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.
!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(" 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.
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")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.
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)}")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.
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")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.
Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.
