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| import gradio as gr | |
| import yfinance as yf | |
| import pandas as pd | |
| import requests | |
| import json | |
| from datetime import datetime, timedelta | |
| import plotly.graph_objects as go | |
| import plotly.express as px | |
| # Tool 1: Get Stock Price | |
| def get_stock_price(symbol): | |
| """Get real-time stock price and basic info""" | |
| try: | |
| stock = yf.Ticker(symbol.upper()) | |
| hist = stock.history(period="1d") | |
| info = stock.info | |
| if hist.empty: | |
| return f"Error: Could not find stock data for {symbol}" | |
| current_price = hist['Close'].iloc[-1] | |
| prev_close = info.get('previousClose', hist['Close'].iloc[-1]) | |
| change = current_price - prev_close | |
| change_percent = (change / prev_close) * 100 | |
| result = f""" | |
| 📈 **{symbol.upper()} Stock Price** | |
| 💰 Current Price: ${current_price:.2f} | |
| 📊 Change: ${change:.2f} ({change_percent:+.2f}%) | |
| 📅 Previous Close: ${prev_close:.2f} | |
| 🏢 Company: {info.get('longName', 'N/A')} | |
| 📈 Market Cap: ${info.get('marketCap', 0):,} | |
| """ | |
| return result | |
| except Exception as e: | |
| return f"Error fetching stock price for {symbol}: {str(e)}" | |
| # Tool 2: Get Stock Fundamentals | |
| def get_stock_fundamentals(symbol): | |
| """Get fundamental analysis data""" | |
| try: | |
| stock = yf.Ticker(symbol.upper()) | |
| info = stock.info | |
| # Key fundamental metrics | |
| pe_ratio = info.get('trailingPE', 'N/A') | |
| forward_pe = info.get('forwardPE', 'N/A') | |
| price_to_book = info.get('priceToBook', 'N/A') | |
| debt_to_equity = info.get('debtToEquity', 'N/A') | |
| roe = info.get('returnOnEquity', 'N/A') | |
| profit_margin = info.get('profitMargins', 'N/A') | |
| revenue_growth = info.get('revenueGrowth', 'N/A') | |
| # Format percentages | |
| if isinstance(roe, (int, float)): | |
| roe = f"{roe*100:.2f}%" | |
| if isinstance(profit_margin, (int, float)): | |
| profit_margin = f"{profit_margin*100:.2f}%" | |
| if isinstance(revenue_growth, (int, float)): | |
| revenue_growth = f"{revenue_growth*100:.2f}%" | |
| result = f""" | |
| 🔍 **{symbol.upper()} Fundamental Analysis** | |
| 📊 **Valuation Metrics:** | |
| • P/E Ratio: {pe_ratio} | |
| • Forward P/E: {forward_pe} | |
| • Price-to-Book: {price_to_book} | |
| 💰 **Financial Health:** | |
| • Debt-to-Equity: {debt_to_equity} | |
| • Return on Equity: {roe} | |
| • Profit Margin: {profit_margin} | |
| • Revenue Growth: {revenue_growth} | |
| 🏢 **Company Info:** | |
| • Sector: {info.get('sector', 'N/A')} | |
| • Industry: {info.get('industry', 'N/A')} | |
| • Employees: {info.get('fullTimeEmployees', 'N/A'):,} | |
| • Market Cap: ${info.get('marketCap', 0):,} | |
| """ | |
| return result | |
| except Exception as e: | |
| return f"Error fetching fundamentals for {symbol}: {str(e)}" | |
| # Tool 3: Compare Stocks | |
| def compare_stocks(symbol1, symbol2, symbol3=""): | |
| """Compare 2-3 stocks side by side""" | |
| try: | |
| symbols = [s.upper().strip() for s in [symbol1, symbol2, symbol3] if s.strip()] | |
| if len(symbols) < 2: | |
| return "Please provide at least 2 stock symbols" | |
| comparison_data = [] | |
| for symbol in symbols: | |
| stock = yf.Ticker(symbol) | |
| info = stock.info | |
| hist = stock.history(period="1d") | |
| if not hist.empty: | |
| current_price = hist['Close'].iloc[-1] | |
| comparison_data.append({ | |
| 'Symbol': symbol, | |
| 'Company': info.get('longName', 'N/A')[:30], | |
| 'Price': f"${current_price:.2f}", | |
| 'P/E Ratio': info.get('trailingPE', 'N/A'), | |
| 'Market Cap': f"${info.get('marketCap', 0)/1e9:.1f}B", | |
| 'Sector': info.get('sector', 'N/A'), | |
| 'ROE': f"{info.get('returnOnEquity', 0)*100:.1f}%" if info.get('returnOnEquity') else 'N/A' | |
| }) | |
| if not comparison_data: | |
| return "Could not fetch data for any of the provided symbols" | |
| # Create comparison table | |
| result = "📊 **Stock Comparison**\n\n" | |
| result += "| Metric | " + " | ".join([data['Symbol'] for data in comparison_data]) + " |\n" | |
| result += "|" + "---|" * (len(comparison_data) + 1) + "\n" | |
| metrics = ['Company', 'Price', 'P/E Ratio', 'Market Cap', 'Sector', 'ROE'] | |
| for metric in metrics: | |
| result += f"| **{metric}** | " | |
| result += " | ".join([str(data.get(metric, 'N/A')) for data in comparison_data]) | |
| result += " |\n" | |
| return result | |
| except Exception as e: | |
| return f"Error comparing stocks: {str(e)}" | |
| # Tool 4: AI-Powered Investment Analysis | |
| def analyze_stock_ai(symbol, analysis_type="comprehensive"): | |
| """AI-powered investment insights""" | |
| try: | |
| stock = yf.Ticker(symbol.upper()) | |
| info = stock.info | |
| hist = stock.history(period="3mo") # 3 months of data | |
| if hist.empty: | |
| return f"Could not fetch data for {symbol}" | |
| # Calculate technical indicators | |
| current_price = hist['Close'].iloc[-1] | |
| price_change_3m = ((current_price - hist['Close'].iloc[0]) / hist['Close'].iloc[0]) * 100 | |
| avg_volume = hist['Volume'].mean() | |
| volatility = hist['Close'].pct_change().std() * 100 | |
| # Get fundamental data | |
| pe_ratio = info.get('trailingPE', 0) | |
| market_cap = info.get('marketCap', 0) | |
| sector = info.get('sector', 'Unknown') | |
| # AI Analysis Logic | |
| analysis = f""" | |
| 🤖 **AI Investment Analysis for {symbol.upper()}** | |
| 📈 **Technical Analysis:** | |
| • 3-Month Performance: {price_change_3m:+.2f}% | |
| • Current Price: ${current_price:.2f} | |
| • Volatility: {volatility:.2f}% | |
| • Average Volume: {avg_volume:,.0f} | |
| 🎯 **Investment Signals:** | |
| """ | |
| # Simple AI-like decision logic | |
| signals = [] | |
| if price_change_3m > 10: | |
| signals.append("🟢 Strong upward momentum") | |
| elif price_change_3m > 0: | |
| signals.append("🟡 Positive trend") | |
| else: | |
| signals.append("🔴 Declining trend") | |
| if pe_ratio and 10 < pe_ratio < 25: | |
| signals.append("🟢 Reasonable valuation") | |
| elif pe_ratio and pe_ratio > 30: | |
| signals.append("🟡 High valuation - growth expected") | |
| elif pe_ratio and pe_ratio < 10: | |
| signals.append("🟡 Low valuation - value opportunity") | |
| if volatility < 20: | |
| signals.append("🟢 Low volatility - stable") | |
| elif volatility > 40: | |
| signals.append("🔴 High volatility - risky") | |
| for signal in signals: | |
| analysis += f"\n• {signal}" | |
| # Risk Assessment | |
| risk_level = "Low" | |
| if volatility > 30 or (pe_ratio and pe_ratio > 40): | |
| risk_level = "High" | |
| elif volatility > 20 or (pe_ratio and pe_ratio > 25): | |
| risk_level = "Medium" | |
| analysis += f""" | |
| ⚠️ **Risk Assessment:** {risk_level} | |
| 🏢 **Sector:** {sector} | |
| 💼 **Market Cap:** ${market_cap/1e9:.1f}B | |
| 📝 **AI Recommendation:** | |
| Based on technical and fundamental analysis, this stock shows {signals[0].split()[1]} characteristics. | |
| Consider your risk tolerance and portfolio diversification before making investment decisions. | |
| ⚠️ *This is AI-generated analysis for educational purposes only. Not financial advice.* | |
| """ | |
| return analysis | |
| except Exception as e: | |
| return f"Error in AI analysis for {symbol}: {str(e)}" | |
| # Create Gradio Interface | |
| def create_interface(): | |
| with gr.Blocks(title="Financial Analyst MCP Tools", theme=gr.themes.Soft()) as demo: | |
| gr.Markdown("# 🏦 Financial Analyst - MCP Tools") | |
| gr.Markdown("Professional stock analysis tools powered by real-time data and AI insights") | |
| with gr.Tabs(): | |
| # Tool 1: Stock Price | |
| with gr.Tab("📈 Stock Price"): | |
| gr.Markdown("### Get Real-time Stock Price") | |
| with gr.Row(): | |
| price_input = gr.Textbox(label="Stock Symbol", placeholder="e.g., AAPL, TSLA, GOOGL") | |
| price_btn = gr.Button("Get Price", variant="primary") | |
| price_output = gr.Markdown() | |
| price_btn.click(get_stock_price, inputs=price_input, outputs=price_output) | |
| # Tool 2: Fundamentals | |
| with gr.Tab("🔍 Fundamentals"): | |
| gr.Markdown("### Stock Fundamental Analysis") | |
| with gr.Row(): | |
| fund_input = gr.Textbox(label="Stock Symbol", placeholder="e.g., AAPL, MSFT") | |
| fund_btn = gr.Button("Analyze Fundamentals", variant="primary") | |
| fund_output = gr.Markdown() | |
| fund_btn.click(get_stock_fundamentals, inputs=fund_input, outputs=fund_output) | |
| # Tool 3: Compare Stocks | |
| with gr.Tab("⚖️ Compare Stocks"): | |
| gr.Markdown("### Side-by-side Stock Comparison") | |
| with gr.Row(): | |
| with gr.Column(): | |
| comp_input1 = gr.Textbox(label="Stock 1", placeholder="e.g., AAPL") | |
| comp_input2 = gr.Textbox(label="Stock 2", placeholder="e.g., MSFT") | |
| comp_input3 = gr.Textbox(label="Stock 3 (Optional)", placeholder="e.g., GOOGL") | |
| comp_btn = gr.Button("Compare Stocks", variant="primary") | |
| comp_output = gr.Markdown() | |
| comp_btn.click(compare_stocks, inputs=[comp_input1, comp_input2, comp_input3], outputs=comp_output) | |
| # Tool 4: AI Analysis | |
| with gr.Tab("🤖 AI Analysis"): | |
| gr.Markdown("### AI-Powered Investment Insights") | |
| with gr.Row(): | |
| ai_input = gr.Textbox(label="Stock Symbol", placeholder="e.g., NVDA, AMD") | |
| ai_btn = gr.Button("AI Analysis", variant="primary") | |
| ai_output = gr.Markdown() | |
| ai_btn.click(analyze_stock_ai, inputs=ai_input, outputs=ai_output) | |
| gr.Markdown("---") | |
| gr.Markdown("*⚠️ Disclaimer: This tool provides educational information only. Not financial advice. Always consult with financial professionals before making investment decisions.*") | |
| return demo | |
| # Launch the app | |
| if __name__ == "__main__": | |
| demo = create_interface() | |
| demo.launch() | |