2.0.1
 numpy==1.23
-Pillow>=9.0.1
 plotly==5.6.0
 scikit-learn==0.24
-click==8.0.4
 yfinance==0.1.70
 matplotlib==3.5.1
-cufflinks==0.17.3
 statsmodels==0.10.2 
-cython==0.29.30
-scipy==1.8.1
-patsy==0.5.2
-cvxopt==1.2.7
 joblib==1.1.0
 tensorflow==2.8.2
 streamlit-aggrid == 0.2.3
-
+st-btn-select == 0.1.2
+streamlit-option-menu == 0.3.2

style.py

@@ -0,0 +1,19 @@
+import streamlit as st
+
+def add_logo():
+    st.markdown(
+        """
+        <style>
+            [data-testid="stSidebarNav"]::before {
+                content: "⛽ Predicta.Oil";
+                margin-left: 20px;
+                margin-top: 10px;
+                margin-bottom: 10px;
+                font-size: 30px;
+                position: relative;
+                top: 50px;
+            }
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )

🏠_Home.py

@@ -6,6 +6,11 @@ import matplotlib.pyplot as plt
 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>
@@ -15,217 +20,242 @@ footer{visibility:hidden;}
 """
 
 # page expands to full width
-st.set_page_config(page_title="Predicta.oil | Home", layout='wide', page_icon="⛽")
+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")
-st.subheader("""© Castillon, Ignas, Wong""")
 
-st.header("Raw Data")
+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}')
 
-# select time interval
-interv = st.select_slider('Select Time Series Data Interval for Prediction', options=[
-                          'Weekly', 'Monthly', 'Quarterly', 'Daily'])
+# if selection == 'Brent':
+#     displayBrent()
+# if selection == 'WTI':
 
-# 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]))
-
-# 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")
-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')
-
-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")
+# 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'))
-
-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')
-
-    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)
+#     "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)