Updated technical anlysis

app.py

@@ -1,6 +1,8 @@
 import streamlit as st
 
 pg = st.navigation({"Bioma AI PoC":[st.Page("page_1.py", title="Semantic Analysis"), 
-                    st.Page("page_2.py", title="Price Forecasting"),
-                    st.Page("page_3.py", title="MLOps Pipeline")]})
+                    st.Page("page_2.py", title="Technical Analysis"),
+                    st.Page("page_3.py", title="Price Forecasting"),
+                    st.Page("page_4.py", title="MLOps Pipeline"),
+                    st.Page("page_5.py", title="AI Trading")],})
 pg.run()

dash_app.py

@@ -0,0 +1,143 @@
+import pandas as pd
+import pandas_ta as ta
+import dash
+from dash import dcc, html
+from dash.dependencies import Input, Output, State
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+# Load sample data (replace with actual data)
+data = pd.read_csv('data/brent_futures.csv')
+data['Date'] = pd.to_datetime(data['Date'])
+data.sort_values('Date', inplace=True)
+
+# Initialize Dash app
+app = dash.Dash(__name__)
+
+# Dash layout
+app.layout = html.Div([
+    html.H1("OHLCV Chart with Technical Indicators"),
+    
+    # Dropdown for selecting moving averages and other technical indicators
+    html.Div([
+        html.Label("Select Moving Averages"),
+        dcc.Dropdown(
+            id='moving-avg-choice',
+            options=[
+                {'label': 'MA 4', 'value': 'MA_4'},
+                {'label': 'MA 10', 'value': 'MA_10'},
+                {'label': 'MA 30', 'value': 'MA_30'}
+            ],
+            multi=True,
+            value=['MA_4', 'MA_10']  # Default selected
+        ),
+        html.Br(),
+        
+        # Sliders for lengths of technical indicators
+        html.Label("MA 4 Length"),
+        dcc.Slider(id='ma4-length', min=2, max=20, value=4, marks={i: str(i) for i in range(2, 21)}),
+        html.Br(),
+        
+        html.Label("MA 10 Length"),
+        dcc.Slider(id='ma10-length', min=5, max=50, value=10, marks={i: str(i) for i in range(5, 51, 5)}),
+        html.Br(),
+        
+        html.Label("MA 30 Length"),
+        dcc.Slider(id='ma30-length', min=10, max=100, value=30, marks={i: str(i) for i in range(10, 101, 10)}),
+        html.Br(),
+        
+        dcc.Checklist(
+            id='indicator-choice',
+            options=[
+                {'label': 'Bollinger Bands', 'value': 'BB'},
+                {'label': 'RSI', 'value': 'RSI'},
+                {'label': 'MACD', 'value': 'MACD'}
+            ],
+            value=['BB', 'RSI'],  # Default checked
+            labelStyle={'display': 'inline-block'}
+        )
+    ], style={'width': '30%', 'display': 'inline-block', 'padding': '20px'}),
+    
+    # Div for rendering plotly chart
+    html.Div([dcc.Graph(id='technical-chart')], style={'width': '68%', 'display': 'inline-block'}),
+])
+
+# Callback to update chart based on selected technical analyses and lengths
+@app.callback(
+    Output('technical-chart', 'figure'),
+    Input('moving-avg-choice', 'value'),
+    Input('ma4-length', 'value'),
+    Input('ma10-length', 'value'),
+    Input('ma30-length', 'value'),
+    Input('indicator-choice', 'value')
+)
+def update_chart(moving_avg_choice, ma4_length, ma10_length, ma30_length, indicators):
+    # Reset calculations for indicators
+    data['MA_4'] = ta.sma(data['Close'], length=ma4_length) if 'MA_4' in moving_avg_choice else None
+    data['MA_10'] = ta.sma(data['Close'], length=ma10_length) if 'MA_10' in moving_avg_choice else None
+    data['MA_30'] = ta.sma(data['Close'], length=ma30_length) if 'MA_30' in moving_avg_choice else None
+    
+    if 'BB' in indicators:
+        bbands = ta.bbands(data['Close'], length=20)
+        data['BB_upper'], data['BB_middle'], data['BB_lower'] = bbands.iloc[:, 2], bbands.iloc[:, 1], bbands.iloc[:, 0]
+    if 'RSI' in indicators:
+        data['RSI'] = ta.rsi(data['Close'], length=8)
+    if 'MACD' in indicators:
+        macd = ta.macd(data['Close'], fast=12, slow=26, signal=9)
+        data['MACD'], data['MACD_Signal'], data['MACD_Hist'] = macd.iloc[:, 0], macd.iloc[:, 1], macd.iloc[:, 2]
+    
+    # Create subplots
+    fig = make_subplots(
+        rows=4 if 'MACD' in indicators else 3, cols=1,
+        shared_xaxes=True,
+        vertical_spacing=0.05,
+        row_heights=[0.6, 0.2, 0.2, 0.2] if 'MACD' in indicators else [0.6, 0.2, 0.2],
+        subplot_titles=('OHLC with Technical Indicators', 'RSI', 'MACD', 'Volume') if 'MACD' in indicators else ('OHLC with Technical Indicators', 'RSI', 'Volume')
+    )
+
+    # Plot OHLC candlestick
+    fig.add_trace(go.Candlestick(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name="OHLC"), row=1, col=1)
+
+    # Plot Moving Averages
+    if 'MA_4' in moving_avg_choice:
+        fig.add_trace(go.Scatter(x=data['Date'], y=data['MA_4'], mode='lines', name=f'MA {ma4_length}', line=dict(color='blue')), row=1, col=1)
+    if 'MA_10' in moving_avg_choice:
+        fig.add_trace(go.Scatter(x=data['Date'], y=data['MA_10'], mode='lines', name=f'MA {ma10_length}', line=dict(color='orange')), row=1, col=1)
+    if 'MA_30' in moving_avg_choice:
+        fig.add_trace(go.Scatter(x=data['Date'], y=data['MA_30'], mode='lines', name=f'MA {ma30_length}', line=dict(color='green')), row=1, col=1)
+
+    # Plot Bollinger Bands
+    if 'BB' in indicators:
+        fig.add_trace(go.Scatter(x=data['Date'], y=data['BB_upper'], mode='lines', name='BB Upper', line=dict(color='purple')), row=1, col=1)
+        fig.add_trace(go.Scatter(x=data['Date'], y=data['BB_middle'], mode='lines', name='BB Middle', line=dict(color='gray')), row=1, col=1)
+        fig.add_trace(go.Scatter(x=data['Date'], y=data['BB_lower'], mode='lines', name='BB Lower', line=dict(color='purple')), row=1, col=1)
+
+    # Plot RSI
+    if 'RSI' in indicators:
+        fig.add_trace(go.Scatter(x=data['Date'], y=data['RSI'], mode='lines', name='RSI', line=dict(color='magenta')), row=2, col=1)
+
+    # Plot MACD
+    if 'MACD' in indicators:
+        fig.add_trace(go.Scatter(x=data['Date'], y=data['MACD'], mode='lines', name='MACD', line=dict(color='blue')), row=3, col=1)
+        fig.add_trace(go.Scatter(x=data['Date'], y=data['MACD_Signal'], mode='lines', name='MACD Signal', line=dict(color='red')), row=3, col=1)
+        fig.add_trace(go.Bar(x=data['Date'], y=data['MACD_Hist'], name='MACD Hist', marker=dict(color='green')), row=3, col=1)
+
+    # Plot Volume
+    fig.add_trace(go.Bar(x=data['Date'], y=data['Volume'], name='Volume', marker=dict(color='darkgreen')), row=4 if 'MACD' in indicators else 3, col=1)
+
+    # Customize layout
+    fig.update_layout(
+        height=1000,
+        showlegend=True,
+        xaxis_rangeslider_visible=False,
+        dragmode='drawline'  # Allow drawing lines on the chart
+    )
+
+    # Enable dynamic range for y-axes
+    fig.update_yaxes(autorange=True)
+
+    return fig
+
+# Run the app
+if __name__ == '__main__':
+    app.run_server(debug=True)

gradio_app.py

@@ -0,0 +1,65 @@
+import gradio as gr
+import pandas as pd
+import pandas_ta as ta
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+# Sample OHLCV data (replace this with actual data)
+data = pd.read_csv('data/brent_futures.csv')
+data['Date'] = pd.to_datetime(data['Date'])
+data.set_index('Date', inplace=True)
+data.sort_index(inplace=True)
+
+def plot_chart(ma_length, draw_mode):
+    # Calculate Moving Average and Bollinger Bands
+    data['MA'] = ta.sma(data['Close'], length=ma_length)
+    bbands = ta.bbands(data['Close'], length=20)
+    data['BB_upper'], data['BB_middle'], data['BB_lower'] = bbands.iloc[:, 2], bbands.iloc[:, 1], bbands.iloc[:, 0]
+    
+    # Create a subplot figure with 2 rows: OHLC and Volume
+    fig = make_subplots(
+        rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.05,
+        row_heights=[0.7, 0.3], subplot_titles=('OHLC with Moving Average and Bollinger Bands', 'Volume')
+    )
+    
+    # Plot OHLC candlestick
+    fig.add_trace(go.Candlestick(
+        x=data.index, open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name="OHLC"), row=1, col=1)
+    
+    # Add Moving Average and Bollinger Bands
+    fig.add_trace(go.Scatter(x=data.index, y=data['MA'], mode='lines', name=f'MA {ma_length}', line=dict(color='blue')), row=1, col=1)
+    fig.add_trace(go.Scatter(x=data.index, y=data['BB_upper'], mode='lines', name='BB Upper', line=dict(color='purple')), row=1, col=1)
+    fig.add_trace(go.Scatter(x=data.index, y=data['BB_middle'], mode='lines', name='BB Middle', line=dict(color='gray')), row=1, col=1)
+    fig.add_trace(go.Scatter(x=data.index, y=data['BB_lower'], mode='lines', name='BB Lower', line=dict(color='purple')), row=1, col=1)
+    
+    # Plot Volume
+    fig.add_trace(go.Bar(x=data.index, y=data['Volume'], name='Volume', marker=dict(color='green')), row=2, col=1)
+    
+    # Customize layout
+    fig.update_layout(
+        height=700, showlegend=True,
+        legend=dict(orientation='h', yanchor='bottom', y=-0.15, xanchor='center', x=0.5),
+        xaxis_rangeslider_visible=False, dragmode='drawline' if draw_mode else 'zoom'
+    )
+    
+    # Enable dynamic y-axis range
+    fig.update_yaxes(autorange=True)
+    
+    return fig
+
+# Gradio UI
+def update_chart(ma_length, drawline_button):
+    draw_mode = True if drawline_button == 'Draw Line' else False
+    return plot_chart(ma_length, draw_mode)
+
+ma_length_input = gr.Number(value=10, label="Moving Average Length", interactive=True, step=1, minimum=1)
+drawline_button = gr.Button("Draw Line")
+chart_output = gr.Plot()
+
+gr.Interface(
+    fn=update_chart,
+    inputs=[ma_length_input, drawline_button],
+    outputs=[chart_output],
+    title="Interactive Technical Analysis Dashboard",
+    description="Choose moving average length and toggle line drawing mode."
+).launch()

latihan.ipynb

@@ -0,0 +1,10 @@
+{
+ "cells": [],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

modules/technical.py

@@ -0,0 +1,38 @@
+import pandas as pd
+import pandas_ta as ta
+
+class TechnicalAnalysis:
+    def __init__(self, csv_file):
+        # Load the CSV data
+        self.data = pd.read_csv(csv_file)
+        self.data['Date'] = pd.to_datetime(self.data['Date'])
+        self.data.set_index('Date', inplace=True)
+
+    def moving_average(self, length):
+        # Calculate moving average
+        self.data[f'MA_{length}'] = ta.sma(self.data['Close'], length=length)
+
+    def rsi(self, length=14):
+        # Calculate RSI
+        self.data[f'RSI_{length}'] = ta.rsi(self.data['Close'], length=length)
+
+    def bollinger_bands(self, length=20, std=2):
+        # Calculate Bollinger Bands
+        bbands = ta.bbands(self.data['Close'], length=length, std=std)
+        self.data['BB_upper'], self.data['BB_middle'], self.data['BB_lower'] = bbands.iloc[:, 0], bbands.iloc[:, 1], bbands.iloc[:, 2]
+
+    def macd(self, fast=12, slow=26, signal=9):
+        # Calculate MACD
+        macd = ta.macd(self.data['Close'], fast=fast, slow=slow, signal=signal)
+        self.data['MACD'], self.data['MACD_Signal'], self.data['MACD_Hist'] = macd.iloc[:, 0], macd.iloc[:, 1], macd.iloc[:, 2]
+    
+    def ultimate_oscillator(self, fast=7, medium=14, slow=28):
+        # Calculate Ultimate Oscillator
+        self.data['Ultimate_Oscillator'] = ta.uo(self.data['High'], self.data['Low'], self.data['Close'], fast=fast, medium=medium, slow=slow)
+
+    def adx(self, length=14):
+        # Calculate ADX
+        self.data['ADX'], self.data['ADX_DMP'], self.data['ADX_DMN'] = ta.adx(self.data['High'], self.data['Low'], self.data['Close'], length=length)
+
+    def get_data(self):
+        return self.data

page_2.py

@@ -1,63 +1,217 @@
-import streamlit as st
 import pandas as pd
-import os
-from PIL import Image
+import pandas_ta as ta
+import streamlit as st
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+from modules.technical import TechnicalAnalysis
+
+st.set_page_config(page_title="Technical Analysis", layout="wide")
+
+# Load sample data (replace this with actual data)
+data = pd.read_csv('data/brent_futures.csv')
+data['Date'] = pd.to_datetime(data['Date'])
+data.sort_values('Date', inplace=True)
+
+# crudeData = TechnicalAnalysis('data/brent_futures.csv')
+
+# Streamlit sidebar for user input
+st.sidebar.title("Technical Analysis Settings")
+
+# Allow user to choose which technical indicators to display using multiselect
+indicators = st.sidebar.multiselect("Select Technical Indicators", 
+                                    ['Simple Moving Average', 'Bollinger Bands', 'RSI', 'MACD', 'Stochastic Oscillator', 'ADX'],
+                                    default=['Simple Moving Average'])
+
+# Set lengths for technical indicators
+with st.sidebar.expander("Simple Moving Average"):
+    sma_length = st.number_input('Simple Moving Average Length', min_value=5, max_value=300, value=5, step=1)
+with st.sidebar.expander("Bollinger Bands"):
+    bb_length = st.number_input('Bollinger Bands Length', min_value=5, max_value=300, value=20, step=1)
+with st.sidebar.expander("RSI"):
+    rsi_length = st.number_input('RSI Length', min_value=1, max_value=300, value=5, step=1)
+with st.sidebar.expander("MACD"):
+    macd_fast_length = st.number_input("MACD Fast Length", min_value=5, max_value=300, value=12)
+    macd_slow_length = st.number_input("MACD Slow Length", min_value=10, max_value=300, value=26)
+    macd_signal_length = st.number_input("MACD Signal Length", min_value=5, max_value=300, value=9)
+with st.sidebar.expander("Stochastic Oscillator"):
+    stocho_fast_length = st.number_input("Stochastic Oscillator Length", min_value=5, max_value=300, value=14)
+    stocho_slow_length = st.number_input("Stochastic Oscillator Length", min_value=5, max_value=300, value=28)
+with st.sidebar.expander("ADX"):
+    adx_length = st.number_input("ADX Length", min_value=5, max_value=50, value=14)
+
+# Initialize indicator choices
+moving_avg_choice = 'Simple Moving Average' in indicators
+bb_choice = 'Bollinger Bands' in indicators
+rsi_choice = 'RSI' in indicators
+macd_choice = 'MACD' in indicators
+stocho_choice = 'Stochastic Oscillator' in indicators
+adx_choice = 'ADX' in indicators
+
+# Calculate indicators based on user input
+if moving_avg_choice:
+    data['SMA'] = ta.sma(data['Close'], length=sma_length)
+if bb_choice:
+    bbands = ta.bbands(data['Close'], length=bb_length)
+    data['BB_upper'], data['BB_middle'], data['BB_lower'] = bbands.iloc[:, 2], bbands.iloc[:, 1], bbands.iloc[:, 0]
+if rsi_choice:
+    data['RSI'] = ta.rsi(data['Close'], length=rsi_length)
+if macd_choice:
+    macd = ta.macd(data['Close'], fast=macd_fast_length, slow=macd_slow_length, signal=macd_signal_length)
+    data['MACD'], data['MACD_Signal'], data['MACD_Hist'] = macd.iloc[:, 0], macd.iloc[:, 1], macd.iloc[:, 2]
+if stocho_choice:
+    stoch = ta.stoch(data['High'], data['Low'], data['Close'], k=stocho_fast_length, d=stocho_slow_length)
+    data['Stochastic_k'] = stoch.iloc[:, 0]
+    data['Stochastic_d'] = stoch.iloc[:, 1]
+if adx_choice:
+    adx = ta.adx(data['High'], data['Low'], data['Close'], length=adx_length)
+    data['ADX'], data['ADX_DMP'], data['ADX_DMN'] = adx.iloc[:, 0], adx.iloc[:, 1], adx.iloc[:, 2]
+
+# Dynamically calculate the number of rows based on selected options
+rows = 1  # OHLC is mandatory
+if rsi_choice:
+    rows += 1
+if macd_choice:
+    rows += 1
+if stocho_choice:
+    rows += 1
+if adx_choice:
+    rows += 1
+row_heights = [0.6] + [0.2] * (rows - 1)
+
+# Create a subplot figure
+subplot_titles = ['OHLC with Technical Indicators']
+if rsi_choice:
+    subplot_titles.append('RSI')
+if macd_choice:
+    subplot_titles.append('MACD')
+if stocho_choice:
+    subplot_titles.append('Stochastic Oscillator')
+if adx_choice:
+    subplot_titles.append('ADX')
+subplot_titles.append('Volume')
+
+fig = make_subplots(
+    rows=rows + 1, cols=1,  # Extra row for Volume
+    shared_xaxes=True,
+    vertical_spacing=0.05,
+    row_heights=row_heights + [0.2],
+    subplot_titles=subplot_titles
+)
+
+# Plot OHLC candlestick
+fig.add_trace(go.Candlestick(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name="OHLC"), row=1, col=1)
+
+# Plot Moving Averages
+if moving_avg_choice:
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['SMA'], mode='lines', name=f'SMA {sma_length}', line=dict(color='blue')), row=1, col=1)
+
+# Plot Bollinger Bands
+if bb_choice:
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['BB_upper'], mode='lines', name='BB Upper', line=dict(color='purple')), row=1, col=1)
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['BB_middle'], mode='lines', name='BB Middle', line=dict(color='gray')), row=1, col=1)
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['BB_lower'], mode='lines', name='BB Lower', line=dict(color='purple')), row=1, col=1)
+
+# Plot RSI
+if rsi_choice:
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['RSI'], mode='lines', name=f'RSI {rsi_length}', line=dict(color='magenta')), row=2, col=1)
+
+# Plot MACD
+if macd_choice:
+    macd_row = 2 if not rsi_choice else 3
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['MACD'], mode='lines', name='MACD', line=dict(color='blue')), row=macd_row, col=1)
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['MACD_Signal'], mode='lines', name='MACD Signal', line=dict(color='red')), row=macd_row, col=1)
+    fig.add_trace(go.Bar(x=data['Date'], y=data['MACD_Hist'], name='MACD Hist', marker=dict(color='green')), row=macd_row, col=1)
+
+# Plot Stochastic Oscillator
+if stocho_choice:
+    stocho_row = 2 if not rsi_choice and not macd_choice else (3 if (not rsi_choice) or (not macd_choice) else 4)
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['Stochastic_k'], mode='lines', name='Stochastic K', line=dict(color='blue')), row=stocho_row, col=1)
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['Stochastic_d'], mode='lines', name='Stochastic D', line=dict(color='red')), row=stocho_row, col=1)
+# Plot ADX
+if adx_choice:
+    adx_row = 2 if not rsi_choice and not macd_choice and not stocho_choice else (
+        3 if (not macd_choice and not stocho_choice) or (not macd_choice and not rsi_choice) or (not stocho_choice and not rsi_choice) else (
+            4 if (not stocho_choice) or (not macd_choice) or (not rsi_choice) else 5))
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['ADX'], mode='lines', name='ADX', line=dict(color='darkgreen')), row=adx_row, col=1)
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['ADX_DMP'], mode='lines', name='ADX DMP', line=dict(color='lightgreen')), row=adx_row, col=1)
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['ADX_DMN'], mode='lines', name='ADX DMN', line=dict(color='orange')), row=adx_row, col=1)
+
+# Plot Volume
+fig.add_trace(go.Bar(x=data['Date'], y=data['Volume'], name='Volume', marker=dict(color='darkgreen')), row=rows + 1, col=1)
+
+# Customize layout
+fig.update_layout(
+    title='Brent Crude Oil Futures',
+    height=700,
+    showlegend=True,
+    dragmode='pan',
+    xaxis_rangeslider_visible=False,  # Hide default range slider
+)
+
+config = {'scrollZoom': True}
+
+# Enable dynamic range for y-axes
+fig.update_yaxes(autorange=True)
+
+# Show the plot in Streamlit
+st.plotly_chart(fig, config=config)
+
+st.subheader('Technical Analysis Data')
+st.dataframe(data)
+
+st.markdown("""
+    ## Notes for improvements
+    - Add interactive elements to allow users to customize the technical indicators
+    - Add features such as backtesting, alerts, and notifications, line annotations, etc.
+            """)
+
 
-st.title('Price Forecasting - Crude Oil Futures')
-st.subheader('This page is not interactive - only for prototype purposes*')
-st.text('*Due to not having access to GPU for cloud computation yet.')
 
-st.header('Univariate Forecasting with Exogenous Predictors')
+# ta = TechnicalAnalysis('data/brent_futures.csv')
 
-col1, col2, col3 = st.columns(3)
+# # Allow user to input settings
+# length_ma = st.number_input("Moving Average Length", min_value=2, value=20)
+# rsi_button = st.checkbox("Show RSI", value=False)
+# bb_button = st.checkbox("Show Bollinger Bands", value=False)
+# macd_button = st.checkbox("Show MACD", value=False)
 
-uni_df = pd.read_csv(os.path.join('price_forecasting_ml', 
-                                   'artifacts',
-                                   'crude_oil_8998a364-2ecc-483d-8079-f04d455b4522',
-                                   'train_data.csv')).drop(columns=['Unnamed: 0'])
+# # Calculate moving average
+# ta.moving_average(length=length_ma)
 
-with col1:
-    horizon_uni = st.text_input('Univariate Forecasting Horizon')
-with col2:
-    target_uni = st.multiselect('Univariate Target Variable', uni_df.columns
-                             ,default='y')
-with col3:
-    agg_uni = st.selectbox('Univariate Data Aggregation', 
-                           ['Daily', 'Weekly', 'Monthly', 'Yearly'])
+# # Conditionally calculate and show indicators
+# if rsi_button:
+#     ta.rsi()
 
+# if bb_button:
+#     ta.bollinger_bands()
 
-st.dataframe(uni_df)
+# if macd_button:
+#     ta.macd()
 
-img1 = Image.open(os.path.join('price_forecasting_ml',
-                              'artifacts',
-                              'crude_oil_8998a364-2ecc-483d-8079-f04d455b4522',
-                              'forecast_plot.jpg'))
-st.image(img1, caption="Crude Oil Futures Price Forecasting - Univariate with Exogenous Features (Horizon = 5)")
+# # Fetch the updated data
+# data = ta.get_data()
 
-st.markdown("---")
+# # Plot data using Plotly
+# fig = go.Figure()
 
-st.header('Multivariate Forecasting')
+# # Add candlestick
+# fig.add_trace(go.Candlestick(x=data.index, open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name='Candlestick'))
 
-col4, col5, col6 = st.columns(3)
+# # Add moving average
+# fig.add_trace(go.Scatter(x=data.index, y=data[f'MA_{length_ma}'], mode='lines', name=f'MA {length_ma}'))
 
-multi_df = pd.read_csv(os.path.join('price_forecasting_ml', 
-                                   'artifacts',
-                                   'crude_oil_df1ce299-117d-43c7-bcd5-7ecaeac0bc89',
-                                   'train_data.csv')).drop(columns=['Unnamed: 0'])
+# # Add indicators if toggled
+# if rsi_button:
+#     fig.add_trace(go.Scatter(x=data.index, y=data[f'RSI_14'], mode='lines', name='RSI'))
 
-with col4:
-    horizon_multi = st.text_input('Multivariate Forecasting Horizon')
-with col5:
-    target_multi = st.multiselect('Multivariate Target Variable', multi_df.columns
-                             ,default='y')
-with col6:
-    agg_multi = st.selectbox('Multivariate Data Aggregation', 
-                           ['Daily', 'Weekly', 'Monthly', 'Yearly'])
+# if bb_button:
+#     fig.add_trace(go.Scatter(x=data.index, y=data['BB_upper'], mode='lines', name='BB Upper'))
+#     fig.add_trace(go.Scatter(x=data.index, y=data['BB_middle'], mode='lines', name='BB Middle'))
+#     fig.add_trace(go.Scatter(x=data.index, y=data['BB_lower'], mode='lines', name='BB Lower'))
 
-st.dataframe(multi_df)
+# if macd_button:
+#     fig.add_trace(go.Scatter(x=data.index, y=data['MACD'], mode='lines', name='MACD'))
+#     fig.add_trace(go.Scatter(x=data.index, y=data['MACD_Signal'], mode='lines', name='MACD Signal'))
 
-img2 = Image.open(os.path.join('price_forecasting_ml',
-                              'artifacts',
-                              'crude_oil_df1ce299-117d-43c7-bcd5-7ecaeac0bc89',
-                              'forecast_plot.jpg'))
-st.image(img2, caption="Crude Oil Futures Price Forecasting - Multivariate (Horizon = 5)")
+# # Display the chart
+# st.plotly_chart(fig)

page_3.py

@@ -1,79 +1,70 @@
 import streamlit as st
-from PIL import Image
+import pandas as pd
 import os
+from PIL import Image
+import pandas_ta as ta
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
 
-st.title('Machine Learning Operations Pipeline')
-
-st.markdown("""
-# Machine Learning Operations (MLOps) Pipeline Documentation
-
-This is the documentation covering  each of the steps included in Bioma AI's time-series-forecasting MLOps Pipeline.
-
-## Sequential MLOps Steps
-The information flow of the pipeline will closely resemble that of a regression machine learning task. The model development will consist of sequential steps:
-1. Ingestion,
-2. Transformation,
-3. Training,
-4. Evaluation, and
-5. Registration.
-
-""")
+st.set_page_config(page_title="Technical Analysis and Forecasting", layout="wide")
 
-img = Image.open(os.path.join('experimentation_mlops',
-                              'mlops',
-                              'pics',
-                              'pipeline.png'))
-st.image(img, caption="MLOps Pipeline for Bioma AI")
-            
-st.markdown("""
+st.title('Price Forecasting - Crude Oil Futures')
+st.subheader('This page is not interactive - only for prototype purposes*')
+st.text('*Due to not having access to GPU for cloud computation yet.')
 
-## 1. Ingestion
+st.header('Univariate Forecasting with Exogenous Predictors')
 
-Our pipeline involves extracting raw datasets from the internet (S3 Buckets and other cloud services), the assumed dataset is of one of the following file types: csv, json, parquet or xlsx. The extracted data is saved as an artifact which can help in documentation purposes.
+col1, col2, col3 = st.columns(3)
 
-In the case of time series forecasting, the data ingestion process is tasked on receiving data from a specific format and converting it to a Pandas Dataframe for further processing. The data will be downloaded from the web by issuing a request, the data will then be converted into parquet before being written as a Pandas dataframe. The parquet file will be saved as an artifact for the purpose of documentation.
+uni_df = pd.read_csv(os.path.join('price_forecasting_ml', 
+                                   'artifacts',
+                                   'crude_oil_8998a364-2ecc-483d-8079-f04d455b4522',
+                                   'train_data.csv')).drop(columns=['Unnamed: 0'])
 
-## 2. Transformation
+with col1:
+    horizon_uni = st.text_input('Univariate Forecasting Horizon')
+with col2:
+    target_uni = st.multiselect('Univariate Target Variable', uni_df.columns
+                             ,default='y')
+with col3:
+    agg_uni = st.selectbox('Univariate Data Aggregation', 
+                           ['Daily', 'Weekly', 'Monthly', 'Yearly'])
 
-According to the timeframe of the time-series data, the data will be split into a train-test-validation set. The user will be able to customize each of the set's proportions.
 
-Various statistical methods is considered and performed into a selection of columns, the columns and the methods are both customizable. A few methods that are considered are:
-1. Logarithmic
-2. Natural Logarithmic
-3. Standardization
-4. Identity
-5. Logarithmic Difference
+st.dataframe(uni_df)
 
-## 3. Training
+img1 = Image.open(os.path.join('price_forecasting_ml',
+                              'artifacts',
+                              'crude_oil_8998a364-2ecc-483d-8079-f04d455b4522',
+                              'forecast_plot.jpg'))
+st.image(img1, caption="Crude Oil Futures Price Forecasting - Univariate with Exogenous Features (Horizon = 5)")
 
-The training process can be broken down into two types according to the amount of variates being predicted: univariate or multivariate.
+st.markdown("---")
 
-Predictors are either an:
+st.header('Multivariate Forecasting')
 
-1. Endogenous feature (Changes in the target's value has an effect on the predictor's value or the other way around) or 
-2. Exogenous feature (changes in the predictor's value has an effect on the target's value, but not the other way around)
-    <ol type="a">
-    <li>Static Exogenous</li>
-    Static variables such as one-hot encoding for a categorical class identifier.
-    <li>Historical Exogenous</li>
-    Exogenous features that their historical data is only known of.
-    <li>Future Exogenous</li>
-    Exogenous features that their data is known of when making the prediction on that time in the future.
-    </ol>
+col4, col5, col6 = st.columns(3)
 
-Endogenous features will be predicted in conjunction with the target's feature. Exogenous features will not be predicted, rather only be used to predict the target variable.
+multi_df = pd.read_csv(os.path.join('price_forecasting_ml', 
+                                   'artifacts',
+                                   'crude_oil_df1ce299-117d-43c7-bcd5-7ecaeac0bc89',
+                                   'train_data.csv')).drop(columns=['Unnamed: 0'])
 
-In short: multivariate predictions will use predictors as endogenous features, while multivariable predictions use predictors as exogenous features because of its univariate nature.
+with col4:
+    horizon_multi = st.text_input('Multivariate Forecasting Horizon')
+with col5:
+    target_multi = st.multiselect('Multivariate Target Variable', multi_df.columns
+                             ,default='y')
+with col6:
+    agg_multi = st.selectbox('Multivariate Data Aggregation', 
+                           ['Daily', 'Weekly', 'Monthly', 'Yearly'])
 
-## 4. Evaluation
+st.dataframe(multi_df)
 
-The evaluation step is constructed for the trained models to perform prediction on out-of-training data. Ideally, this step will produce outputs such as visualizations and error metrics for arbitrary datasets. 
+img2 = Image.open(os.path.join('price_forecasting_ml',
+                              'artifacts',
+                              'crude_oil_df1ce299-117d-43c7-bcd5-7ecaeac0bc89',
+                              'forecast_plot.jpg'))
+st.image(img2, caption="Crude Oil Futures Price Forecasting - Multivariate (Horizon = 5)")
 
-## 5. Registration
-            
-Registration includes saving the model with the highest accuracy, making it easy to retrieve for inference later on.
-            
-References:
-- [1] [mlflow/recipes-regression-template](https://github.com/mlflow/recipes-regression-template/tree/main?tab=readme-ov-file#installation)
-- [2] [MLflow deployment using Docker, EC2, S3, and RDS](https://aws.plainenglish.io/set-up-mlflow-on-aws-ec2-using-docker-s3-and-rds-90d96798e555)
-            """)
+st.markdown("---")

page_4.py

@@ -0,0 +1,79 @@
+import streamlit as st
+from PIL import Image
+import os
+
+st.title('Machine Learning Operations Pipeline')
+
+st.markdown("""
+# Machine Learning Operations (MLOps) Pipeline Documentation
+
+This is the documentation covering  each of the steps included in Bioma AI's time-series-forecasting MLOps Pipeline.
+
+## Sequential MLOps Steps
+The information flow of the pipeline will closely resemble that of a regression machine learning task. The model development will consist of sequential steps:
+1. Ingestion,
+2. Transformation,
+3. Training,
+4. Evaluation, and
+5. Registration.
+
+""")
+
+img = Image.open(os.path.join('experimentation_mlops',
+                              'mlops',
+                              'pics',
+                              'pipeline.png'))
+st.image(img, caption="MLOps Pipeline for Bioma AI")
+            
+st.markdown("""
+
+## 1. Ingestion
+
+Our pipeline involves extracting raw datasets from the internet (S3 Buckets and other cloud services), the assumed dataset is of one of the following file types: csv, json, parquet or xlsx. The extracted data is saved as an artifact which can help in documentation purposes.
+
+In the case of time series forecasting, the data ingestion process is tasked on receiving data from a specific format and converting it to a Pandas Dataframe for further processing. The data will be downloaded from the web by issuing a request, the data will then be converted into parquet before being written as a Pandas dataframe. The parquet file will be saved as an artifact for the purpose of documentation.
+
+## 2. Transformation
+
+According to the timeframe of the time-series data, the data will be split into a train-test-validation set. The user will be able to customize each of the set's proportions.
+
+Various statistical methods is considered and performed into a selection of columns, the columns and the methods are both customizable. A few methods that are considered are:
+1. Logarithmic
+2. Natural Logarithmic
+3. Standardization
+4. Identity
+5. Logarithmic Difference
+
+## 3. Training
+
+The training process can be broken down into two types according to the amount of variates being predicted: univariate or multivariate.
+
+Predictors are either an:
+
+1. Endogenous feature (Changes in the target's value has an effect on the predictor's value or the other way around) or 
+2. Exogenous feature (changes in the predictor's value has an effect on the target's value, but not the other way around)
+    <ol type="a">
+    <li>Static Exogenous</li>
+    Static variables such as one-hot encoding for a categorical class identifier.
+    <li>Historical Exogenous</li>
+    Exogenous features that their historical data is only known of.
+    <li>Future Exogenous</li>
+    Exogenous features that their data is known of when making the prediction on that time in the future.
+    </ol>
+
+Endogenous features will be predicted in conjunction with the target's feature. Exogenous features will not be predicted, rather only be used to predict the target variable.
+
+In short: multivariate predictions will use predictors as endogenous features, while multivariable predictions use predictors as exogenous features because of its univariate nature.
+
+## 4. Evaluation
+
+The evaluation step is constructed for the trained models to perform prediction on out-of-training data. Ideally, this step will produce outputs such as visualizations and error metrics for arbitrary datasets. 
+
+## 5. Registration
+            
+Registration includes saving the model with the highest accuracy, making it easy to retrieve for inference later on.
+            
+References:
+- [1] [mlflow/recipes-regression-template](https://github.com/mlflow/recipes-regression-template/tree/main?tab=readme-ov-file#installation)
+- [2] [MLflow deployment using Docker, EC2, S3, and RDS](https://aws.plainenglish.io/set-up-mlflow-on-aws-ec2-using-docker-s3-and-rds-90d96798e555)
+            """)

page_5.py

@@ -0,0 +1 @@
+import streamlit as st

price_forecasting_ml/data/DatedBrent/dated_brent_price.csv

@@ -0,0 +1,2679 @@
+,date,BrDa
+0,2014-01-02,108.02
+1,2014-01-03,106.605
+2,2014-01-06,106.57
+3,2014-01-07,106.935
+4,2014-01-08,107.355
+5,2014-01-09,107.33
+6,2014-01-10,106.55
+7,2014-01-13,108.355
+8,2014-01-14,107.39
+9,2014-01-15,108.245
+10,2014-01-16,107.575
+11,2014-01-17,108.525
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+13,2014-01-21,109.72
+14,2014-01-22,110.2
+15,2014-01-23,110.115
+16,2014-01-24,109.39
+17,2014-01-27,108.69
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+280,2015-02-10,55.835
+281,2015-02-11,54.0
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price_forecasting_ml/data/EURUSD.csv

@@ -0,0 +1,1456 @@
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+06/23/2019,1.1381,N/A,1.1366,1.1386,1.1367
+06/21/2019,1.1368,N/A,1.1291,1.1378,1.1283
+06/20/2019,1.1302,N/A,1.1291,1.1309,1.1288
+06/19/2019,1.1264,N/A,1.1224,1.1266,1.1226
+06/18/2019,1.1197,N/A,1.1191,1.1202,1.1192
+06/17/2019,1.123,N/A,1.1217,1.1231,1.1218
+06/16/2019,1.1223,N/A,1.1207,1.1223,1.1208
+06/14/2019,1.1209,N/A,1.1275,1.1289,1.1203
+06/13/2019,1.1279,N/A,1.1275,1.1287,1.1274
+06/12/2019,1.1296,N/A,1.1287,1.1299,1.1287
+06/11/2019,1.1328,N/A,1.1329,1.1337,1.1325
+06/10/2019,1.1308,N/A,1.1312,1.1319,1.1307
+06/09/2019,1.1314,N/A,1.1331,1.1331,1.1311
+06/07/2019,1.1333,N/A,1.1274,1.1348,1.1252
+06/06/2019,1.1272,N/A,1.1274,1.1283,1.1271
+06/05/2019,1.1225,N/A,1.1219,1.1233,1.122
+06/04/2019,1.1263,N/A,1.1251,1.1265,1.1251
+06/03/2019,1.1247,N/A,1.124,1.1256,1.124
+06/02/2019,1.1185,N/A,1.1167,1.1186,1.1162
+05/31/2019,1.1179,N/A,1.113,1.1182,1.1125
+05/30/2019,1.1134,N/A,1.113,1.1141,1.1127
+05/29/2019,1.1138,N/A,1.1129,1.1143,1.1131
+05/28/2019,1.1162,N/A,1.116,1.1173,1.116
+05/27/2019,1.1183,N/A,1.1196,1.1203,1.1181
+05/26/2019,1.1212,N/A,1.1202,1.1215,1.1203
+05/24/2019,1.1211,N/A,1.118,1.1213,1.1178
+05/23/2019,1.1181,N/A,1.118,1.1187,1.1178
+05/22/2019,1.1151,N/A,1.1152,1.116,1.1148
+05/21/2019,1.1164,N/A,1.1158,1.117,1.1159
+05/20/2019,1.1162,N/A,1.117,1.1176,1.1162
+05/19/2019,1.1159,N/A,1.1156,1.1168,1.1158
+05/17/2019,1.1159,N/A,1.1172,1.1184,1.1155
+05/16/2019,1.1179,N/A,1.1172,1.1184,1.1171
+05/15/2019,1.1208,N/A,1.12,1.1216,1.1201
+05/14/2019,1.1206,N/A,1.1203,1.1221,1.12
+05/13/2019,1.1231,N/A,1.1223,1.1238,1.122
+05/12/2019,1.1235,N/A,1.1233,1.1245,1.1229
+05/10/2019,1.1235,N/A,1.122,1.1253,1.1215
+05/09/2019,1.1231,N/A,1.122,1.1236,1.1215
+05/08/2019,1.1187,N/A,1.1191,1.1201,1.1187
+05/07/2019,1.12,N/A,1.119,1.1201,1.1188
+05/06/2019,1.1205,N/A,1.1197,1.1211,1.1192
+05/05/2019,1.1189,N/A,1.12,1.1203,1.1171
+05/03/2019,1.1202,N/A,1.1202,1.1207,1.1135
+03/18/2019,1.1328,N/A,1.1325,1.136,1.1319
+03/15/2019,1.1322,N/A,1.1302,1.1345,1.1296
+03/14/2019,1.1305,N/A,1.1325,1.134,1.1294
+03/13/2019,1.1305,N/A,1.1286,1.1318,1.1278
+03/12/2019,1.1286,N/A,1.1286,1.1292,1.1284
+03/11/2019,1.1247,N/A,1.1247,1.1255,1.1244
+03/10/2019,1.1231,N/A,1.1238,1.1239,1.1231
+03/08/2019,1.1243,N/A,1.1243,1.1246,1.1185

price_forecasting_ml/data/U.S._Crude_Production_ThousandPerDay.csv

@@ -0,0 +1,1254 @@
+Month,U.S. Field Production of Crude Oil Thousand Barrels per Day
+May 2024,13178
+Apr 2024,13239
+Mar 2024,13171
+Feb 2024,13102
+Jan 2024,12554
+Dec 2023,13308
+Nov 2023,13281
+Oct 2023,13149
+Sep 2023,13177
+Aug 2023,13047
+Jul 2023,12935
+Jun 2023,12866
+May 2023,12730
+Apr 2023,12680
+Mar 2023,12815
+Feb 2023,12591
+Jan 2023,12611
+Dec 2022,12175
+Nov 2022,12467
+Oct 2022,12431
+Sep 2022,12439
+Aug 2022,12123
+Jul 2022,11992
+Jun 2022,11913
+May 2022,11742
+Apr 2022,11812
+Mar 2022,11875
+Feb 2022,11467
+Jan 2022,11442
+Dec 2021,11678
+Nov 2021,11782
+Oct 2021,11564
+Sep 2021,10921
+Aug 2021,11276
+Jul 2021,11392
+Jun 2021,11366
+May 2021,11390
+Apr 2021,11318
+Mar 2021,11351
+Feb 2021,9916
+Jan 2021,11137
+Dec 2020,11172
+Nov 2020,11196
+Oct 2020,10456
+Sep 2020,10926
+Aug 2020,10579
+Jul 2020,11004
+Jun 2020,10446
+May 2020,9714
+Apr 2020,11911
+Mar 2020,12795
+Feb 2020,12844
+Jan 2020,12850
+Dec 2019,12980
+Nov 2019,13000
+Oct 2019,12805
+Sep 2019,12584
+Aug 2019,12479
+Jul 2019,11896
+Jun 2019,12216
+May 2019,12153
+Apr 2019,12145
+Mar 2019,11911
+Feb 2019,11652
+Jan 2019,11871
+Dec 2018,11944
+Nov 2018,11885
+Oct 2018,11508
+Sep 2018,11443
+Aug 2018,11391
+Jul 2018,10896
+Jun 2018,10640
+May 2018,10434
+Apr 2018,10499
+Mar 2018,10466
+Feb 2018,10262
+Jan 2018,10000
+Dec 2017,9983
+Nov 2017,10085
+Oct 2017,9668
+Sep 2017,9516
+Aug 2017,9250
+Jul 2017,9246
+Jun 2017,9110
+May 2017,9185
+Apr 2017,9101
+Mar 2017,9164
+Feb 2017,9094
+Jan 2017,8874
+Dec 2016,8815
+Nov 2016,8902
+Oct 2016,8803
+Sep 2016,8543
+Aug 2016,8689
+Jul 2016,8661
+Jun 2016,8675
+May 2016,8834
+Apr 2016,8873
+Mar 2016,9100
+Feb 2016,9060
+Jan 2016,9201
+Dec 2015,9275
+Nov 2015,9332
+Oct 2015,9399
+Sep 2015,9477
+Aug 2015,9409
+Jul 2015,9445
+Jun 2015,9357
+May 2015,9474
+Apr 2015,9658
+Mar 2015,9583
+Feb 2015,9490
+Jan 2015,9383
+Dec 2014,9563
+Nov 2014,9324
+Oct 2014,9265
+Sep 2014,9086
+Aug 2014,8931
+Jul 2014,8846
+Jun 2014,8742
+May 2014,8645
+Apr 2014,8526
+Mar 2014,8296
+Feb 2014,8141
+Jan 2014,8077
+Dec 2013,7932
+Nov 2013,7919
+Oct 2013,7760
+Sep 2013,7838
+Aug 2013,7534
+Jul 2013,7522
+Jun 2013,7277
+May 2013,7326
+Apr 2013,7374
+Mar 2013,7214
+Feb 2013,7145
+Jan 2013,7083
+Dec 2012,7106
+Nov 2012,7082
+Oct 2012,6970
+Sep 2012,6598
+Aug 2012,6351
+Jul 2012,6431
+Jun 2012,6275
+May 2012,6370
+Apr 2012,6320
+Mar 2012,6327
+Feb 2012,6268
+Jan 2012,6177
+Dec 2011,6112
+Nov 2011,6039
+Oct 2011,5902
+Sep 2011,5614
+Aug 2011,5665
+Jul 2011,5440
+Jun 2011,5589
+May 2011,5625
+Apr 2011,5562
+Mar 2011,5618
+Feb 2011,5406
+Jan 2011,5497
+Dec 2010,5607
+Nov 2010,5576
+Oct 2010,5631
+Sep 2010,5616
+Aug 2010,5447
+Jul 2010,5298
+Jun 2010,5380
+May 2010,5401
+Apr 2010,5392
+Mar 2010,5515
+Feb 2010,5553
+Jan 2010,5404
+Dec 2009,5452
+Nov 2009,5395
+Oct 2009,5524
+Sep 2009,5559
+Aug 2009,5418
+Jul 2009,5398
+Jun 2009,5269
+May 2009,5377
+Apr 2009,5278
+Mar 2009,5219
+Feb 2009,5239
+Jan 2009,5144
+Dec 2008,5103
+Nov 2008,5078
+Oct 2008,4738
+Sep 2008,3974
+Aug 2008,5003
+Jul 2008,5177
+Jun 2008,5138
+May 2008,5143
+Apr 2008,5180
+Mar 2008,5189
+Feb 2008,5148
+Jan 2008,5115
+Dec 2007,5111
+Nov 2007,5034
+Oct 2007,5022
+Sep 2007,4902
+Aug 2007,4970
+Jul 2007,5038
+Jun 2007,5075
+May 2007,5211
+Apr 2007,5180
+Mar 2007,5119
+Feb 2007,5118
+Jan 2007,5108
+Dec 2006,5186
+Nov 2006,5064
+Oct 2006,5108
+Sep 2006,5031
+Aug 2006,5040
+Jul 2006,5094
+Jun 2006,5163
+May 2006,5151
+Apr 2006,5082
+Mar 2006,5027
+Feb 2006,5032
+Jan 2006,5048
+Dec 2005,4988
+Nov 2005,4857
+Oct 2005,4555
+Sep 2005,4214
+Aug 2005,5198
+Jul 2005,5253
+Jun 2005,5442
+May 2005,5596
+Apr 2005,5563
+Mar 2005,5601
+Feb 2005,5501
+Jan 2005,5446
+Dec 2004,5510
+Nov 2004,5423
+Oct 2004,5170
+Sep 2004,5081
+Aug 2004,5325
+Jul 2004,5484
+Jun 2004,5407
+May 2004,5556
+Apr 2004,5560
+Mar 2004,5617
+Feb 2004,5572
+Jan 2004,5585
+Dec 2003,5571
+Nov 2003,5547
+Oct 2003,5614
+Sep 2003,5609
+Aug 2003,5574
+Jul 2003,5498
+Jun 2003,5659
+May 2003,5663
+Apr 2003,5726
+Mar 2003,5803
+Feb 2003,5783
+Jan 2003,5755
+Dec 2002,5722
+Nov 2002,5624
+Oct 2002,5358
+Sep 2002,5411
+Aug 2002,5796
+Jul 2002,5751
+Jun 2002,5884
+May 2002,5905
+Apr 2002,5844
+Mar 2002,5886
+Feb 2002,5881
+Jan 2002,5873
+Dec 2001,5887
+Nov 2001,5881
+Oct 2001,5746
+Sep 2001,5709
+Aug 2001,5725
+Jul 2001,5749
+Jun 2001,5766
+May 2001,5829
+Apr 2001,5863
+Mar 2001,5880
+Feb 2001,5780
+Jan 2001,5799
+Dec 2000,5855
+Nov 2000,5833
+Oct 2000,5809
+Sep 2000,5758
+Aug 2000,5789
+Jul 2000,5739
+Jun 2000,5823
+May 2000,5847
+Apr 2000,5854
+Mar 2000,5918
+Feb 2000,5852
+Jan 2000,5784
+Dec 1999,5959
+Nov 1999,5960
+Oct 1999,5947
+Sep 1999,5804
+Aug 1999,5780
+Jul 1999,5798
+Jun 1999,5760
+May 1999,5875
+Apr 1999,5887
+Mar 1999,5883
+Feb 1999,5966
+Jan 1999,5963
+Dec 1998,6043
+Nov 1998,6140
+Oct 1998,6143
+Sep 1998,5789
+Aug 1998,6203
+Jul 1998,6194
+Jun 1998,6267
+May 1998,6347
+Apr 1998,6483
+Mar 1998,6408
+Feb 1998,6476
+Jan 1998,6541
+Dec 1997,6531
+Nov 1997,6459
+Oct 1997,6467
+Sep 1997,6486
+Aug 1997,6347
+Jul 1997,6409
+Jun 1997,6442
+May 1997,6474
+Apr 1997,6441
+Mar 1997,6452
+Feb 1997,6514
+Jan 1997,6402
+Dec 1996,6506
+Nov 1996,6476
+Oct 1996,6481
+Sep 1996,6482
+Aug 1996,6360
+Jul 1996,6338
+Jun 1996,6458
+May 1996,6394
+Apr 1996,6444
+Mar 1996,6571
+Feb 1996,6577
+Jan 1996,6495
+Dec 1995,6530
+Nov 1995,6585
+Oct 1995,6421
+Sep 1995,6416
+Aug 1995,6447
+Jul 1995,6449
+Jun 1995,6579
+May 1995,6629
+Apr 1995,6604
+Mar 1995,6600
+Feb 1995,6794
+Jan 1995,6682
+Dec 1994,6760
+Nov 1994,6628
+Oct 1994,6658
+Sep 1994,6609
+Aug 1994,6544
+Jul 1994,6501
+Jun 1994,6611
+May 1994,6688
+Apr 1994,6612
+Mar 1994,6746
+Feb 1994,6770
+Jan 1994,6817
+Dec 1993,6858
+Nov 1993,6912
+Oct 1993,6839
+Sep 1993,6712
+Aug 1993,6758
+Jul 1993,6688
+Jun 1993,6795
+May 1993,6847
+Apr 1993,6881
+Mar 1993,6974
+Feb 1993,6943
+Jan 1993,6961
+Dec 1992,7103
+Nov 1992,7024
+Oct 1992,7126
+Sep 1992,7030
+Aug 1992,6922
+Jul 1992,7131
+Jun 1992,7167
+May 1992,7169
+Apr 1992,7293
+Mar 1992,7348
+Feb 1992,7389
+Jan 1992,7361
+Dec 1991,7299
+Nov 1991,7328
+Oct 1991,7437
+Sep 1991,7368
+Aug 1991,7316
+Jul 1991,7347
+Jun 1991,7320
+May 1991,7409
+Apr 1991,7509
+Mar 1991,7546
+Feb 1991,7637
+Jan 1991,7500
+Dec 1990,7338
+Nov 1990,7387
+Oct 1990,7542
+Sep 1990,7224
+Aug 1990,7287
+Jul 1990,7173
+Jun 1990,7106
+May 1990,7328
+Apr 1990,7407
+Mar 1990,7433
+Feb 1990,7497
+Jan 1990,7546
+Dec 1989,7337
+Nov 1989,7536
+Oct 1989,7453
+Sep 1989,7548
+Aug 1989,7544
+Jul 1989,7444
+Jun 1989,7624
+May 1989,7816
+Apr 1989,7772
+Mar 1989,7575
+Feb 1989,7788
+Jan 1989,7937
+Dec 1988,7942
+Nov 1988,8023
+Oct 1988,8023
+Sep 1988,7895
+Aug 1988,8079
+Jul 1988,8040
+Jun 1988,8170
+May 1988,8229
+Apr 1988,8288
+Mar 1988,8374
+Feb 1988,8374
+Jan 1988,8250
+Dec 1987,8318
+Nov 1987,8397
+Oct 1987,8364
+Sep 1987,8205
+Aug 1987,8210
+Jul 1987,8251
+Jun 1987,8279
+May 1987,8336
+Apr 1987,8498
+Mar 1987,8464
+Feb 1987,8389
+Jan 1987,8480
+Dec 1986,8352
+Nov 1986,8412
+Oct 1986,8419
+Sep 1986,8328
+Aug 1986,8374
+Jul 1986,8660
+Jun 1986,8623
+May 1986,8838
+Apr 1986,8864
+Mar 1986,9013
+Feb 1986,9173
+Jan 1986,9137
+Dec 1985,9030
+Nov 1985,8902
+Oct 1985,8970
+Sep 1985,8954
+Aug 1985,8803
+Jul 1985,8949
+Jun 1985,9022
+May 1985,9132
+Apr 1985,9043
+Mar 1985,9095
+Feb 1985,9025
+Jan 1985,8740
+Dec 1984,8897
+Nov 1984,8979
+Oct 1984,8906
+Sep 1984,8993
+Aug 1984,8809
+Jul 1984,8885
+Jun 1984,8852
+May 1984,8955
+Apr 1984,8862
+Mar 1984,8672
+Feb 1984,8874
+Jan 1984,8868
+Dec 1983,8397
+Nov 1983,8770
+Oct 1983,8771
+Sep 1983,8784
+Aug 1983,8679
+Jul 1983,8636
+Jun 1983,8667
+May 1983,8631
+Apr 1983,8776
+Mar 1983,8700
+Feb 1983,8758
+Jan 1983,8697
+Dec 1982,8598
+Nov 1982,8697
+Oct 1982,8701
+Sep 1982,8701
+Aug 1982,8634
+Jul 1982,8658
+Jun 1982,8646
+May 1982,8683
+Apr 1982,8591
+Mar 1982,8667
+Feb 1982,8702
+Jan 1982,8509
+Dec 1981,8585
+Nov 1981,8586
+Oct 1981,8563
+Sep 1981,8604
+Aug 1981,8583
+Jul 1981,8500
+Jun 1981,8629
+May 1981,8501
+Apr 1981,8557
+Mar 1981,8613
+Feb 1981,8604
+Jan 1981,8540
+Dec 1980,8606
+Nov 1980,8495
+Oct 1980,8532
+Sep 1980,8619
+Aug 1980,8414
+Jul 1980,8547
+Jun 1980,8554
+May 1980,8635
+Apr 1980,8685
+Mar 1980,8698
+Feb 1980,8705
+Jan 1980,8675
+Dec 1979,8615
+Nov 1979,8761
+Oct 1979,8621
+Sep 1979,8523
+Aug 1979,8548
+Jul 1979,8364
+Jun 1979,8432
+May 1979,8601
+Apr 1979,8553
+Mar 1979,8601
+Feb 1979,8525
+Jan 1979,8475
+Dec 1978,8662
+Nov 1978,8741
+Oct 1978,8820
+Sep 1978,8800
+Aug 1978,8758
+Jul 1978,8756
+Jun 1978,8832
+May 1978,8825
+Apr 1978,8818
+Mar 1978,8720
+Feb 1978,8377
+Jan 1978,8360
+Dec 1977,8487
+Nov 1977,8579
+Oct 1977,8573
+Sep 1977,8480
+Aug 1977,8307
+Jul 1977,8105
+Jun 1977,8102
+May 1977,8075
+Apr 1977,8145
+Mar 1977,8090
+Feb 1977,8139
+Jan 1977,7854
+Dec 1976,8061
+Nov 1976,8080
+Oct 1976,8063
+Sep 1976,8150
+Aug 1976,8111
+Jul 1976,8127
+Jun 1976,8094
+May 1976,8125
+Apr 1976,8077
+Mar 1976,8232
+Feb 1976,8231
+Jan 1976,8232
+Dec 1975,8254
+Nov 1975,8278
+Oct 1975,8324
+Sep 1975,8280
+Aug 1975,8249
+Jul 1975,8336
+Jun 1975,8421
+May 1975,8379
+Apr 1975,8457
+Mar 1975,8493
+Feb 1975,8591
+Jan 1975,8455
+Dec 1974,8527
+Nov 1974,8569
+Oct 1974,8611
+Sep 1974,8443
+Aug 1974,8699
+Jul 1974,8780
+Jun 1974,8780
+May 1974,8911
+Apr 1974,8954
+Mar 1974,8965
+Feb 1974,9142
+Jan 1974,8934
+Dec 1973,9063
+Nov 1973,9161
+Oct 1973,9224
+Sep 1973,9065
+Aug 1973,9169
+Jul 1973,9217
+Jun 1973,9214
+May 1973,9262
+Apr 1973,9292
+Mar 1973,9272
+Feb 1973,9395
+Jan 1973,9176
+Dec 1972,9335
+Nov 1972,9426
+Oct 1972,9482
+Sep 1972,9508
+Aug 1972,9483
+Jul 1972,9496
+Jun 1972,9522
+May 1972,9614
+Apr 1972,9513
+Mar 1972,9462
+Feb 1972,9336
+Jan 1972,9114
+Dec 1971,9100
+Nov 1971,9139
+Oct 1971,9162
+Sep 1971,9135
+Aug 1971,9411
+Jul 1971,9456
+Jun 1971,9604
+May 1971,9645
+Apr 1971,9769
+Mar 1971,9768
+Feb 1971,9729
+Jan 1971,9655
+Dec 1970,9944
+Nov 1970,10044
+Oct 1970,10013
+Sep 1970,9853
+Aug 1970,9560
+Jul 1970,9202
+Jun 1970,9359
+May 1970,9523
+Apr 1970,9591
+Mar 1970,9508
+Feb 1970,9570
+Jan 1970,9478
+Dec 1969,9528
+Nov 1969,9346
+Oct 1969,9213
+Sep 1969,9295
+Aug 1969,9067
+Jul 1969,9295
+Jun 1969,9631
+May 1969,9356
+Apr 1969,9238
+Mar 1969,9055
+Feb 1969,8928
+Jan 1969,8888
+Dec 1968,8906
+Nov 1968,8969
+Oct 1968,8916
+Sep 1968,8932
+Aug 1968,9134
+Jul 1968,9156
+Jun 1968,9144
+May 1968,9205
+Apr 1968,9123
+Mar 1968,9319
+Feb 1968,9325
+Jan 1968,9028
+Dec 1967,8908
+Nov 1967,8978
+Oct 1967,9000
+Sep 1967,9095
+Aug 1967,9435
+Jul 1967,9154
+Jun 1967,8539
+May 1967,8385
+Apr 1967,8475
+Mar 1967,8544
+Feb 1967,8620
+Jan 1967,8567
+Dec 1966,8511
+Nov 1966,8427
+Oct 1966,8323
+Sep 1966,8254
+Aug 1966,8252
+Jul 1966,8230
+Jun 1966,8346
+May 1966,8344
+Apr 1966,8272
+Mar 1966,8294
+Feb 1966,8240
+Jan 1966,8047
+Dec 1965,8180
+Nov 1965,7988
+Oct 1965,7875
+Sep 1965,7418
+Aug 1965,7748
+Jul 1965,7665
+Jun 1965,7748
+May 1965,7686
+Apr 1965,7895
+Mar 1965,7863
+Feb 1965,7808
+Jan 1965,7773
+Dec 1964,7770
+Nov 1964,7634
+Oct 1964,7623
+Sep 1964,7532
+Aug 1964,7449
+Jul 1964,7473
+Jun 1964,7560
+May 1964,7572
+Apr 1964,7739
+Mar 1964,7712
+Feb 1964,7688
+Jan 1964,7624
+Dec 1963,7511
+Nov 1963,7544
+Oct 1963,7536
+Sep 1963,7507
+Aug 1963,7640
+Jul 1963,7585
+Jun 1963,7562
+May 1963,7564
+Apr 1963,7609
+Mar 1963,7558
+Feb 1963,7587
+Jan 1963,7304
+Dec 1962,7381
+Nov 1962,7441
+Oct 1962,7367
+Sep 1962,7320
+Aug 1962,7234
+Jul 1962,7226
+Jun 1962,7257
+May 1962,7193
+Apr 1962,7391
+Mar 1962,7376
+Feb 1962,7467
+Jan 1962,7347
+Dec 1961,7311
+Nov 1961,7152
+Oct 1961,7127
+Sep 1961,6995
+Aug 1961,7104
+Jul 1961,6958
+Jun 1961,7103
+May 1961,7147
+Apr 1961,7328
+Mar 1961,7471
+Feb 1961,7296
+Jan 1961,7210
+Dec 1960,7150
+Nov 1960,7133
+Oct 1960,6958
+Sep 1960,6971
+Aug 1960,6940
+Jul 1960,6860
+Jun 1960,6939
+May 1960,6848
+Apr 1960,7038
+Mar 1960,7128
+Feb 1960,7241
+Jan 1960,7230
+Dec 1959,7193
+Nov 1959,6982
+Oct 1959,6911
+Sep 1959,6857
+Aug 1959,6766
+Jul 1959,6784
+Jun 1959,7083
+May 1959,7220
+Apr 1959,7256
+Mar 1959,7188
+Feb 1959,7194
+Jan 1959,7223
+Dec 1958,7138
+Nov 1958,6975
+Oct 1958,6964
+Sep 1958,7088
+Aug 1958,6936
+Jul 1958,6571
+Jun 1958,6339
+May 1958,6232
+Apr 1958,6300
+Mar 1958,6277
+Feb 1958,6820
+Jan 1958,6880
+Dec 1957,6924
+Nov 1957,6842
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req.txt

@@ -0,0 +1,21 @@
+seaborn
+torch
+torchvision
+torchaudio
+pyYAML
+scipy
+scikit-learn
+packaging
+pyWavelets
+scipy
+tiffile
+pandas
+nltk
+ipykernel
+matplotlib
+streamlit
+plotly
+nbformat
+wordcloud
+openpyxl
+neuralforecast

requirements.txt

@@ -1,21 +1,6 @@
-seaborn
-torch
-torchvision
-torchaudio
-pyYAML
-scipy
-scikit-learn
-packaging
-pyWavelets
-scipy
-tiffile
+numpy
 pandas
-nltk
-ipykernel
-matplotlib
-streamlit
 plotly
-nbformat
-wordcloud
 openpyxl
-neuralforecast
+yfinance
+pandas_ta