!pip install openbb[all] --quietimport warningswarnings.filterwarnings('ignore')import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsfrom datetime import datetime, timedeltaimport openbbfrom openbb import obbpd.set_option('display.max_columns', None)pd.set_option('display.width', 1000)print(" Advanced OpenBB Financial Analysis Tutorial")print("=" * 60)

print("\n 1. BUILDING AND ANALYZING A TECH PORTFOLIO")print("-" * 50)tech_stocks = ['AAPL', 'GOOGL', 'MSFT', 'TSLA', 'NVDA']initial_weights = [0.25, 0.20, 0.25, 0.15, 0.15]end_date = datetime.now().strftime('%Y-%m-%d')start_date = (datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d')portfolio_data = {}portfolio_returns = pd.DataFrame()successful_stocks = []print(f"Fetching data from {start_date} to {end_date}...")for i, symbol in enumerate(tech_stocks):   try:       data = obb.equity.price.historical(symbol=symbol, start_date=start_date, end_date=end_date)       df = data.to_df()             if df.index.duplicated().any():           df = df[~df.index.duplicated(keep='first')]             portfolio_data[symbol] = df             returns = df['close'].pct_change().dropna()       portfolio_returns[symbol] = returns       successful_stocks.append(symbol)             print(f" {symbol}: {len(df)} days of data")   except Exception as e:       print(f" Error fetching {symbol}: {str(e)}")if successful_stocks:   successful_indices = [tech_stocks.index(stock) for stock in successful_stocks]   portfolio_weights = [initial_weights[i] for i in successful_indices]   total_weight = sum(portfolio_weights)   portfolio_weights = [w/total_weight for w in portfolio_weights]     print(f"\n Portfolio composition (normalized weights):")   for stock, weight in zip(successful_stocks, portfolio_weights):       print(f"  {stock}: {weight:.1%}")else:   portfolio_weights = []print("\n 2. PORTFOLIO PERFORMANCE ANALYSIS")print("-" * 50)if not portfolio_returns.empty and portfolio_weights:   weighted_returns = (portfolio_returns * portfolio_weights).sum(axis=1)     annual_return = weighted_returns.mean() * 252   annual_volatility = weighted_returns.std() * np.sqrt(252)   sharpe_ratio = annual_return / annual_volatility if annual_volatility > 0 else 0   max_drawdown = (weighted_returns.cumsum().expanding().max() - weighted_returns.cumsum()).max()     print(f"Portfolio Annual Return: {annual_return:.2%}")   print(f"Portfolio Volatility: {annual_volatility:.2%}")   print(f"Sharpe Ratio: {sharpe_ratio:.3f}")   print(f"Max Drawdown: {max_drawdown:.2%}")     print("\n Individual Stock Performance:")   for stock in successful_stocks:       stock_return = portfolio_returns[stock].mean() * 252       stock_vol = portfolio_returns[stock].std() * np.sqrt(252)       print(f"{stock}: Return {stock_return:.2%}, Volatility {stock_vol:.2%}")else:   print(" No valid portfolio data available for analysis")

print("\n 3. ADVANCED TECHNICAL ANALYSIS")print("-" * 50)symbol = 'NVDA'try:   price_data = obb.equity.price.historical(symbol=symbol, start_date=start_date, end_date=end_date)   df = price_data.to_df()     df['SMA_20'] = df['close'].rolling(window=20).mean()   df['SMA_50'] = df['close'].rolling(window=50).mean()   df['EMA_12'] = df['close'].ewm(span=12).mean()   df['EMA_26'] = df['close'].ewm(span=26).mean()     df['MACD'] = df['EMA_12'] - df['EMA_26']   df['MACD_signal'] = df['MACD'].ewm(span=9).mean()     delta = df['close'].diff()   gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()   loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()   rs = gain / loss   df['RSI'] = 100 - (100 / (1 + rs))     df['BB_middle'] = df['close'].rolling(window=20).mean()   bb_std = df['close'].rolling(window=20).std()   df['BB_upper'] = df['BB_middle'] + (bb_std * 2)   df['BB_lower'] = df['BB_middle'] - (bb_std * 2)     current_price = df['close'].iloc[-1]   current_rsi = df['RSI'].iloc[-1]   macd_signal = "BUY" if df['MACD'].iloc[-1] > df['MACD_signal'].iloc[-1] else "SELL"   price_vs_sma20 = "Above" if current_price > df['SMA_20'].iloc[-1] else "Below"     print(f"\n{symbol} Technical Analysis:")   print(f"Current Price: ${current_price:.2f}")   print(f"RSI (14): {current_rsi:.2f} ({'Overbought' if current_rsi > 70 else 'Oversold' if current_rsi < 30 else 'Neutral'})")   print(f"MACD Signal: {macd_signal}")   print(f"Price vs SMA(20): {price_vs_sma20}")  except Exception as e:   print(f"Error in technical analysis: {str(e)}")print("\n 4. SECTOR ANALYSIS & STOCK SCREENING")print("-" * 50)sectors = {   'Technology': ['AAPL', 'GOOGL', 'MSFT'],   'Electric Vehicles': ['TSLA', 'RIVN', 'LCID'],   'Semiconductors': ['NVDA', 'AMD', 'INTC']}sector_performance = {}for sector_name, stocks in sectors.items():   sector_returns = []   for stock in stocks:       try:           data = obb.equity.price.historical(symbol=stock, start_date=start_date, end_date=end_date)           df = data.to_df()           if df.index.duplicated().any():               df = df[~df.index.duplicated(keep='first')]           returns = df['close'].pct_change().dropna()           sector_returns.append(returns.mean() * 252)       except Exception as e:           print(f" Failed to fetch {stock}: {str(e)}")           continue     if sector_returns:       avg_return = np.mean(sector_returns)       sector_performance[sector_name] = avg_return       print(f"{sector_name}: {avg_return:.2%} average annual return")print("\n 5. MARKET SENTIMENT ANALYSIS")print("-" * 50)for symbol in successful_stocks[:2]:    try:       news = obb.news.company(symbol=symbol, limit=3)       news_df = news.to_df()       print(f"\n{symbol} Recent News Headlines:")       for idx, row in news_df.iterrows():           print(f"• {row.get('title', 'N/A')[:80]}...")           break    except Exception as e:       print(f"News not available for {symbol}: {str(e)}")

print("\n  6. RISK ANALYSIS")print("-" * 50)if not portfolio_returns.empty and len(portfolio_returns.columns) > 1:   correlation_matrix = portfolio_returns.corr()   print("\nPortfolio Correlation Matrix:")   print(correlation_matrix.round(3))     portfolio_var = np.dot(portfolio_weights, np.dot(correlation_matrix *                         (portfolio_returns.std().values.reshape(-1,1) *                          portfolio_returns.std().values.reshape(1,-1)),                         portfolio_weights))   portfolio_risk = np.sqrt(portfolio_var) * np.sqrt(252)   print(f"\nPortfolio Risk (Volatility): {portfolio_risk:.2%}")print("\n 7. CREATING PERFORMANCE VISUALIZATIONS")print("-" * 50)if not portfolio_returns.empty:   fig, axes = plt.subplots(2, 2, figsize=(15, 10))   fig.suptitle('Portfolio Analysis Dashboard', fontsize=16)     cumulative_returns = (1 + portfolio_returns).cumprod()   cumulative_returns.plot(ax=axes[0,0], title='Cumulative Returns', alpha=0.7)   axes[0,0].legend(bbox_to_anchor=(1.05, 1), loc='upper left')     rolling_vol = portfolio_returns.rolling(window=30).std() * np.sqrt(252)   rolling_vol.plot(ax=axes[0,1], title='30-Day Rolling Volatility', alpha=0.7)   axes[0,1].legend(bbox_to_anchor=(1.05, 1), loc='upper left')     weighted_returns.hist(bins=50, ax=axes[1,0], alpha=0.7)   axes[1,0].set_title('Portfolio Returns Distribution')   axes[1,0].axvline(weighted_returns.mean(), color='red', linestyle='--', label='Mean')   axes[1,0].legend()     if len(correlation_matrix) > 1:       sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, ax=axes[1,1])       axes[1,1].set_title('Correlation Matrix')     plt.tight_layout()   plt.show()print("\n 8. INVESTMENT SUMMARY & RECOMMENDATIONS")print("-" * 50)print("Portfolio Analysis Complete!")print(f" Analyzed {len(successful_stocks)} stocks")print(f" Calculated {len(sector_performance)} sector performances")print(f" Generated technical indicators and risk metrics")if not portfolio_returns.empty and len(successful_stocks) > 0:   best_performer = portfolio_returns.mean().idxmax()   worst_performer = portfolio_returns.mean().idxmin()   print(f" Best Performer: {best_performer}")   print(f" Worst Performer: {worst_performer}")print("\n Key Insights:")print("• Diversification across tech sectors reduces portfolio risk")print("• Technical indicators help identify entry/exit points")print("• Regular rebalancing maintains target allocations")print("• Monitor correlations to avoid concentration risk")print("\n Next Steps:")print("• Backtest different allocation strategies")print("• Add fundamental analysis metrics")print("• Implement automated alerts for technical signals")print("• Explore ESG and factor-based screening")print("\n" + "="*60)print("OpenBB Advanced Tutorial Complete! ")print("Visit https://openbb.co for more features and documentation")