Delete main.py

main.py

@@ -1,544 +0,0 @@
-import streamlit as st
-import pandas as pd
-import plotly.graph_objects as go
-import plotly.express as px
-from datetime import datetime, timedelta
-from phi.agent.agent import Agent
-from phi.model.groq import Groq
-from phi.tools.yfinance import YFinanceTools
-from phi.tools.duckduckgo import DuckDuckGo
-from phi.tools.googlesearch import GoogleSearch
-import yfinance as yf
-
-import os
-from dotenv import load_dotenv
-
-# Load environment variables from .env file
-load_dotenv()
-
-# Get API key from environment variables
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-
-# Add error handling
-if not GROQ_API_KEY:
-    st.error("GROQ_API_KEY not found. Please check your .env file.")
-
-# Enhanced stock symbol mappings
-COMMON_STOCKS = {
-    # US Stocks
-    'NVIDIA': 'NVDA',
-    'APPLE': 'AAPL',
-    'GOOGLE': 'GOOGL',
-    'MICROSOFT': 'MSFT',
-    'TESLA': 'TSLA',
-    'AMAZON': 'AMZN',
-    'META': 'META',
-    'NETFLIX': 'NFLX',
-    # Indian Stocks - NSE
-    'TCS': 'TCS.NS',
-    'RELIANCE': 'RELIANCE.NS',
-    'INFOSYS': 'INFY.NS',
-    'WIPRO': 'WIPRO.NS',
-    'HDFC': 'HDFCBANK.NS',
-    'TATAMOTORS': 'TATAMOTORS.NS',
-    'ICICIBANK': 'ICICIBANK.NS',
-    'SBIN': 'SBIN.NS',
-    'MARUTI': 'MARUTI.NS',
-    'BHARTIARTL': 'BHARTIARTL.NS',
-    'HCLTECH': 'HCLTECH.NS',
-    'ITC': 'ITC.NS',
-    'AXISBANK': 'AXISBANK.NS'
-}
-
-# Page configuration
-st.set_page_config(
-    page_title="Advanced Stock Market Analysis",
-    page_icon="📈",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# Custom CSS with improved styling
-st.markdown("""
-    <style>
-    .main {
-        padding: 2rem;
-    }
-    .stApp {
-        max-width: 1400px;
-        margin: 0 auto;
-    }
-    .metric-card {
-        background-color: #f8f9fa;
-        border-radius: 10px;
-        padding: 1rem;
-        margin: 0.5rem 0;
-        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-        transition: transform 0.2s;
-    }
-    .metric-card:hover {
-        transform: translateY(-2px);
-        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
-    }
-    .stock-header {
-        font-size: 28px;
-        font-weight: bold;
-        margin-bottom: 20px;
-        color: #f4e285;
-        text-align: center;
-        padding: 1rem;
-        background: #1a1f36;
-        border-radius: 10px;
-    }
-    .news-card {
-        background-color: white;
-        padding: 1rem;
-        border-radius: 5px;
-        margin: 10px 0;
-        border-left: 4px solid #1f77b4;
-        transition: transform 0.2s;
-    }
-    .news-card:hover {
-        transform: translateX(5px);
-    }
-    .stButton>button {
-        width: 100%;
-    }
-    .market-indicator {
-        font-size: 16px;
-        color: #666;
-        text-align: center;
-        margin-bottom: 1rem;
-    }
-    </style>
-    """, unsafe_allow_html=True)
-
-# Initialize session state
-if 'agents_initialized' not in st.session_state:
-    st.session_state.agents_initialized = False
-    st.session_state.watchlist = set()
-    st.session_state.analysis_history = []
-    st.session_state.last_refresh = None
-
-def initialize_agents():
-    """Initialize all agent instances with improved error handling"""
-    if not st.session_state.agents_initialized:
-        try:
-            st.session_state.web_agent = Agent(
-                name="Web Search Agent",
-                role="Search the web for the information",
-                model=Groq(api_key=GROQ_API_KEY,
-                           id="llama-3.3-70b-versatile"),
-                tools=[
-                    GoogleSearch(fixed_language='english', fixed_max_results=5)
-                    # DuckDuckGo(fixed_max_results=1)
-                ],
-                instructions=['Always include sources and verification'],
-                show_tool_calls=True,
-                markdown=True
-            )
-
-            st.session_state.finance_agent = Agent(
-                name="Financial AI Agent",
-                role="Providing financial insights",
-                model=Groq(api_key=GROQ_API_KEY,
-                           id="llama-3.3-70b-versatile"),
-                tools=[
-                    YFinanceTools(
-                        stock_price=True,
-                        company_news=True,
-                        analyst_recommendations=True,
-                        historical_prices=True
-                    )
-                ],
-                instructions=["Provide detailed analysis with data visualization"],
-                show_tool_calls=True,
-                markdown=True
-            )
-
-            st.session_state.multi_ai_agent = Agent(
-                name='A Stock Market Agent',
-                role='A comprehensive assistant specializing in stock market analysis',
-                model=Groq(api_key=GROQ_API_KEY,
-                           id="llama-3.3-70b-versatile"),
-                team=[st.session_state.web_agent, st.session_state.finance_agent],
-                instructions=["Provide comprehensive analysis with multiple data sources"],
-                show_tool_calls=True,
-                markdown=True
-            )
-
-            st.session_state.agents_initialized = True
-            return True
-        except Exception as e:
-            st.error(f"Error initializing agents: {str(e)}")
-            return False
-
-def get_symbol_from_name(stock_name):
-    """Enhanced function to fetch stock symbol from full stock name"""
-    try:
-        # Clean up input
-        stock_name = stock_name.strip().upper()
-        
-        # First check if it's in our common stocks dictionary
-        if stock_name in COMMON_STOCKS:
-            return COMMON_STOCKS[stock_name]
-        
-        # Check if it's already a valid symbol
-        ticker = yf.Ticker(stock_name)
-        try:
-            info = ticker.info
-            if info and 'symbol' in info:
-                return stock_name
-        except:
-            pass
-        
-        # Try Indian stock market (NSE)
-        try:
-            indian_symbol = f"{stock_name}.NS"
-            ticker = yf.Ticker(indian_symbol)
-            info = ticker.info
-            if info and 'symbol' in info:
-                return indian_symbol
-        except:
-            # Try BSE
-            try:
-                bse_symbol = f"{stock_name}.BO"
-                ticker = yf.Ticker(bse_symbol)
-                info = ticker.info
-                if info and 'symbol' in info:
-                    return bse_symbol
-            except:
-                pass
-        
-        st.error(f"Could not find valid symbol for {stock_name}")
-        return None
-    except Exception as e:
-        st.error(f"Error processing {stock_name}: {str(e)}")
-        return None
-
-def get_stock_data(symbol, period="1y"):
-    """Enhanced function to fetch stock data with proper cache handling"""
-    try:
-        # Create a new ticker instance
-        stock = yf.Ticker(symbol)
-        
-        # Fetch data with error handling
-        try:
-            info = stock.info
-            if not info:
-                raise ValueError("No data retrieved for symbol")
-        except Exception as info_error:
-            # If .NS suffix is missing for Indian stocks, try adding it
-            if not symbol.endswith('.NS') and not symbol.endswith('.BO'):
-                try:
-                    indian_symbol = f"{symbol}.NS"
-                    stock = yf.Ticker(indian_symbol)
-                    info = stock.info
-                    symbol = indian_symbol
-                except:
-                    # Try Bombay Stock Exchange
-                    try:
-                        bse_symbol = f"{symbol}.BO"
-                        stock = yf.Ticker(bse_symbol)
-                        info = stock.info
-                        symbol = bse_symbol
-                    except:
-                        raise info_error
-            else:
-                raise info_error
-
-        # Fetch historical data
-        hist = stock.history(period=period, interval="1d", auto_adjust=True)
-        
-        if hist.empty:
-            raise ValueError("No historical data available")
-            
-        return info, hist
-    except Exception as e:
-        st.error(f"Error fetching data for {symbol}: {str(e)}")
-        return None, None
-
-def create_price_chart(hist_data, symbol):
-    """Create an interactive price chart using plotly"""
-    fig = go.Figure()
-    
-    # Add candlestick chart
-    fig.add_trace(go.Candlestick(
-        x=hist_data.index,
-        open=hist_data['Open'],
-        high=hist_data['High'],
-        low=hist_data['Low'],
-        close=hist_data['Close'],
-        name='Price'
-    ))
-    
-    # Add moving averages
-    ma20 = hist_data['Close'].rolling(window=20).mean()
-    ma50 = hist_data['Close'].rolling(window=50).mean()
-    
-    fig.add_trace(go.Scatter(x=hist_data.index, y=ma20, name='20 Day MA', line=dict(color='orange')))
-    fig.add_trace(go.Scatter(x=hist_data.index, y=ma50, name='50 Day MA', line=dict(color='blue')))
-    
-    fig.update_layout(
-        title=f'{symbol} Stock Price',
-        yaxis_title='Price',
-        template='plotly_white',
-        xaxis_rangeslider_visible=False,
-        height=600
-    )
-    
-    return fig
-
-def create_volume_chart(hist_data):
-    """Create enhanced volume chart using plotly"""
-    # Calculate volume moving average
-    volume_ma = hist_data['Volume'].rolling(window=20).mean()
-    
-    fig = go.Figure()
-    
-    # Add volume bars
-    fig.add_trace(go.Bar(
-        x=hist_data.index,
-        y=hist_data['Volume'],
-        name='Volume',
-        marker_color='rgba(31, 119, 180, 0.3)'
-    ))
-    
-    # Add volume moving average
-    fig.add_trace(go.Scatter(
-        x=hist_data.index,
-        y=volume_ma,
-        name='20 Day Volume MA',
-        line=dict(color='red')
-    ))
-    
-    fig.update_layout(
-        title='Trading Volume Analysis',
-        yaxis_title='Volume',
-        template='plotly_white',
-        height=400
-    )
-    
-    return fig
-
-def format_large_number(number):
-    """Format large numbers into readable format"""
-    if number >= 1e12:
-        return f"${number/1e12:.2f}T"
-    elif number >= 1e9:
-        return f"${number/1e9:.2f}B"
-    elif number >= 1e6:
-        return f"${number/1e6:.2f}M"
-    else:
-        return f"${number:,.2f}"
-
-def display_metrics(info):
-    """Display enhanced key metrics in a grid"""
-    col1, col2, col3, col4 = st.columns(4)
-    
-    with col1:
-        st.markdown('<div class="metric-card">', unsafe_allow_html=True)
-        market_cap = info.get('marketCap', 'N/A')
-        if market_cap != 'N/A':
-            market_cap = format_large_number(market_cap)
-        st.metric("Market Cap", market_cap)
-        st.markdown('</div>', unsafe_allow_html=True)
-    
-    with col2:
-        st.markdown('<div class="metric-card">', unsafe_allow_html=True)
-        pe_ratio = info.get('trailingPE', 'N/A')
-        if pe_ratio != 'N/A':
-            pe_ratio = f"{pe_ratio:.2f}"
-        st.metric("P/E Ratio", pe_ratio)
-        st.markdown('</div>', unsafe_allow_html=True)
-    
-    with col3:
-        st.markdown('<div class="metric-card">', unsafe_allow_html=True)
-        high = info.get('fiftyTwoWeekHigh', 'N/A')
-        if high != 'N/A':
-            high = f"${high:.2f}"
-        st.metric("52 Week High", high)
-        st.markdown('</div>', unsafe_allow_html=True)
-    
-    with col4:
-        st.markdown('<div class="metric-card">', unsafe_allow_html=True)
-        low = info.get('fiftyTwoWeekLow', 'N/A')
-        if low != 'N/A':
-            low = f"${low:.2f}"
-        st.metric("52 Week Low", low)
-        st.markdown('</div>', unsafe_allow_html=True)
-
-def main():
-    # Sidebar
-    with st.sidebar:
-        st.header("📊 Analysis Options")
-        analysis_type = st.selectbox(
-            "Choose Analysis Type",
-            ["Comprehensive Analysis", "Technical Analysis", "Fundamental Analysis", 
-             "News Analysis", "Sentiment Analysis"]
-        )
-        
-        # Add market selection
-        market = st.selectbox(
-            "Select Market",
-            ["US Market", "Indian Market (NSE)", "Indian Market (BSE)"]
-        )
-        
-        st.markdown("---")
-        
-        
-
-    # Main content
-    st.markdown('<h1 class="stock-header">🤖 Advanced Stock Market Analysis By NeuSpaarX</h1>', 
-                unsafe_allow_html=True)
-    
-    # Search and Analysis Section
-    col1, col2 = st.columns([2, 1])
-    with col1:
-        stock_input = st.text_input(
-            "Enter Stock Name or Symbol",
-            help="Enter company name (e.g., NVIDIA) or symbol (e.g., NVDA)"
-        )
-    with col2:
-        date_range = st.selectbox(
-            "Select Time Range",
-            ["1 Month", "3 Months", "6 Months", "1 Year", "5 Years"], key="time_range"
-        )
-        # Convert selected range to yfinance period format
-        period_map = {
-            "1 Month": "1mo",
-            "3 Months": "3mo",
-            "6 Months": "6mo",
-            "1 Year": "1y",
-            "5 Years": "5y"
-        }
-        period = period_map[date_range]
-
-    if st.button("Analyze", type="primary"):
-        if not stock_input:
-            st.error("Please enter a stock name or symbol.")
-            return
-
-        # Convert input to symbol
-        stock_symbol = get_symbol_from_name(stock_input)
-        if stock_symbol:
-            try:
-                # Initialize agents
-                if initialize_agents():
-                    # Show loading spinner
-                    with st.spinner(f"Analyzing {stock_symbol}..."):
-                        # Fetch fresh stock data
-                        info, hist = get_stock_data(stock_symbol, period=period)
-                        
-                        if info and hist is not None:
-                            # Display market status
-                            market_status = "🟢 Market Open" if info.get('regularMarketOpen') else "🔴 Market Closed"
-                            st.markdown(f"<div class='market-indicator'>{market_status}</div>", unsafe_allow_html=True)
-                            
-                            # Create tabs for different sections
-                            overview_tab, charts_tab = st.tabs(["Overview", "Charts"])
-                            
-                            with overview_tab:
-                                # Display company info
-                                st.markdown("### Company Overview")
-                                st.write(info.get('longBusinessSummary', 'No description available.'))
-                                
-                                # Display key metrics
-                                st.markdown("### Key Metrics")
-                                display_metrics(info)
-                                
-                                # Additional company information
-                                col1, col2 = st.columns(2)
-                                with col1:
-                                    st.markdown("### Company Details")
-                                    st.write(f"Sector: {info.get('sector', 'N/A')}")
-                                    st.write(f"Industry: {info.get('industry', 'N/A')}")
-                                    st.write(f"Country: {info.get('country', 'N/A')}")
-                                    st.write(f"Employees: {info.get('fullTimeEmployees', 'N/A'):,}")
-                                
-                                with col2:
-                                    st.markdown("### Trading Information")
-                                    st.write(f"Exchange: {info.get('exchange', 'N/A')}")
-                                    st.write(f"Currency: {info.get('currency', 'N/A')}")
-                                    st.write(f"Volume: {info.get('volume', 'N/A'):,}")
-                            
-                            with charts_tab:
-                                # Price chart
-                                st.markdown("### Price Analysis")
-                                price_chart = create_price_chart(hist, stock_symbol)
-                                st.plotly_chart(price_chart, use_container_width=True)
-                                
-                                # Volume chart
-                                volume_chart = create_volume_chart(hist)
-                                st.plotly_chart(volume_chart, use_container_width=True)
-                                
-                                # Technical indicators
-                                st.markdown("### Technical Indicators")
-                                col1, col2, col3 = st.columns(3)
-                                
-                                with col1:
-                                    rsi = hist['Close'].diff()
-                                    rsi_pos = rsi.copy()
-                                    rsi_neg = rsi.copy()
-                                    rsi_pos[rsi_pos < 0] = 0
-                                    rsi_neg[rsi_neg > 0] = 0
-                                    rsi_14_pos = rsi_pos.rolling(window=14).mean()
-                                    rsi_14_neg = abs(rsi_neg.rolling(window=14).mean())
-                                    rsi_14 = 100 - (100 / (1 + rsi_14_pos / rsi_14_neg))
-                                    st.metric("RSI (14)", f"{rsi_14.iloc[-1]:.2f}")
-                                
-                                with col2:
-                                    ma20 = hist['Close'].rolling(window=20).mean()
-                                    ma50 = hist['Close'].rolling(window=50).mean()
-                                    cross_signal = "Bullish" if ma20.iloc[-1] > ma50.iloc[-1] else "Bearish"
-                                    st.metric("MA Cross Signal", cross_signal)
-                                
-                                with col3:
-                                    volatility = hist['Close'].pct_change().std() * (252 ** 0.5) * 100
-                                    st.metric("Annualized Volatility", f"{volatility:.2f}%")
-
-
-                            # Add refresh button
-                            if st.button("🔄 Refresh Data"):
-                                st.session_state.last_refresh = datetime.now()
-                                st.experimental_rerun()
-
-            except Exception as e:
-                st.error(f"An error occurred: {str(e)}")
-
-    # Display analysis history
-    if st.session_state.analysis_history:
-        st.markdown("---")
-        st.markdown("### Recent Analysis History")
-        history_df = pd.DataFrame(st.session_state.analysis_history)
-        history_df['timestamp'] = history_df['timestamp'].dt.strftime('%Y-%m-%d %H:%M:%S')
-        st.dataframe(history_df, use_container_width=True)
-
-    # Footer
-    st.markdown("---")
-    st.markdown("### About")
-    st.markdown("""
-        This advanced stock market analysis tool combines:
-        - Real-time market data analysis
-        - AI-powered insights and predictions
-        - Technical and fundamental analysis
-        - News and sentiment analysis
-        - Interactive charts and visualizations
-        
-        Features:
-        - Support for both US and Indian markets (NSE/BSE)
-        - Company name and symbol resolution
-        - Watchlist management
-        - Multiple timeframe analysis
-        - Technical indicators
-        
-        Use the sidebar to configure your analysis preferences and manage your watchlist.
-    """)
-    
-    # Display last refresh time if available
-    if st.session_state.last_refresh:
-        st.markdown(f"<div class='market-indicator'>Last refreshed: {st.session_state.last_refresh.strftime('%Y-%m-%d %H:%M:%S')}</div>", 
-                   unsafe_allow_html=True)
-
-if __name__ == "__main__":
-    main()