app.py

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 Information")
    st.markdown("**Developed by : Venugopal Adep**")
    
    # 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()