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pages/1_High_Performing_Shares.py

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+import streamlit as st
+import yfinance as yf
+import pandas as pd
+from datetime import datetime, timedelta
+import plotly.graph_objects as go
+
+# ---------------------------
+# Configuration and Constants
+# ---------------------------
+
+# Define Indian sectors and their corresponding prominent stocks
+SECTORS = {
+    'Information Technology': ['TCS.NS', 'INFY.NS', 'WIPRO.NS', 'HCLTECH.NS', 'TECHM.NS'],
+    'Pharmaceuticals': ['SUNPHARMA.NS', 'DRREDDY.NS', 'CIPLA.NS', 'BIOCON.NS', 'DIVISLAB.NS'],
+    'Banking': ['HDFCBANK.NS', 'ICICIBANK.NS', 'KOTAKBANK.NS', 'AXISBANK.NS', 'SBIN.NS'],
+    'Energy': ['RELIANCE.NS', 'ONGC.NS', 'IOC.NS', 'BPCL.NS', 'TATAMOTORS.NS'],
+    'Automobiles': ['MARUTI.NS', 'TATAMOTORS.NS', 'BAJAJ-AUTO.NS', 'M&M.NS', 'HEROMOTOCO.NS'],
+    'Consumer Goods': ['HINDUNILVR.NS', 'ITC.NS', 'NESTLEIND.NS', 'TATACONSUM.NS', 'DABUR.NS'],
+    'Financial Services': ['BAJAJFINSV.NS', 'HDFC.NS', 'AXISBANK.NS', 'ICICIPRULI.NS', 'INDUSINDBK.NS'],
+    'Metals': ['TATASTEEL.NS', 'JSWSTEEL.NS', 'HINDALCO.NS', 'SAIL.NS', 'JINDALSTEL.NS'],
+    'Telecommunication Services': ['BHARTIARTL.NS', 'RELIANCE.NS', 'TATACOMM.NS', 'VODAFONE-I.NS', 'IDEA.NS'],
+    'Real Estate': ['DLF.NS', 'PROPTIA.NS', 'OIL.NS', 'BRIGADE.NS', 'ANSALHSG.NS']
+}
+
+COLORS = [
+    '#2E7D32', '#1976D2', '#C62828', '#F57C00', 
+    '#6A1B9A', '#283593', '#00838F', '#558B2F',
+    '#D84315', '#4527A0', '#00695C'
+]
+
+# ---------------------------
+# Utility Functions
+# ---------------------------
+
+def get_high_performing_stocks(sector, top_n=5, period='1mo'):
+    """
+    Fetches and returns the top N high performing stocks in the specified sector over a given period.
+    
+    Parameters:
+        sector (str): The sector name.
+        top_n (int): Number of top performers to return.
+        period (str): Time period for performance calculation (e.g., '1mo').
+    
+    Returns:
+        pd.DataFrame: DataFrame containing top N performing stocks.
+    """
+    if sector not in SECTORS:
+        st.error(f"No data available for sector: {sector}")
+        return pd.DataFrame()
+    
+    tickers = SECTORS[sector]
+    data = {}
+    company_names = []
+    for ticker in tickers:
+        stock = yf.Ticker(ticker)
+        hist = stock.history(period=period)
+        if not hist.empty and len(hist['Close']) >= 2:
+            hist = hist.sort_index()
+            latest_close = hist['Close'][-1]
+            oldest_close = hist['Close'][0]
+            performance = ((latest_close - oldest_close) / oldest_close) * 100
+            data[ticker] = performance
+            info = stock.info
+            company_names.append(info.get('shortName', 'N/A'))
+        else:
+            data[ticker] = 0.0  # Assign 0.0% if insufficient data
+            company_names.append('N/A')
+    
+    # Create DataFrame
+    df = pd.DataFrame(list(data.items()), columns=['Ticker', 'Performance (%)'])
+    df['Company'] = company_names
+    # Sort by performance descending
+    df = df.sort_values(by='Performance (%)', ascending=False)
+    # Return top N
+    return df.head(top_n)
+
+# ---------------------------
+# Streamlit UI
+# ---------------------------
+
+def main():
+    st.title("📈 High Performing Shares by Sector")
+    
+    # Sector selection
+    sector = st.selectbox("Select a Sector", list(SECTORS.keys()))
+    
+    # Performance period selection
+    period = st.selectbox("Select Performance Period", ['1mo', '3mo', '6mo', '1y'], index=0)
+    
+    if st.button("Get Top Performers"):
+        if sector:
+            with st.spinner(f'Fetching top performers for {sector} sector over {period}...'):
+                top_stocks = get_high_performing_stocks(sector, top_n=5, period=period)
+                if not top_stocks.empty:
+                    st.subheader(f"Top 5 High Performing Stocks in {sector} Sector over {period}")
+                    st.dataframe(top_stocks[['Ticker', 'Company', 'Performance (%)']])
+    
+                    # Bar Chart
+                    fig = go.Figure(data=[
+                        go.Bar(
+                            x=top_stocks['Ticker'],
+                            y=top_stocks['Performance (%)'],
+                            marker_color=COLORS[:len(top_stocks)]
+                        )
+                    ])
+                    fig.update_layout(title="Top Performing Stocks", xaxis_title="Ticker", yaxis_title="Performance (%)")
+                    st.plotly_chart(fig)
+                else:
+                    st.warning("No high performing stocks found.")
+    
+    st.markdown("---")
+    st.markdown("**Note:** This is a demonstration. In a production application, consider integrating a dynamic data source for sector stocks.")
+
+if __name__ == "__main__":
+        main()