Update app.py

app.py

@@ -297,7 +297,8 @@ with market_analysis:
         fig = go.Figure()
 
         # Add RSI line
-        fig.add_trace(go.Scatter(x=data2['Date'], y=data2['RSI'], mode='lines', name='RSI'))
+        fig.add_trace(go.Scatter(x=data2['Date'], y=data2['RSI'], mode='lines', name='RSI'),
+                     line=dict(dash='solid', color=cs.rsi_color, width=2))
 
         # Add overbought and oversold lines
         overbought_strong = 79
@@ -448,56 +449,69 @@ with news_analysis:
     figure.update_yaxes(fixedrange=False)
     st.plotly_chart(figure)
 
+    # Add subheader for news section
+    st.subheader("News Events")
+    """In this section, news events for each date in the data will be displayed along the features for that date"""
+    # Filter data for news events
     news = events[events['Date'].between(start_date, end_date, inclusive='both')]
-    news = news[['Date', 'Raw_Headline', 'Bold_KW', 'Feature', 'Raw_News', 'Sources', 'Urls']]
+    news = news[['Date', 'Raw_Headline', 'Bold_KW', 'SetFeature', 'Raw_News', 'Sources', 'Urls']]
    
-    
-    # Extract only the date part
+    # Extract only the date from the datetime
     news['Date'] = news['Date'].dt.date
     
     # Sort DataFrame based on the 'Date' column in descending order
     news = news.sort_values(by='Date', ascending=False)
    
-
     # Reset index to reflect the new order
     news.reset_index(drop=True, inplace=True)
-   
-    st.subheader("News Events")
-    
+
+    # Get all the unique dates to iterate over
     dates = list(news['Date'].unique())
-   
+    
+    # Sort the date list
     dates = np.sort(dates)
+    
     # Reverse the array to have the latest date at index 0
     dates = dates[::-1]
-    print(dates, len(dates))
+
+    # Decide number of items to display per page
     num_dates = len(dates)
     items_per_page = min(num_dates, 5)
+
+    # iterate over the paginator
     for i, date in paginator("Select Page Number", dates,  items_per_page=items_per_page, on_sidebar=False, ukey='news_pages'):
+        # Display the date
         st.write(f'<span style="font-size: large;"><b>Date:</b> <u>{date}</u></span>', unsafe_allow_html=True)
+
+        # Filter data for each date in the loop
         filtered_news = news[news['Date'] == date]
-        print(filtered_news.shape)
+
+        # Extract the details required
         features = filtered_news['SetFeature'].sum()
         headlines = filtered_news['Raw_Headline'].sum()
         news_list = filtered_news['Raw_News'].sum() 
         sources = filtered_news['Sources'].sum()
         urls = filtered_news['Urls'].sum()
 
+        # Create a container to display news for each date
         main_container = st.container(height = 250, border=True)
-        # main_container.write(f"Date: {date}")
-        
+
+        # Create columns to display news on one side and features on the other
         col1, col2 = main_container.columns([0.7, 0.3], gap='medium')
 
-        # Merge lists of headlines into a single list
+        # Display each headline in the extracted headlines in the container
         for index, headline in enumerate(headlines):
+            # Link news article's Url to the headline to redirect to the source article webpage on click
             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)
+            # Display news source in the container
             col1.write(f"<span style='font-size: small;'>By {sources[index]}</span><br>", unsafe_allow_html=True)
+            # Display news content on click
             with col1:              
                 with st.expander("Show Full Article"):
                     st.write(news_list[index])
 
+        # Display features on click
         with col2:
-            # st.divider()
             with st.expander("Oil Sector Features"):
                 st.write(set(features))
                 
@@ -517,26 +531,41 @@ with final_recs:
     # The results shown here are based on our model's inference on this test data, which is available in the Colab Notebook provided along GitHub submission. 
     # """)
 
-    
+    # Load generated recommendations data
     recs = pd.read_csv("test_recom.csv")
+
+    # Convert date column to datetime values
     recs['Date'] = pd.to_datetime(recs['Date'])
+    
+    # Get only the date from datetime
     recs['Date'] = recs['Date'].dt.date
+    
+    # Get all the unique dates to add to the selectbox and to iterate over
     rec_dates = np.sort(list(recs['Date'].unique()))
+    
+    # Create the date select box
     pred_date = st.selectbox("Pick the Test Date", rec_dates)
-    fp = {}
-    for index, d in enumerate(rec_dates[:-1]):
-        fr = recs[recs['Date'] == rec_dates[index+1]]
-        fr.reset_index(inplace=True, drop=True)
-        following_price = fr['Price'][0]
-        fp[d] = following_price
-
-    fp[rec_dates[-1]] = 'Not Available'
     
+    # Store the close price value of the following day for each date in a dictionary to call later
+    fp = {}   # initialize an empty dictionary
+    for index, d in enumerate(rec_dates[:-1]):    # iterate over the unique dates
+        fr = recs[recs['Date'] == rec_dates[index+1]]   # get data of the following day
+        fr.reset_index(inplace=True, drop=True)    # reset index
+        following_price = fr['Price'][0    # get close price
+        fp[d] = following_price    # append dictionary
+
+    # As no following day data is available for the latest date in the list, assign it 'Not Available'
+    fp[rec_dates[-1]] = 'Not Available'
+
+    # Add radio buttons to select role
     role = st.radio(
     "Show recommendation summary as:",
     ["Active Trader", "Equity Analyst"], horizontal=True)
-    
+
+    # filter data based on the date selected by the user
     filter_recs = recs[recs['Date'] == pred_date]
+
+    # filter required data based on the role selected by the user
     if role == 'Active Trader':
         trade_recs = filter_recs[['Date', 'Recommendations_Active_Trader', 'Price']]
         # Convert back to Dictionaries from strings
@@ -547,25 +576,35 @@ with final_recs:
         # Convert back to Dictionaries from strings
         trade_recs['Recommendations_Equity_Analyst'] = trade_recs['Recommendations_Equity_Analyst'].apply(convert_str_to_list)
         trade_recs.rename(columns={'Recommendations_Equity_Analyst': 'Recommendations'}, inplace=True)
-    
-    trade_recs.reset_index(inplace=True, drop=True)
+
+    # reset index after filteration
+    trade_recs.reset_index(inplace=True, drop=True)  
+
+    # create container to display generated recommendations
     genrec_container = st.container(border=False)
+
+    # create columns to display date, current close price, and following day close price side-by-side
     rec_col1, rec_col2, rec_col3 = genrec_container.columns(3, gap='medium')
-    current_price = trade_recs['Price'][0]
-    
+
+    # Show selected date
     rec_col1.write(f'<span style="font-size: large;"><b>Current Date:</b> <u>{pred_date}</u></span>', unsafe_allow_html=True)
+    # Show selected date close price
+    current_price = trade_recs['Price'][0]
     rec_col2.write(f'<span style="font-size: large;"><b>Current Close Price:</b> {current_price}</span>', unsafe_allow_html=True)
+    # Show following day close price
     rec_col3.write(f'<span style="font-size: large;"><b>Following Close Price:</b> {fp[pred_date]}</span>', unsafe_allow_html=True)
-    
+
+    # Show generated recommendations
     genrec_container.subheader("Generated Recommendation")
     genrec_container.write(trade_recs['Recommendations'][0])
-    
+
+    # Show Market and News Analysis w.r.t. OGDCL Close Price chart
     # 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),
         # text=events['Cleaned_Headline'],
         # hovertext=events['FeatureSentiment'],
-        customdata=events['Feature'],
+        customdata=events['SetFeature'],
         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
@@ -590,12 +629,16 @@ with final_recs:
 
     # Add all traces to the figure
     figure = go.Figure(data=[line_stock], layout=layout)
+    # Add positive impact
     figure.add_scatter(x=plot_sub_pos['Date'], y=plot_sub_pos['Price'], mode='markers', 
         marker=dict(symbol='triangle-up', size=10, color=cs.pos_impacts_color), name='Positive Impact', 
         customdata=plot_sub_pos['SetFeature'], hovertemplate='%{x}<br>Close: %{y}<br>Feature: %{customdata}<br>')  
+    # Add negative impact
     figure.add_scatter(x=plot_sub_neg['Date'], y=plot_sub_neg['Price'], mode='markers', 
         marker=dict(symbol='triangle-down', size=10, color=cs.neg_impacts_color), name='Negative Impact', 
         customdata=plot_sub_neg['SetFeature'], hovertemplate='%{x}<br>Close: %{y}<br>Feature: %{customdata}<br>',)
+
+    # Update layout
     figure.update_layout(
         title={
             'text': title,
@@ -608,8 +651,8 @@ with final_recs:
         hovermode='closest',
         margin=dict(l=40, r=40, t=80, b=40),
         modebar_add="togglespikelines",
-        # scrollZoom=True,
     )
+    # Add date range selection buttons to chart
     figure.update_xaxes(
         rangeslider_visible=True,
         rangeselector=dict(