Create app.py

app.py

@@ -0,0 +1,162 @@
+import streamlit as st
+import yfinance as yf
+import plotly.graph_objects as go
+from datetime import datetime, timedelta
+import pandas as pd
+
+st.set_page_config(layout="wide", page_title="Stock Analysis Dashboard")
+
+def format_number(num):
+    """Format large numbers to readable format with B/M suffix"""
+    if num >= 1e9:
+        return f"₹{num/1e9:.2f}B"
+    elif num >= 1e6:
+        return f"₹{num/1e6:.2f}M"
+    else:
+        return f"₹{num:,.2f}"
+
+def create_price_chart(ticker_data):
+    """Create interactive price chart using Plotly"""
+    fig = go.Figure(data=[go.Candlestick(x=ticker_data.index,
+                open=ticker_data['Open'],
+                high=ticker_data['High'],
+                low=ticker_data['Low'],
+                close=ticker_data['Close'])])
+    
+    fig.update_layout(
+        title="Stock Price Movement",
+        yaxis_title="Price",
+        xaxis_title="Date",
+        template="plotly_dark"
+    )
+    return fig
+
+def create_volume_chart(ticker_data):
+    """Create volume chart using Plotly"""
+    fig = go.Figure(data=[go.Bar(x=ticker_data.index, y=ticker_data['Volume'])])
+    fig.update_layout(
+        title="Trading Volume",
+        yaxis_title="Volume",
+        xaxis_title="Date",
+        template="plotly_dark"
+    )
+    return fig
+
+def main():
+    st.title("📊 Stock Analysis Dashboard")
+    
+    # Input section
+    col1, col2 = st.columns([2,2])
+    with col1:
+        ticker_symbol = st.text_input("Enter Stock Ticker (e.g., RELIANCE.NS for Indian stocks):")
+    with col2:
+        period = st.selectbox("Select Time Period:", 
+                            ["1mo", "3mo", "6mo", "1y", "2y", "5y", "max"],
+                            index=2)
+    
+    if ticker_symbol:
+        try:
+            # Fetch stock data
+            stock = yf.Ticker(ticker_symbol)
+            info = stock.info
+            hist_data = stock.history(period=period)
+            
+            # Company Overview Section
+            st.header("Company Overview")
+            col1, col2, col3 = st.columns([1,1,1])
+            
+            with col1:
+                st.metric("Current Price", format_number(info.get('currentPrice', 0)))
+                st.metric("Market Cap", format_number(info.get('marketCap', 0)))
+                
+            with col2:
+                day_change = info.get('regularMarketChangePercent', 0)
+                st.metric("Day Change", f"{day_change:.2f}%")
+                st.metric("P/E Ratio", f"{info.get('forwardPE', 0):.2f}")
+                
+            with col3:
+                st.metric("52 Week High", format_number(info.get('fiftyTwoWeekHigh', 0)))
+                st.metric("52 Week Low", format_number(info.get('fiftyTwoWeekLow', 0)))
+            
+            # Price Charts
+            st.header("Price Analysis")
+            tab1, tab2 = st.tabs(["Price Chart", "Volume Chart"])
+            
+            with tab1:
+                st.plotly_chart(create_price_chart(hist_data), use_container_width=True)
+            with tab2:
+                st.plotly_chart(create_volume_chart(hist_data), use_container_width=True)
+            
+            # Detailed Information
+            st.header("Detailed Information")
+            col1, col2 = st.columns([1,1])
+            
+            with col1:
+                st.subheader("Company Profile")
+                st.write("**Sector:**", info.get('sector', 'N/A'))
+                st.write("**Industry:**", info.get('industry', 'N/A'))
+                st.write("**Country:**", info.get('country', 'N/A'))
+                st.write("**Website:**", info.get('website', 'N/A'))
+                
+                st.subheader("Key Statistics")
+                metrics_df = pd.DataFrame({
+                    'Metric': [
+                        'Beta',
+                        'Dividend Yield (%)',
+                        'Trailing P/E',
+                        'Forward P/E',
+                        'PEG Ratio',
+                        'Price to Book'
+                    ],
+                    'Value': [
+                        f"{info.get('beta', 0):.2f}",
+                        f"{info.get('dividendYield', 0)*100:.2f}",
+                        f"{info.get('trailingPE', 0):.2f}",
+                        f"{info.get('forwardPE', 0):.2f}",
+                        f"{info.get('pegRatio', 0):.2f}",
+                        f"{info.get('priceToBook', 0):.2f}"
+                    ]
+                })
+                st.dataframe(metrics_df, hide_index=True)
+            
+            with col2:
+                st.subheader("Business Summary")
+                st.write(info.get('longBusinessSummary', 'No summary available'))
+                
+                st.subheader("Financial Metrics")
+                financial_df = pd.DataFrame({
+                    'Metric': [
+                        'Revenue Growth',
+                        'Gross Margins',
+                        'Operating Margins',
+                        'Profit Margins',
+                        'Return on Equity',
+                        'Return on Assets'
+                    ],
+                    'Value': [
+                        f"{info.get('revenueGrowth', 0)*100:.2f}%",
+                        f"{info.get('grossMargins', 0)*100:.2f}%",
+                        f"{info.get('operatingMargins', 0)*100:.2f}%",
+                        f"{info.get('profitMargins', 0)*100:.2f}%",
+                        f"{info.get('returnOnEquity', 0)*100:.2f}%",
+                        f"{info.get('returnOnAssets', 0)*100:.2f}%"
+                    ]
+                })
+                st.dataframe(financial_df, hide_index=True)
+            
+            # Historical Data Download Section
+            st.header("Download Historical Data")
+            csv = hist_data.to_csv()
+            st.download_button(
+                label="📥 Download Historical Data",
+                data=csv,
+                file_name=f"{ticker_symbol}_historical_data.csv",
+                mime="text/csv"
+            )
+            
+        except Exception as e:
+            st.error(f"Error fetching data for {ticker_symbol}. Please check the ticker symbol and try again.")
+            st.error(f"Error details: {str(e)}")
+
+if __name__ == "__main__":
+    main()