Update app.py

app.py

@@ -7,6 +7,8 @@ import numpy as np
 import ast
 from pagination import paginator
 import style as cs
+import random
+import time
 
 
 
@@ -83,16 +85,17 @@ 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"])
+market_analysis, news_analysis, trade_recs, final_recs, chat = st.tabs(["Market Analysis", "News Analysis", "Trading Recommendations", "Final Recommendations","Gup Shup"])
 
 with market_analysis:
     st.header("Market Analysis")
+    st.write("This module provides market analysis for the following day based on the selected current date.")
     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)
+    end_date = col2.date_input("Current 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)]
@@ -245,6 +248,7 @@ with market_analysis:
 
 with news_analysis:
     st.header("News Analysis")
+    st.write("This module provides news impact for the following day based on the selected current date.")
     
     # Load data
     data_file_path = r"Events_SameDay.csv"  # Update this with your file path
@@ -364,21 +368,29 @@ with news_analysis:
 
     # news = events[events['Date'].between(start_date, end_date, inclusive='both')]
    
-    news = events[['Date', 'Raw_Headline', 'Bold_KW', 'Feature', 'Raw_News', 'Sources', 'Urls']]
+    pos_news = plot_sub_pos[['Date', 'Raw_Headline', 'Bold_KW', 'Feature', 'Raw_News', 'Sources', 'Urls']]
+    neg_news = plot_sub_neg[['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)
+        pos_news[col] = pos_news[col].apply(convert_str_to_list)
+        neg_news[col] = neg_news[col].apply(convert_str_to_list)
     # Extract only the date part
-    news['Date'] = news['Date'].dt.date
+    neg_news['Date'] = neg_news['Date'].dt.date
+    pos_news['Date'] = pos_news['Date'].dt.date
     # Sort DataFrame based on the 'Date' column in descending order
-    news = news.sort_values(by='Date', ascending=False)
+    neg_news = neg_news.sort_values(by='Date', ascending=False)
+    pos_news = pos_news.sort_values(by='Date', ascending=False)
 
     # Reset index to reflect the new order
-    news.reset_index(drop=True, inplace=True)
+    pos_news.reset_index(drop=True, inplace=True)
+    neg_news.reset_index(drop=True, inplace=True)
     st.subheader("News Events")
     # st.dataframe(news, hide_index=True)
     # st.table(news)
-    dates = list(news['Date'].unique())
+    dates_neg = list(neg_news['Date'].unique())
+    dates_pos = list(pos_news['Date'].unique())
+    dates = dates_pos + dates_neg
     dates = np.sort(dates)
     # Reverse the array to have the latest date at index 0
     dates = dates[::-1]
@@ -388,33 +400,61 @@ with news_analysis:
     for i, date in paginator("Select Page Number", dates,  items_per_page=items_per_page, on_sidebar=False, ukey='news_pages'):
         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}")
+        if date in dates_pos:
+            filtered_news = pos_news[pos_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))
         
-        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))
+        if date in dates_neg:
+            filtered_news = neg_news[neg_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")
@@ -554,5 +594,162 @@ with trade_recs:
 
     # st.write(trade_recs)
     
+with final_recs:
+    st.header("GenAI Recommendations")
+    st.write("This module provides trading recommendation for the following day based on the selected current date.")
+    
+     # 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=cs.close_line_color, width=2),   # color='royalblue'
+        text=events['EMA9_Signal'],
+        hovertext=events['EMA55_Signal'],
+        meta = events["RSI_Signals"],
+        customdata=events['MACD_Signals'],
+        hovertemplate='%{x}<br>Close: %{y}<br> EMA9_Signal: %{text}<br>EMA55_Signal: %{hovertext}<br> RSI_Signal: %{meta}<br>MACD_Signal: %{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=cs.pos_impacts_color, size=15), showlegend=True,
+        # visible='legendonly'
+        )
+    dummy_negative = go.Scatter(x=[None], y=[None], mode='lines', name='Negative Impacts',
+        marker=dict(color=cs.neg_impacts_color, size=15), showlegend=True,
+        # visible='legendonly'
+    )
+
+    fontsize = 12
+    annotations = []
+    # Create annotations for the Positive points
+    for index, row in plot_sub_pos.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=cs.pos_impacts_color),
+            )
+        annotations.append(annotation1)
+
+    # Create annotations for the Negative points
+    for index, row in plot_sub_neg.iterrows():
+        annotation2 = dict(x=row['Date'], y=row['Price'], text = row['Negative_Impacts'],
+                showarrow=True, arrowhead=0, arrowcolor=cs.close_line_color,
+                ax=10, ay=-20,   # Dynamic offset
+                font=dict(size=fontsize, color=cs.neg_impacts_color),
+            )
+        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)
+    if 'Trading_Recommendations\r' in events.columns:
+        events.rename(columns={'Trading_Recommendations\r': 'Trading_Recommendations'}, inplace=True)
+    
+    print(events.columns)
+    trade_recs = events[['Date', 'Trading_Recommendations']]
+    # Extract date component from the datetime column
+    trade_recs['Date'] = trade_recs['Date'].dt.date
+    # Convert back to Dictionaries from strings
+    trade_recs['Trading_Recommendations'] = trade_recs['Trading_Recommendations'].apply(convert_str_to_list)
+    dates = list(trade_recs['Date'].unique())
+    dates = np.sort(dates)
+    # Reverse the array to have the latest date at index 0
+    dates = dates[::-1]
+    num_dates = len(dates)
+    genrec_container = st.container(border=False)
+    role = genrec_container.radio(
+    "Get your recommendation summary as:",
+    ["Trader", "Equity Analyst"], horizontal=True)
+
+    genrec = 'Recommendation for following day will go here.'
+    genrec_container.write(genrec)
 with chat:
-    st.balloons()
+    # Streamed response emulator
+    def response_generator():
+        response = random.choice(
+            [
+                "Hello there! How can I assist you today?",
+                "Hi, human! Is there anything I can help you with?",
+                "Do you need help?",
+            ]
+        )
+        for word in response.split():
+            yield word + " "
+            time.sleep(0.05)
+
+
+    st.title("Simple chat")
+
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # Display chat messages from history on app rerun
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # Accept user input
+    if prompt := st.chat_input("What is up?"):
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        # Display user message in chat message container
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        # Display assistant response in chat message container
+        with st.chat_message("assistant"):
+            response = st.write_stream(response_generator())
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": response})