Update app.py

app.py

@@ -3,146 +3,173 @@ import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
+import numpy as np
+import ast
+from pagination import paginator
 
-default_start_date = pd.to_datetime('2020-11-02')
-date_limit = pd.to_datetime('2021-08-12')
 
-st.title("Stock Analysis Dashboard")
+# Utils Functions
+def signals_to_plot(selected_indicator, num_signals, signal_column, data):
+    if selected_indicator != 'RSI':
+        if num_signals == 'All':
+            buy_dates = data[data[signal_column] == 3.0]
+            sell_dates = data[data[signal_column] == -3.0]
+            hold_dates = data[data[signal_column] == 0]
+        elif num_signals == 'Last 5 Days':
+            last5 = data.tail(5)
+            buy_dates = last5[last5[signal_column] == 3.0]
+            sell_dates = last5[last5[signal_column] == -3.0]
+            hold_dates = last5[last5[signal_column] == 0]
+        elif num_signals == 'Last 15 Days':
+            last15 = data.tail(15)
+            buy_dates = last15[last15[signal_column] == 3.0]
+            sell_dates = last15[last15[signal_column] == -3.0]
+            hold_dates = last15[last15[signal_column] == 0]
+        elif num_signals == 'Last 20 Days':
+            last20 = data.tail(20)
+            buy_dates = last20[last20[signal_column] == 3.0]
+            sell_dates = last20[last20[signal_column] == -3.0]
+            hold_dates = last20[last20[signal_column] == 0]
+
+    elif selected_indicator == 'RSI':
+        if num_signals == 'All':
+            buy_dates = data[data[signal_column] >= 1.0]
+            sell_dates = data[data[signal_column] <= -1.0]
+            hold_dates = data[data[signal_column] == 0]
+        elif num_signals == 'Last 5 Days':
+            last5 = data.tail(5)
+            buy_dates = last5[last5[signal_column] >= 1.0]
+            sell_dates = last5[last5[signal_column] <= -1.0]
+            hold_dates = last5[last5[signal_column] == 0]
+        elif num_signals == 'Last 15 Days':
+            last15 = data.tail(15)
+            buy_dates = last15[last15[signal_column] >= 1.0]
+            sell_dates = last15[last15[signal_column] <= -1.0]
+            hold_dates = last15[last15[signal_column] == 0]
+        elif num_signals == 'Last 20 Days':
+            last20 = data.tail(20)
+            buy_dates = last20[last20[signal_column] >= 1.0]
+            sell_dates = last20[last20[signal_column] <= -1.0]
+            hold_dates = last20[last20[signal_column] == 0]
+    return buy_dates, sell_dates, hold_dates
 
+def convert_str_to_list(string):
+    try:
+        # Use ast.literal_eval to safely evaluate the string as a list
+        return ast.literal_eval(string)
+    except (ValueError, SyntaxError):
+        # If the string cannot be converted to a list, return it as is
+        return string
+    
+
+
+st.title("Stock Analysis Dashboard")
 # Load data
 data_file_path = r"technicalRecommendation.csv"  # Update this with your file path
 data = pd.read_csv(data_file_path)
+
 # Convert 'Date' column to datetime format
 data['Date'] = pd.to_datetime(data['Date'])
-print(data.shape, data.columns)
-market_analysis, news_analysis, trade_recs = st.tabs(["Market Analysis", "News Analysis", "Trading Recommendations"])
+# print(data.shape, data.columns)
+
+default_start_date = data['Date'].min()  # Set default start date for the start date picker
+date_limit = pd.to_datetime('2021-08-12')   # Set date limit for end date picker
+
+
+# Create Tabs
+market_analysis, news_analysis, trade_recs, chat = st.tabs(["Market Analysis", "News Analysis", "Trading Recommendations", "Gup Shup"])
 
 with market_analysis:
     st.header("Market Analysis")
-    start_date = st.sidebar.date_input('Start Date', value=default_start_date, min_value=data['Date'].min(), max_value=date_limit)
-    end_date = st.sidebar.date_input("End Date", value=date_limit, min_value=data['Date'].min(), max_value=date_limit)
+    date_container = st.container(height = 90, border=False)
+        # main_container.write(f"Date: {date}")
+        
+    col1, col2 = date_container.columns([0.5, 0.5], gap='medium')
+    start_date = col1.date_input('Start Date', value=default_start_date, min_value=data['Date'].min(), max_value=date_limit)
+    end_date = col2.date_input("End Date", value=date_limit, min_value=data['Date'].min(), max_value=date_limit)
     start_date = pd.to_datetime(start_date)
     end_date = pd.to_datetime(end_date)
     data2 = data[data['Date'].between(start_date, end_date)]
 
     # Dropdown for selecting the indicator
-    selected_indicator = st.sidebar.selectbox("Select an Indicator", ['EMA 9', 'EMA 55', 'MACD', 'RSI'])
+    selected_indicator = st.selectbox("Select an Indicator", ['EMA 9', 'EMA 55', 'MACD', 'RSI'])
     # Dropdown for selecting the Number of Signal Days
-    num_signals = st.sidebar.selectbox("Signals to Show", ['None', 'All', 'Last 5 Days', 'Last 15 Days', 'Last 20 Days'])
+    num_signals = st.selectbox("Signals to Show", ['None', 'All', 'Last 5 Days', 'Last 15 Days', 'Last 20 Days'])
     
     if selected_indicator == 'EMA 9':
         # Plot close price and EMA 9
         fig = px.line(data2, x='Date', y=['Close_price', 'EMA_9'], title='Close Price vs EMA 9',
                     labels={'Date': 'Date', 'value': 'Price', 'variable': 'Type'})
         fig.update_traces(selector=dict(type='scatter'))
-        ema9_signal = 'EMA9_Signal'
         # Plot ‘strong buy’ signals
         if num_signals != 'None':
-            if num_signals == 'All':
-                strong_buy_dates = data2[data2[ema9_signal] == 3.0]
-            elif num_signals == 'Last 5 Days':
-                last5 = data2.tail(5)
-                strong_buy_dates = last5[last5[ema9_signal] == 3.0]
-            elif num_signals == 'Last 15 Days':
-                last15 = data2.tail(15)
-                strong_buy_dates = last15[last15[ema9_signal] == 3.0]
-            elif num_signals == 'Last 20 Days':
-                last20 = data2.tail(20)
-                strong_buy_dates = last20[last20[ema9_signal] == 3.0]
-
-            fig.add_scatter(x=strong_buy_dates['Date'], y=strong_buy_dates['EMA_9'], mode='markers', marker=dict(symbol='triangle-up', size=10, color='green'), name='Strong buy')
-
-        # Plot ‘strong sell’ signals
-        if num_signals != 'None':
-            if num_signals == 'All':
-                strong_sell_dates = data2[data2[ema9_signal] == -3.0]
-            elif num_signals == 'Last 5 Days':
-                last5 = data2.tail(5)
-                strong_sell_dates = last5[last5[ema9_signal] == -3.0]
-            elif num_signals == 'Last 15 Days':
-                last15 = data2.tail(15)
-                strong_sell_dates = last15[last15[ema9_signal] == -3.0]
-            elif num_signals == 'Last 20 Days':
-                last20 = data2.tail(20)
-                strong_sell_dates = last20[last20[ema9_signal] == -3.0]
+            strong_buy_dates, strong_sell_dates, strong_hold_dates = signals_to_plot(
+                selected_indicator=selected_indicator, 
+                num_signals=num_signals, 
+                signal_column='EMA9_Signal', 
+                data=data2)
 
+            fig.add_scatter(x=strong_buy_dates['Date'], y=strong_buy_dates['EMA_9'], mode='markers', marker=dict(symbol='triangle-up', size=10, color='green'), name='Strong buy')  
             fig.add_scatter(x=strong_sell_dates['Date'], y=strong_sell_dates['EMA_9'], mode='markers', marker=dict(symbol='triangle-down', size=10, color='red'), name='Strong sell')
-
+        fig.update_xaxes(
+        rangeslider_visible=True,
+        rangeselector=dict(
+            buttons=list([
+                dict(count=1, label="1m", step="month", stepmode="backward"),
+                dict(count=6, label="6m", step="month", stepmode="backward"),
+                dict(count=1, label="YTD", step="year", stepmode="todate"),
+                dict(count=1, label="1y", step="year", stepmode="backward"),
+                dict(step="all")
+            ])
+        )
+    )
         st.plotly_chart(fig)
     
     elif selected_indicator == 'EMA 55':
         # Plot close price and EMA 9
-        fig = px.line(data2, x='Date', y=['Close_price', 'EMA_55'], title='Close Price vs EMA 9',
+        fig = px.line(data2, x='Date', y=['Close_price', 'EMA_55'], title='Close Price vs EMA 55',
                     labels={'Date': 'Date', 'value': 'Price', 'variable': 'Type'})
         fig.update_traces(selector=dict(type='scatter'))
-        ema55_signal = 'EMA55_Signal'
         # Plot ‘strong buy’ signals
         if num_signals != 'None':
-            if num_signals == 'All':
-                strong_buy_dates = data2[data2[ema55_signal] == 3.0]
-            elif num_signals == 'Last 5 Days':
-                last5 = data2.tail(5)
-                strong_buy_dates = last5[last5[ema55_signal] == 3.0]
-            elif num_signals == 'Last 15 Days':
-                last15 = data2.tail(15)
-                strong_buy_dates = last15[last15[ema55_signal] == 3.0]
-            elif num_signals == 'Last 20 Days':
-                last20 = data2.tail(20)
-                strong_buy_dates = last20[last20[ema55_signal] == 3.0]
+            strong_buy_dates, strong_sell_dates, strong_hold_dates = signals_to_plot(
+                selected_indicator=selected_indicator, 
+                num_signals=num_signals, 
+                signal_column='EMA55_Signal', 
+                data=data2)
 
             fig.add_scatter(x=strong_buy_dates['Date'], y=strong_buy_dates['EMA_55'], mode='markers', marker=dict(symbol='triangle-up', size=10, color='green'), name='Strong buy')
-
-        # Plot ‘strong sell’ signals
-        if num_signals != 'None':
-            if num_signals == 'All':
-                strong_sell_dates = data2[data2[ema55_signal] == -3.0]
-            elif num_signals == 'Last 5 Days':
-                last5 = data2.tail(5)
-                strong_sell_dates = last5[last5[ema55_signal] == -3.0]
-            elif num_signals == 'Last 15 Days':
-                last15 = data2.tail(15)
-                strong_sell_dates = last15[last15[ema55_signal] == -3.0]
-            elif num_signals == 'Last 20 Days':
-                last20 = data2.tail(20)
-                strong_sell_dates = last20[last20[ema55_signal] == -3.0]
-
             fig.add_scatter(x=strong_sell_dates['Date'], y=strong_sell_dates['EMA_55'], mode='markers', marker=dict(symbol='triangle-down', size=10, color='red'), name='Strong sell')
-
+        fig.update_xaxes(
+            rangeslider_visible=True,
+            rangeselector=dict(
+                buttons=list([
+                    dict(count=1, label="1m", step="month", stepmode="backward"),
+                    dict(count=6, label="6m", step="month", stepmode="backward"),
+                    dict(count=1, label="YTD", step="year", stepmode="todate"),
+                    dict(count=1, label="1y", step="year", stepmode="backward"),
+                    dict(step="all")
+                ])
+            )
+        )
         st.plotly_chart(fig)
 
     elif selected_indicator == 'MACD':
         # Set up the figure and subplots
-        macd_signal = 'MACD_Signals'
-
         fig = make_subplots(rows=2, cols=1)
         # fig = go.Figure()
         # Add subplot for Close Price and Signals
         fig.add_trace(go.Scatter(x=data2['Date'], y=data2['Close_price'], mode='lines', name='Close Price'),
                         row=1, col=1)
         if num_signals != 'None':
-            if num_signals == 'All':
-                strong_buy_dates = data2[data2[macd_signal] == 3.0]
-                strong_sell_dates = data2[data2[macd_signal] == -3.0]
-                strong_hold_dates = data2[data2[macd_signal] == 0]
-            elif num_signals == 'Last 5 Days':
-                last5 = data2.tail(5)
-                strong_buy_dates = last5[last5[macd_signal] == 3.0]
-                strong_sell_dates = last5[last5[macd_signal] == -3.0]
-                strong_hold_dates = last5[last5[macd_signal] == 0]
-            elif num_signals == 'Last 15 Days':
-                last15 = data2.tail(15)
-                strong_buy_dates = last15[last15[macd_signal] == 3.0]
-                strong_sell_dates = last15[last15[macd_signal] == -3.0]
-                strong_hold_dates = last15[last15[macd_signal] == 0]
-            elif num_signals == 'Last 20 Days':
-                last20 = data2.tail(20)
-                strong_buy_dates = last20[last20[macd_signal] == 3.0]
-                strong_sell_dates = last20[last20[macd_signal] == -3.0]
-                strong_hold_dates = last20[last20[macd_signal] == 0]
-
+            strong_buy_dates, strong_sell_dates, strong_hold_dates = signals_to_plot(
+                selected_indicator=selected_indicator, 
+                num_signals=num_signals, 
+                signal_column='MACD_Signals', 
+                data=data2)
             fig.add_trace(go.Scatter(x=strong_buy_dates['Date'], y=strong_buy_dates['Close_price'], mode='markers', marker=dict(symbol='triangle-up', size=10, color='green'), name='Strong Buy'), row=1, col=1)
             fig.add_trace(go.Scatter(x=strong_sell_dates['Date'], y=strong_sell_dates['Close_price'], mode='markers', marker=dict(symbol='triangle-down', size=10, color='red'), name='Strong Sell'), row=1, col=1)
-            fig.add_trace(go.Scatter(x=strong_hold_dates['Date'], y=strong_hold_dates['Close_price'], mode='markers', marker=dict(symbol='circle', size=10, color='orange'), name='Strong Sell'), row=1, col=1)
+            fig.add_trace(go.Scatter(x=strong_hold_dates['Date'], y=strong_hold_dates['Close_price'], mode='markers', marker=dict(symbol='circle', size=10, color='orange'), name='Hold'), row=1, col=1)
 
         # Add subplot for MACD
         # fig2 = go.Figure()
@@ -156,6 +183,18 @@ with market_analysis:
         #                 yaxis=dict(title='Close Price', side='left', showgrid=False),
         #                 yaxis2=dict(title='MACD', side='right', overlaying='y', showgrid=False))
         fig.update_layout(title='MACD Analysis')
+        fig.update_xaxes(
+            rangeslider_visible=False,
+            rangeselector=dict(
+                buttons=list([
+                    dict(count=1, label="1m", step="month", stepmode="backward"),
+                    dict(count=6, label="6m", step="month", stepmode="backward"),
+                    dict(count=1, label="YTD", step="year", stepmode="todate"),
+                    dict(count=1, label="1y", step="year", stepmode="backward"),
+                    dict(step="all")
+                ])
+            )
+        )
         st.plotly_chart(fig, use_container_width=True)
         # st.plotly_chart(fig2, use_container_width=True)
 
@@ -167,45 +206,317 @@ with market_analysis:
         fig.add_trace(go.Scatter(x=data2['Date'], y=data2['RSI'], mode='lines', name='RSI'))
 
         # Add overbought and oversold lines
-        overbought_strong = 70
-        oversold_strong = 30
+        overbought_strong = 79
+        oversold_strong = 22
         fig.add_shape(type="line", x0=data2['Date'].min(), y0=overbought_strong, x1=data2['Date'].max(), y1=overbought_strong, line=dict(color="red", width=1, dash="dash"), name="Overbought")
         fig.add_shape(type="line", x0=data2['Date'].min(), y0=oversold_strong, x1=data2['Date'].max(), y1=oversold_strong, line=dict(color="green", width=1, dash="dash"), name="Oversold")
 
-        rsi_signal = 'RSI_Signals'
         if num_signals != 'None':
-            if num_signals == 'All':
-                strong_buy_dates = data2[data2[rsi_signal] >= 1.0]
-                strong_sell_dates = data2[data2[rsi_signal] <= -1.0]
-                strong_hold_dates = data2[data2[rsi_signal] == 0]
-            elif num_signals == 'Last 5 Days':
-                last5 = data2.tail(5)
-                strong_buy_dates = last5[last5[rsi_signal] >= 1.0]
-                strong_sell_dates = last5[last5[rsi_signal] <= -1.0]
-                strong_hold_dates = last5[last5[rsi_signal] == 0]
-            elif num_signals == 'Last 15 Days':
-                last15 = data2.tail(15)
-                strong_buy_dates = last15[last15[rsi_signal] >= 1.0]
-                strong_sell_dates = last15[last15[rsi_signal] <= -1.0]
-                strong_hold_dates = last15[last15[rsi_signal] == 0]
-            elif num_signals == 'Last 20 Days':
-                last20 = data2.tail(20)
-                strong_buy_dates = last20[last20[rsi_signal] >= 1.0]
-                strong_sell_dates = last20[last20[rsi_signal] <= -1.0]
-                strong_hold_dates = last20[last20[rsi_signal] == 0]
-
+            strong_buy_dates, strong_sell_dates, strong_hold_dates = signals_to_plot(
+                selected_indicator=selected_indicator,
+                num_signals=num_signals, 
+                signal_column='RSI_Signals', 
+                data=data2)
             fig.add_trace(go.Scatter(x=strong_buy_dates['Date'], y=strong_buy_dates['RSI'], mode='markers', marker=dict(symbol='triangle-up', size=10, color='green'), name='Strong Buy'))
             fig.add_trace(go.Scatter(x=strong_sell_dates['Date'], y=strong_sell_dates['RSI'], mode='markers', marker=dict(symbol='triangle-down', size=10, color='red'), name='Strong Sell'))
-            fig.add_trace(go.Scatter(x=strong_hold_dates['Date'], y=strong_hold_dates['RSI'], mode='markers', marker=dict(symbol='circle', size=10, color='orange'), name='Strong Sell'))
+            # fig.add_trace(go.Scatter(x=strong_hold_dates['Date'], y=strong_hold_dates['RSI'], mode='markers', marker=dict(symbol='circle', size=10, color='orange'), name='Hold'))
         
         fig.update_layout(title='RSI Analysis')
+        fig.update_xaxes(
+            rangeslider_visible=True,
+            rangeselector=dict(
+                buttons=list([
+                    dict(count=1, label="1m", step="month", stepmode="backward"),
+                    dict(count=6, label="6m", step="month", stepmode="backward"),
+                    dict(count=1, label="YTD", step="year", stepmode="todate"),
+                    dict(count=1, label="1y", step="year", stepmode="backward"),
+                    dict(step="all")
+                ])
+            )
+        )
         st.plotly_chart(fig)
     # st.write(data2)
 
 with news_analysis:
     st.header("News Analysis")
-    st.write('data to be added.')
+    st.write('Date, event impact, close price, headline or Bold_KW.')
+    # Load data
+    data_file_path = r"Events_SameDay.csv"  # Update this with your file path
+    events = pd.read_csv(data_file_path)
+
+    # Convert 'Date' column to datetime format
+    events['Date'] = pd.to_datetime(events['Date'])
+    print(events.shape, events.columns)
+
+    default_start_date = events['Date'].min()  # Set default start date for the start date picker
+    date_limit = pd.to_datetime('2021-08-12')   # Set date limit for end date picker
+    
+    # Add a new column for positive values of column A
+    events['Positive_Impacts'] = events[events['Events_Impact'] > 0]['Events_Impact']
+
+    # Add a new column for negative values of column A
+    events['Negative_Impacts'] = events[events['Events_Impact'] < 0]['Events_Impact']
 
+    # Fill NaN values in the new columns with 0
+    events['Positive_Impacts'].fillna("", inplace=True)
+    events['Negative_Impacts'].fillna("", inplace=True)
+    # Create the line trace for stock prices
+    line_stock = go.Scatter(x=events['Date'], y=events['Price'], mode='lines', name='OGDCL Close Price',
+        line=dict(dash='solid', color='royalblue', width=2),
+        # text=events['Cleaned_Headline'],
+        # hovertext=events['FeatureSentiment'],
+        customdata=events['Feature'],
+        hovertemplate='%{x}<br>Close: %{y}<br>Feature: %{customdata}<br>',
+        # hoverlabel=dict(font=dict(color=events
+        # ['FeatureSentiment'].apply(lambda x: 'red' if x == 'Negative' else 'blue' if x == 'Neutral' else 'green'))),  # Customize the line style, color, and width
+    )
+    # Create dummy traces for the legend
+    dummy_positive = go.Scatter(x=[None], y=[None], mode='lines', name='Positive Impacts',
+        marker=dict(color='black', size=15), showlegend=True,
+        # visible='legendonly'
+        )
+    dummy_negative = go.Scatter(x=[None], y=[None], mode='lines', name='Negative Impacts',
+        marker=dict(color='red', size=15), showlegend=True,
+        # visible='legendonly'
+    )
+
+    fontsize = 12
+    annotations = []
+    # Create annotations for the Positive points
+    for index, row in events.iterrows():
+        annotation1 = dict(x=row['Date'], y=row['Price'], text = row['Positive_Impacts'],
+                showarrow=True, arrowhead=0, # arrowcolor='black',
+                ax=10, ay=-20,   # Dynamic offset
+                font=dict(size=fontsize, color='green'),
+            )
+        annotations.append(annotation1)
+
+    # Create annotations for the Negative points
+    for index, row in events.iterrows():
+        annotation2 = dict(x=row['Date'], y=row['Price'], text = row['Negative_Impacts'],
+                showarrow=True, arrowhead=0, arrowcolor='blue',
+                ax=10, ay=-20,   # Dynamic offset
+                font=dict(size=fontsize, color='red'),
+            )
+        annotations.append(annotation2)
+
+    # Create the layout
+    title = 'OGDCL Close Price w.r.t. News Impacts'
+    layout = go.Layout(
+        title=title,
+        xaxis=dict(
+            title='Date',
+            tickformat='%b %d, %Y',
+            # gridcolor='lightgray',
+            range=[start_date, end_date],
+            # tickvals=list(range(dateA, dateB, 3)),
+        ),
+        yaxis=dict(
+            title='OGDCL Close Price',
+            # gridcolor='lightgray',
+            range=[90, 120],
+            tickvals=list(range(90, 120, 5)),
+        ),
+        # plot_bgcolor='rgba(245, 245, 245, 0.8)',
+        # paper_bgcolor='white',
+        # hoverlabel=dict(
+        #     bgcolor='white',
+        #     font=dict(color='black'),
+        # ),
+        annotations=annotations,
+    )
+
+    # Add all traces to the figure
+    figure = go.Figure(data=[line_stock, dummy_negative, dummy_positive], layout=layout)
+
+    figure.update_layout(
+        title={
+            'text': title,
+            'x': 0.5,
+            'y': 0.95,
+            'xanchor': 'center',
+            'yanchor': 'top',
+            'font': dict(size=12),
+        },
+        hovermode='closest',
+        margin=dict(l=40, r=40, t=80, b=40),
+    )
+    figure.update_xaxes(
+        rangeslider_visible=True,
+        rangeselector=dict(
+            buttons=list([
+                dict(count=1, label="1m", step="month", stepmode="backward"),
+                dict(count=6, label="6m", step="month", stepmode="backward"),
+                dict(count=1, label="YTD", step="year", stepmode="todate"),
+                dict(count=1, label="1y", step="year", stepmode="backward"),
+                dict(step="all")
+            ])
+        )
+    )
+    st.plotly_chart(figure)
+
+    # news = events[events['Date'].between(start_date, end_date, inclusive='both')]
+    news = events[(events['Date'] >= start_date) & (events['Date'] <= end_date)]
+    print(news[news['Date']==start_date])
+    news = news[['Date', 'Raw_Headline', 'Bold_KW', 'Feature', 'Raw_News', 'Sources', 'Urls']]
+    cols = ['Raw_Headline', 'Bold_KW', 'Feature', 'Raw_News', 'Sources', 'Urls']
+    for col in cols:
+        news[col] = news[col].apply(convert_str_to_list)
+    # Extract only the date part
+    news['Date'] = news['Date'].dt.date
+    st.subheader("News Events")
+    # st.dataframe(news, hide_index=True)
+    # st.table(news)
+    dates = list(news['Date'].unique())
+    dates = np.sort(dates)
+    print(dates, len(dates))
+    num_dates = len(dates)
+    items_per_page = min(num_dates, 5)
+    for i, date in paginator("Select Page Number", dates,  items_per_page=items_per_page, on_sidebar=False):
+        st.write(f'<span style="font-size: large;"><b>Date:</b> <u>{date}</u></span>', unsafe_allow_html=True)
+        
+
+        filtered_news = news[news['Date'] == date]
+        print(filtered_news.shape)
+        features = filtered_news['Feature'].sum()
+        headlines = filtered_news['Raw_Headline'].sum()
+        news_list = filtered_news['Raw_News'].sum() 
+        sources = filtered_news['Sources'].sum()
+        urls = filtered_news['Urls'].sum()
+
+        main_container = st.container(height = 250, border=True)
+        # main_container.write(f"Date: {date}")
+        
+        col1, col2 = main_container.columns([0.7, 0.3], gap='medium')
+
+        # Merge lists of headlines into a single list
+        for index, headline in enumerate(headlines):
+            col1.page_link(urls[index], label=f"**:blue[{headline}]**")
+            # col1.write(f"<span style='font-size: large;'><b><u>{headline}</u></b></span>", unsafe_allow_html=True)
+            col1.write(f"<span style='font-size: small;'>By {sources[index]}</span><br>", unsafe_allow_html=True)
+            with col1:              
+                with st.expander("Show Full Article"):
+                    st.write(news_list[index])
+
+        with col2:
+            # st.divider()
+            with st.expander("Oil Sector Features"):
+                st.write(set(features))
+
+        
+            
 with trade_recs:
     st.header("Trading Recommendations")
-    st.write('recommendations to be added.')
+    line_color = 'blue'
+     # Create the line trace for stock prices
+    # Dropdown for selecting the indicator
+    selected_indicator = st.selectbox("Indicator to Show", ['EMA 9', 'EMA 55', 'MACD', 'RSI'])
+    if selected_indicator == 'EMA 9':
+        column = 'EMA9_Signal'
+        hover_text = '%{x}<br>Close: %{y}<br> EMA9_Signal: %{customdata}<br>'
+    elif selected_indicator == 'EMA 55':
+        column = 'EMA55_Signal'
+        hover_text = '%{x}<br>Close: %{y}<br> EMA55_Signal: %{customdata}<br>'
+    elif selected_indicator == 'MACD':
+        column = 'MACD_Signals'
+        hover_text = '%{x}<br>Close: %{y}<br> MACD_Signal: %{customdata}<br>'
+    elif selected_indicator == 'RSI':
+        column = 'RSI_Signals'
+        hover_text = '%{x}<br>Close: %{y}<br> RSI_Signal: %{customdata}<br>'
+    line_stock = go.Scatter(x=events['Date'], y=events['Price'], mode='lines', name='OGDCL Close Price',
+        line=dict(dash='solid',color=line_color, width=2),   # color='royalblue'
+        text=[column],
+        # hovertext=events['FeatureSentiment'],
+        customdata=events[column],
+        hovertemplate=hover_text,
+        # hoverlabel=dict(font=dict(color=events
+        # ['FeatureSentiment'].apply(lambda x: 'red' if x == 'Negative' else 'blue' if x == 'Neutral' else 'green'))),  # Customize the line style, color, and width
+    )
+    # Create dummy traces for the legend
+    dummy_positive = go.Scatter(x=[None], y=[None], mode='lines', name='Positive Impacts',
+        marker=dict(color='black', size=15), showlegend=True,
+        # visible='legendonly'
+        )
+    dummy_negative = go.Scatter(x=[None], y=[None], mode='lines', name='Negative Impacts',
+        marker=dict(color='red', size=15), showlegend=True,
+        # visible='legendonly'
+    )
+
+    fontsize = 12
+    annotations = []
+    # Create annotations for the Positive points
+    for index, row in events.iterrows():
+        annotation1 = dict(x=row['Date'], y=row['Price'], text = row['Positive_Impacts'],
+                showarrow=True, arrowhead=0, # arrowcolor='black',
+                ax=10, ay=-20,   # Dynamic offset
+                font=dict(size=fontsize, color='green'),
+            )
+        annotations.append(annotation1)
+
+    # Create annotations for the Negative points
+    for index, row in events.iterrows():
+        annotation2 = dict(x=row['Date'], y=row['Price'], text = row['Negative_Impacts'],
+                showarrow=True, arrowhead=0, arrowcolor=line_color,
+                ax=10, ay=-20,   # Dynamic offset
+                font=dict(size=fontsize, color='red'),
+            )
+        annotations.append(annotation2)
+
+    # Create the layout
+    title = 'OGDCL Close Price w.r.t. News Impacts'
+    layout = go.Layout(
+        title=title,
+        xaxis=dict(
+            title='Date',
+            tickformat='%b %d, %Y',
+            # gridcolor='lightgray',
+            range=[start_date, end_date],
+            # tickvals=list(range(dateA, dateB, 3)),
+        ),
+        yaxis=dict(
+            title='OGDCL Close Price',
+            # gridcolor='lightgray',
+            range=[90, 120],
+            tickvals=list(range(90, 120, 5)),
+        ),
+        # plot_bgcolor='rgba(245, 245, 245, 0.8)',
+        # paper_bgcolor='white',
+        # hoverlabel=dict(
+        #     bgcolor='white',
+        #     font=dict(color='black'),
+        # ),
+        annotations=annotations,
+    )
+
+    # Add all traces to the figure
+    figure = go.Figure(data=[line_stock, dummy_negative, dummy_positive], layout=layout)
+    # figure.update_traces(mode="markers+lines")
+    figure.update_layout(
+        title={
+            'text': title,
+            'x': 0.5,
+            'y': 0.95,
+            'xanchor': 'center',
+            'yanchor': 'top',
+            'font': dict(size=12),
+        },
+        hovermode='closest',
+        margin=dict(l=40, r=40, t=80, b=40),
+    )
+    figure.update_xaxes(
+        rangeslider_visible=True,
+        rangeselector=dict(
+            buttons=list([
+                dict(count=1, label="1m", step="month", stepmode="backward"),
+                dict(count=6, label="6m", step="month", stepmode="backward"),
+                dict(count=1, label="YTD", step="year", stepmode="todate"),
+                dict(count=1, label="1y", step="year", stepmode="backward"),
+                dict(step="all")
+            ])
+        )
+    )
+    st.plotly_chart(figure)
+    
+
+with chat:
+    st.balloons()