Update app.py

app.py

@@ -10,7 +10,6 @@ from plotly.subplots import make_subplots
 # Configuration and Constants
 # ---------------------------
 
-# Define sector ETFs
 SECTORS = {
     'Technology': 'XLK',
     'Healthcare': 'XLV',
@@ -35,21 +34,26 @@ COLORS = [
 # Utility Functions
 # ---------------------------
 
+def get_appropriate_interval(start_date, end_date, market_status):
+    """
+    Determines the appropriate data interval based on date range and market status.
+    """
+    days_diff = (end_date - start_date).days
+    
+    if market_status and days_diff <= 7:  # Only use 1m data for up to 7 days when market is open
+        return '1m'
+    elif days_diff <= 60:
+        return '1h'
+    else:
+        return '1d'
+
 @st.cache_data(ttl=60*5)  # Cache data for 5 minutes
 def fetch_sector_performance(start_date, end_date, interval='1d'):
     """
-    Fetches the performance data for each sector ETF over a specified date range.
-    
-    Parameters:
-        start_date (datetime.date): Start date.
-        end_date (datetime.date): End date.
-        interval (str): Data interval (e.g., '1d', '1m').
-    
-    Returns:
-        list of dict: Sector performance data.
+    Fetches the performance data for each sector ETF with improved error handling.
     """
     sector_data = []
-    adjusted_end_date = end_date + timedelta(days=1)  # Include end_date
+    adjusted_end_date = end_date + timedelta(days=1)
     
     india_tz = pytz.timezone('Asia/Kolkata')
     start_datetime = datetime.combine(start_date, datetime.min.time()).astimezone(india_tz)
@@ -58,6 +62,7 @@ def fetch_sector_performance(start_date, end_date, interval='1d'):
     for sector, ticker in SECTORS.items():
         try:
             stock = yf.Ticker(ticker)
+            # Add error handling for download
             hist = stock.history(start=start_datetime, end=end_datetime, interval=interval)
             
             if not hist.empty and len(hist['Close']) >= 2:
@@ -66,24 +71,30 @@ def fetch_sector_performance(start_date, end_date, interval='1d'):
                 oldest_close = hist['Close'][0]
                 performance = ((latest_close - oldest_close) / oldest_close) * 100
                 color = '#66BB6A' if performance >= 0 else '#EF5350'
+                
+                # Store more detailed data for debugging
                 sector_data.append({
                     'Sector': sector,
                     'Performance': performance,
                     'Color': color,
                     'Ticker': ticker,
-                    'Historical Data': hist  # Include for debugging
+                    'Historical Data': hist,
+                    'Data Points': len(hist),
+                    'Start Price': oldest_close,
+                    'End Price': latest_close
                 })
             else:
-                # Handle cases with insufficient data
+                st.warning(f"Insufficient data for {sector} ({ticker})")
                 sector_data.append({
                     'Sector': sector,
                     'Performance': 0.0,
                     'Color': 'gray',
                     'Ticker': ticker,
-                    'Historical Data': hist  # Include for debugging
+                    'Historical Data': pd.DataFrame(),
+                    'Error': 'Insufficient data'
                 })
         except Exception as e:
-            # Handle exceptions for individual tickers
+            st.warning(f"Error fetching data for {sector} ({ticker}): {str(e)}")
             sector_data.append({
                 'Sector': sector,
                 'Performance': 0.0,
@@ -94,9 +105,25 @@ def fetch_sector_performance(start_date, end_date, interval='1d'):
             })
     
     # Sort data by performance descending
+    sector_data = [d for d in sector_data if d['Performance'] != 0.0]  # Filter out failed fetches
     sector_data.sort(key=lambda x: x['Performance'], reverse=True)
     return sector_data
 
+def is_market_open():
+    """
+    Checks if the NSE market is currently open based on Asia/Kolkata timezone.
+    
+    Returns:
+        bool: True if market is open, False otherwise.
+    """
+    india_tz = pytz.timezone('Asia/Kolkata')
+    now = datetime.now(india_tz)
+    # Define market hours: 9:15 AM to 3:30 PM IST
+    market_open = now.replace(hour=9, minute=15, second=0, microsecond=0)
+    market_close = now.replace(hour=15, minute=30, second=0, microsecond=0)
+    
+    return market_open <= now <= market_close
+
 def get_summary_stats(sector_data):
     """
     Calculate summary statistics from sector data.
@@ -128,21 +155,6 @@ def format_performance(performance):
     """
     return f"{performance:+.2f}%"
 
-def is_market_open():
-    """
-    Checks if the NSE market is currently open based on Asia/Kolkata timezone.
-    
-    Returns:
-        bool: True if market is open, False otherwise.
-    """
-    india_tz = pytz.timezone('Asia/Kolkata')
-    now = datetime.now(india_tz)
-    # Define market hours: 9:15 AM to 3:30 PM IST
-    market_open = now.replace(hour=9, minute=15, second=0, microsecond=0)
-    market_close = now.replace(hour=15, minute=30, second=0, microsecond=0)
-    
-    return market_open <= now <= market_close
-
 # ---------------------------
 # Streamlit UI
 # ---------------------------
@@ -151,17 +163,17 @@ def main():
     st.set_page_config(page_title="Market Sector Performance Dashboard", layout="wide")
     st.title("📈 Market Sector Performance Dashboard")
     
-    # Initialize session state for refresh counter and last updated time
+    # Initialize session state
     if 'refresh' not in st.session_state:
         st.session_state.refresh = 0
     if 'last_updated' not in st.session_state:
         st.session_state.last_updated = None
 
-    # Sidebar for date range selection and refresh button
+    # Sidebar controls
     with st.sidebar:
         st.header("Controls")
         
-        # Date Range Selection
+        # Date Range Selection with improved validation
         st.markdown("### Select Date Range")
         india_tz = pytz.timezone('Asia/Kolkata')
         now = datetime.now(india_tz)
@@ -171,76 +183,69 @@ def main():
         start_date = st.date_input("Start Date", default_start)
         end_date = st.date_input("End Date", default_end)
         
-        # Validate Date Range
         if start_date > end_date:
             st.error("Error: Start Date must be before End Date.")
+            st.stop()
+            
+        # Warn about long date ranges
+        date_diff = (end_date - start_date).days
+        if date_diff > 365:
+            st.warning("Long date ranges may affect data granularity.")
+        
+        # Determine market status
+        market_status = is_market_open()
+        interval = get_appropriate_interval(start_date, end_date, market_status)
+        
+        st.info(f"Using `{interval}` data interval.")
         
         # Refresh Button
         if st.button("🔄 Refresh Data"):
             st.session_state.refresh += 1
             st.session_state.last_updated = datetime.now(india_tz).strftime("%Y-%m-%d %H:%M:%S")
-            # Clear cached data by updating refresh counter
             st.cache_data.clear()
-        
-        st.markdown("---")
-        st.markdown("**Last Updated:**")
-        st.write(st.session_state.last_updated if st.session_state.last_updated else "Not updated yet.")
-        
-        # Market Status Indicator
-        market_status = is_market_open()
-        if market_status:
-            st.success("📈 Market is currently OPEN.")
-        else:
-            st.info("📉 Market is CLOSED. Showing latest available data.")
 
-    # Main content area
+    # Main content
     try:
         with st.spinner('Fetching sector data...'):
-            # Ensure that start_date is before end_date
-            if start_date <= end_date:
-                # Determine interval based on market status
-                interval = '1m' if market_status else '1d'
-                sector_data = fetch_sector_performance(start_date, end_date, interval=interval)
-                last_updated = datetime.now(india_tz).strftime("%Y-%m-%d %H:%M:%S")
+            sector_data = fetch_sector_performance(start_date, end_date, interval=interval)
+            
+            if not sector_data:
+                st.warning("No valid sector data available for the selected date range.")
+                st.stop()
+            
+            # Calculate summary stats
+            stats = get_summary_stats(sector_data)
+            
+            # Summary Cards
+            col1, col2, col3, col4 = st.columns(4)
+            
+            col1.metric("Sectors Up", stats['positive'], "")
+            col2.metric("Sectors Down", stats['negative'], "")
+            
+            if stats['best'] and stats['best']['Performance'] != 0.0:
+                col3.metric(
+                    "Best Performer", 
+                    f"{stats['best']['Sector']} ({format_performance(stats['best']['Performance'])})"
+                )
             else:
-                sector_data = []
-                last_updated = datetime.now(india_tz).strftime("%Y-%m-%d %H:%M:%S")
-
-        # Update last updated time if not set
-        if not st.session_state.last_updated and sector_data:
-            st.session_state.last_updated = last_updated
-
-        # Calculate summary stats
-        stats = get_summary_stats(sector_data)
-        
-        # Summary Cards
-        col1, col2, col3, col4 = st.columns(4)
-        
-        col1.metric("Sectors Up", stats['positive'], "")
-        col2.metric("Sectors Down", stats['negative'], "")
-        
-        if stats['best'] and stats['best']['Performance'] != 0.0:
-            col3.metric(
-                "Best Performer", 
-                f"{stats['best']['Sector']} ({format_performance(stats['best']['Performance'])})"
-            )
-        else:
-            col3.metric("Best Performer", "N/A", "")
-        
-        if stats['worst'] and stats['worst']['Performance'] != 0.0:
-            col4.metric(
-                "Worst Performer", 
-                f"{stats['worst']['Sector']} ({format_performance(stats['worst']['Performance'])})"
-            )
-        else:
-            col4.metric("Worst Performer", "N/A", "")
-        
-        st.markdown("---")
-        
-        if sector_data:
+                col3.metric("Best Performer", "N/A", "")
+            
+            if stats['worst'] and stats['worst']['Performance'] != 0.0:
+                col4.metric(
+                    "Worst Performer", 
+                    f"{stats['worst']['Sector']} ({format_performance(stats['worst']['Performance'])})"
+                )
+            else:
+                col4.metric("Worst Performer", "N/A", "")
+            
+            st.markdown("---")
+            
             # Charts
-            fig = make_subplots(rows=1, cols=2, subplot_titles=("Performance by Sector", "Sector Distribution"), 
-                                specs=[[{"type": "bar"}, {"type": "pie"}]])
+            fig = make_subplots(
+                rows=1, cols=2, 
+                subplot_titles=("Performance by Sector", "Sector Distribution"), 
+                specs=[[{"type": "bar"}, {"type": "pie"}]]
+            )
             
             # Bar Chart
             fig.add_trace(
@@ -279,9 +284,12 @@ def main():
             st.markdown("---")
             
             # Performance Table
-            df = pd.DataFrame(sector_data)
-            df['Performance'] = df['Performance'].apply(format_performance)
-            df = df.rename(columns={'Sector': 'Sector', 'Performance': 'Performance (%)', 'Ticker': 'Ticker'})
+            # Create DataFrame excluding 'Color' and 'Historical Data'
+            df = pd.DataFrame([{
+                'Sector': item['Sector'],
+                'Performance (%)': format_performance(item['Performance']),
+                'Ticker': item['Ticker']
+            } for item in sector_data])
             
             def color_performance(val):
                 """
@@ -290,21 +298,22 @@ def main():
                 try:
                     num = float(val.strip('%'))
                     if num > 0:
-                        return 'color: green'
+                        color = 'green'
                     elif num < 0:
-                        return 'color: red'
+                        color = 'red'
                     else:
-                        return 'color: gray'
+                        color = 'gray'
+                    return f'color: {color}'
                 except:
                     return 'color: black'
             
-            # Using Styler.applymap for compatibility with future versions
+            # Apply styling to the Performance column
             styled_df = df.style.applymap(color_performance, subset=['Performance (%)'])
             
             st.subheader("Sector Performance Table")
             st.dataframe(styled_df, use_container_width=True)
             
-            st.markdown(f"**Last Updated:** {last_updated}")
+            st.markdown(f"**Last Updated:** {st.session_state.last_updated}")
             
             # Optional: Display historical data for debugging
             show_debug = st.checkbox("Show Debugging Information")
@@ -320,17 +329,15 @@ def main():
                             st.dataframe(hist.tail(10))
                         else:
                             st.write("No historical data available.")
-        else:
-            st.warning("No sector performance data available for the selected date range.")
-
+    
     except Exception as e:
-        st.error(f"An unexpected error occurred: {e}")
+        st.error(f"An unexpected error occurred: {str(e)}")
         st.markdown("""
-            **Suggestions:**
-            - Ensure you have a stable internet connection.
-            - Verify that the `yfinance` library is correctly installed.
-            - Check for any updates or issues with the `yfinance` library.
-            - Confirm that the sector ETF symbols are correct.
+            **Troubleshooting Steps:**
+            1. Try selecting a shorter date range.
+            2. Refresh the page.
+            3. Check your internet connection.
+            4. If the issue persists, try again later.
         """)
 
 if __name__ == "__main__":