Developing An Superior Portfolio Analysis And Market Intelligence Instrument With OpenBB

!pip arrange 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('present.max_columns', None)
pd.set_option('present.width', 1000)


print("🚀 Superior 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 info from {start_date} to {end_date}...")


for i, picture in enumerate(tech_stocks):
   try:
       info = obb.equity.worth.historic(picture=picture, start_date=start_date, end_date=end_date)
       df = info.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(picture)
      
       print(f"✅ {picture}: {len(df)} days of data")
   apart from Exception as e:
       print(f"❌ Error fetching {picture}: {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.suggest() * 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().rising().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📊 Explicit individual Stock Effectivity:")
   for stock in successful_stocks:
       stock_return = portfolio_returns[stock].suggest() * 252
       stock_vol = portfolio_returns[stock].std() * np.sqrt(252)
       print(f"{stock}: Return {stock_return:.2%}, Volatility {stock_vol:.2%}")
else:
   print("❌ No reliable portfolio info accessible for analysis")

print("n🔍 3. ADVANCED TECHNICAL ANALYSIS")
print("-" * 50)


picture="NVDA"
try:
   price_data = obb.equity.worth.historic(picture=picture, start_date=start_date, end_date=end_date)
   df = price_data.to_df()
  
   df['SMA_20'] = df['close'].rolling(window=20).suggest()
   df['SMA_50'] = df['close'].rolling(window=50).suggest()
   df['EMA_12'] = df['close'].ewm(span=12).suggest()
   df['EMA_26'] = df['close'].ewm(span=26).suggest()
  
   df['MACD'] = df['EMA_12'] - df['EMA_26']
   df['MACD_signal'] = df['MACD'].ewm(span=9).suggest()
  
   delta = df['close'].diff()
   obtain = (delta.the place(delta > 0, 0)).rolling(window=14).suggest()
   loss = (-delta.the place(delta  df['MACD_signal'].iloc[-1] else "SELL"
   price_vs_sma20 = "Above" if current_price > df['SMA_20'].iloc[-1] else "Beneath"
  
   print(f"n{picture} Technical Analysis:")
   print(f"Current Worth: ${current_price:.2f}")
   print(f"RSI (14): {current_rsi:.2f} ({'Overbought' if current_rsi > 70 else 'Oversold' if current_rsi 

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.spherical(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 Hazard (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="increased 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="increased 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.suggest(), coloration="pink", linestyle="--", label="Indicate")
   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.current()


print("n🎯 8. INVESTMENT SUMMARY & RECOMMENDATIONS")
print("-" * 50)


print("Portfolio Analysis Full!")
print(f"✅ Analyzed {len(successful_stocks)} shares")
print(f"✅ Calculated {len(sector_performance)} sector performances")
print(f"✅ Generated technical indicators and hazard metrics")


if not portfolio_returns.empty and len(successful_stocks) > 0:
   best_performer = portfolio_returns.suggest().idxmax()
   worst_performer = portfolio_returns.suggest().idxmin()
   print(f"🏆 Biggest Performer: {best_performer}")
   print(f"📉 Worst Performer: {worst_performer}")


print("n💡 Key Insights:")
print("• Diversification all through tech sectors reduces portfolio hazard")
print("• Technical indicators help set up entry/exit components")
print("• Widespread rebalancing maintains purpose allocations")
print("• Monitor correlations to avoid focus hazard")


print("n🔧 Subsequent Steps:")
print("• Backtest fully totally different allocation strategies")
print("• Add elementary analysis metrics")
print("• Implement automated alerts for technical alerts")
print("• Uncover ESG and factor-based screening")


print("n" + "="*60)
print("OpenBB Superior Tutorial Full! 🎉")
print("Go to https://openbb.co for additional choices and documentation")