Update app.py

app.py

@@ -86,17 +86,18 @@ date_limit = pd.to_datetime('2021-08-12')   # Set date limit for end date picker
 
 
 # Create Tabs
-market_analysis, news_analysis, trade_recs, final_recs, chat = st.tabs(["Market Analysis", "News Analysis", "Trading Recommendations", "GenAI Recommendations","AI Stock Advisor"])
+market_analysis, news_analysis, final_recs, chat = st.tabs(["Market Analysis", "News Analysis", "GenAI Recommendations","AI Stock Advisor"])
 
 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)
+    date_container = st.container(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("Current Date", value=date_limit, min_value=data['Date'].min(), max_value=date_limit)
+    # start_date = col1.date_input('Start Date', value=default_start_date, min_value=data['Date'].min(), max_value=date_limit)
+    end_date = col1.date_input("Current Date", value=date_limit, min_value=data['Date'].min(), max_value=date_limit)
+    start_date = data['Date'].min()
     start_date = pd.to_datetime(start_date)
     end_date = pd.to_datetime(end_date)
     data2 = data[data['Date'].between(start_date, end_date)]
@@ -457,121 +458,154 @@ with news_analysis:
                 with st.expander("Oil Sector Features"):
                     st.write(set(features))
             
-with trade_recs:
-    st.header("Trading Recommendations")
-
-     # 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=cs.close_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=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)
+# with trade_recs:
+#     st.header("Trading Recommendations")
+
+#      # 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=cs.close_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=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)
+#     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, ukey='trade_pages'):
+#         st.write(f'<span style="font-size: large;"><b>Date:</b> <u>{date}</u></span>', unsafe_allow_html=True)
+        
+#         filtered_trades = trade_recs[trade_recs['Date'] == date]
+#         filtered_trades.reset_index(inplace=True)
+#         rec = filtered_trades['Trading_Recommendations'][0]
+#         print(type(rec), rec)
+#         trade_container = st.container(border=False)
+#         trade_container.write(rec)
+
+#     # st.write(trade_recs)
+    
 
-    # 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,
-    )
+with final_recs:
+    st.header("GenAI Recommendations")
+    st.write("This module provides trading recommendation for the following day based on the selected current date.")
 
-    # 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)
+    # print(events.columns)
     trade_recs = events[['Date', 'Trading_Recommendations']]
     # Extract date component from the datetime column
     trade_recs['Date'] = trade_recs['Date'].dt.date
@@ -582,22 +616,14 @@ with trade_recs:
     # Reverse the array to have the latest date at index 0
     dates = dates[::-1]
     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, ukey='trade_pages'):
-        st.write(f'<span style="font-size: large;"><b>Date:</b> <u>{date}</u></span>', unsafe_allow_html=True)
-        
-        filtered_trades = trade_recs[trade_recs['Date'] == date]
-        filtered_trades.reset_index(inplace=True)
-        rec = filtered_trades['Trading_Recommendations'][0]
-        print(type(rec), rec)
-        trade_container = st.container(border=False)
-        trade_container.write(rec)
-
-    # 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.")
+    st.subheader("GenAI Recommendations")
+    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)
     
      # Create the line trace for stock prices
     line_stock = go.Scatter(x=events['Date'], y=events['Price'], mode='lines', name='OGDCL Close Price',
@@ -694,28 +720,7 @@ with final_recs:
         )
     )
     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)
-    st.subheader("GenAI Recommendations")
-    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: