Building an Advanced Portfolio Analysis and Market Intelligence Tool with OpenBB

In this tutorial, we dive deep into the advanced capabilities of OpenBB to perform comprehensive portfolio analysis and market intelligence. We start by constructing a tech-focused portfolio, fetching historical market data, and computing key performance metrics. We then explore advanced technical indicators, sector-level performance, market sentiment, and correlation-based risk analysis. Along the way, we integrate visualizations and insights to make the analysis more intuitive and actionable, ensuring that we cover both the quantitative and qualitative aspects of investment decision-making. Check out the Full Codes here.

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!pip install openbb[all] --quiet


import warnings
warnings.filterwarnings('ignore')


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta
import openbb


from openbb import obb


pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)


print("<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/1f680.png" alt="🚀" class="wp-smiley" /> Advanced OpenBB Financial Analysis Tutorial")
print("=" * 60)

We begin by installing and importing OpenBB along with essential Python libraries for data analysis and visualization. We configure our environment to suppress warnings, set display options for pandas, and get ready to perform advanced financial analysis. Check out the Full Codes here.

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print("n<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/1f4ca.png" alt="📊" class="wp-smiley" /> 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"<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/2705.png" alt="✅" class="wp-smiley" /> {symbol}: {len(df)} days of data")
   except Exception as e:
       print(f"<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/274c.png" alt="❌" class="wp-smiley" /> 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<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/1f4cb.png" alt="📋" class="wp-smiley" /> Portfolio composition (normalized weights):")
   for stock, weight in zip(successful_stocks, portfolio_weights):
       print(f"  {stock}: {weight:.1%}")
else:
   portfolio_weights = []


print("n<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/1f4c8.png" alt="📈" class="wp-smiley" /> 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<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/1f4ca.png" alt="📊" class="wp-smiley" /> 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("<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/274c.png" alt="❌" class="wp-smiley" /> No valid portfolio data available for analysis")

We build a tech portfolio, fetch a year of prices with OpenBB, compute normalized weights and daily returns, then evaluate performance, annual return, volatility, Sharpe, max drawdown, and review stock-wise stats in real time. Check out the Full Codes here.

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print("n<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/1f50d.png" alt="🔍" class="wp-smiley" /> 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<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/1f3ed.png" alt="🏭" class="wp-smiley" /> 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"<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/274c.png" alt="❌" class="wp-smiley" /> 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<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/1f4f0.png" alt="📰" class="wp-smiley" /> 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)}")

We run advanced technical analysis on NVDA, calculating SMAs, EMAs, MACD, RSI, and Bollinger Bands, to gauge momentum and potential entry/exit signals. We then screen sectors by annualized returns across Technology, EVs, and Semiconductors, and we pull fresh company headlines to fold market sentiment into our thesis. Check out the Full Codes here.

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print("n<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/26a0.png" alt="⚠" class="wp-smiley" />  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<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/1f4ca.png" alt="📊" class="wp-smiley" /> 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<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/1f3af.png" alt="🎯" class="wp-smiley" /> 8. INVESTMENT SUMMARY & RECOMMENDATIONS")
print("-" * 50)


print("Portfolio Analysis Complete!")
print(f"<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/2705.png" alt="✅" class="wp-smiley" /> Analyzed {len(successful_stocks)} stocks")
print(f"<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/2705.png" alt="✅" class="wp-smiley" /> Calculated {len(sector_performance)} sector performances")
print(f"<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/2705.png" alt="✅" class="wp-smiley" /> 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"<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/1f3c6.png" alt="🏆" class="wp-smiley" /> Best Performer: {best_performer}")
   print(f"<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/1f4c9.png" alt="📉" class="wp-smiley" /> Worst Performer: {worst_performer}")


print("n<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/1f4a1.png" alt="💡" class="wp-smiley" /> 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<img src="https://s.w.org/images/core/emoji/16.0.1/72x72/1f527.png" alt="🔧" class="wp-smiley" /> 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! <img src="https://s.w.org/images/core/emoji/16.0.1/72x72/1f389.png" alt="🎉" class="wp-smiley" />")
print("Visit https://openbb.co for more features and documentation")

We quantify portfolio risk via correlations and annualized volatility, visualize performance with cumulative returns, rolling volatility, return distribution, and a correlation heatmap, then conclude with best/worst performers, key insights (diversification, signals, rebalancing), and concrete next steps like backtesting, adding fundamentals, alerts, and ESG screening.

In conclusion, we have successfully leveraged OpenBB to build, analyze, and visualize a diversified portfolio while extracting sector insights, technical signals, and risk metrics. We see how combining performance statistics with market sentiment and advanced visualizations empowers us to make informed investment decisions. This approach allows us to continuously monitor and refine our strategies, ensuring that we remain agile in changing market conditions and confident in the data-driven choices we make.

Check out the Full Codes here. Feel free to check out our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter.

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