tools-only OpenBB Equity Analysis
Perform comprehensive stock analysis using the OpenBB Platform.
Usage
/openbb-equity TICKER [--analysis fundamental|technical|all] [--period 1y]
What This Command Does
Retrieves and analyzes equity data for any stock ticker using OpenBB's comprehensive data sources.
Workflow
1. Check OpenBB Installation
First, verify OpenBB is installed:
try:
from openbb import obb
print("✅ OpenBB installed")
except ImportError:
print("⚠️ Installing OpenBB...")
import subprocess
subprocess.run(["pip", "install", "openbb"], check=True)
from openbb import obb
2. Parse Arguments
# Parse user input
import sys
ticker = sys.argv[1].upper() if len(sys.argv) > 1 else "AAPL"
analysis_type = "all" # fundamental, technical, or all
period = "1y" # 1d, 1w, 1m, 3m, 6m, 1y, 5y
# Parse flags
for arg in sys.argv[2:]:
if arg.startswith("--analysis="):
analysis_type = arg.split("=")[1]
elif arg.startswith("--period="):
period = arg.split("=")[1]
3. Retrieve Historical Price Data
# Get historical prices
price_data = obb.equity.price.historical(
symbol=ticker,
interval="1d",
period=period
)
df = price_data.to_dataframe()
print(f"\n📈 Historical Prices for {ticker}")
print(f"Period: {period}")
print(f"Latest Close: ${df['close'].iloc[-1]:.2f}")
print(f"52-Week High: ${df['high'].max():.2f}")
print(f"52-Week Low: ${df['low'].min():.2f}")
print(f"YTD Return: {((df['close'].iloc[-1] / df['close'].iloc[0]) - 1) * 100:.2f}%")
4. Fundamental Analysis (if requested)
if analysis_type in ["fundamental", "all"]:
print(f"\n📊 Fundamental Analysis for {ticker}")
# Company profile
try:
profile = obb.equity.profile(symbol=ticker)
print(f"\nCompany: {profile.name}")
print(f"Sector: {profile.sector}")
print(f"Industry: {profile.industry}")
print(f"Market Cap: ${profile.market_cap / 1e9:.2f}B")
except:
print("Profile data not available")
# Financial metrics
try:
metrics = obb.equity.fundamental.metrics(symbol=ticker)
print(f"\nKey Metrics:")
print(f"P/E Ratio: {metrics.pe_ratio:.2f}")
print(f"EPS: ${metrics.eps:.2f}")
print(f"Dividend Yield: {metrics.dividend_yield:.2%}")
print(f"ROE: {metrics.roe:.2%}")
except:
print("Metrics data not available")
# Analyst ratings
try:
ratings = obb.equity.estimates.analyst(symbol=ticker)
print(f"\nAnalyst Consensus:")
print(f"Buy: {ratings.buy_count}")
print(f"Hold: {ratings.hold_count}")
print(f"Sell: {ratings.sell_count}")
print(f"Target Price: ${ratings.target_price:.2f}")
except:
print("Analyst ratings not available")
5. Technical Analysis (if requested)
if analysis_type in ["technical", "all"]:
print(f"\n📉 Technical Analysis for {ticker}")
# Calculate technical indicators
import pandas as pd
# Simple Moving Averages
df['SMA_20'] = df['close'].rolling(window=20).mean()
df['SMA_50'] = df['close'].rolling(window=50).mean()
df['SMA_200'] = df['close'].rolling(window=200).mean()
current_price = df['close'].iloc[-1]
sma_20 = df['SMA_20'].iloc[-1]
sma_50 = df['SMA_50'].iloc[-1]
sma_200 = df['SMA_200'].iloc[-1]
print(f"\nMoving Averages:")
print(f"Current Price: ${current_price:.2f}")
print(f"SMA 20: ${sma_20:.2f} {'🟢' if current_price > sma_20 else '🔴'}")
print(f"SMA 50: ${sma_50:.2f} {'🟢' if current_price > sma_50 else '🔴'}")
print(f"SMA 200: ${sma_200:.2f} {'🟢' if current_price > sma_200 else '🔴'}")
# RSI calculation
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))
rsi = df['RSI'].iloc[-1]
print(f"\nRSI (14): {rsi:.2f}")
if rsi > 70:
print("⚠️ Overbought territory")
elif rsi < 30:
print("🟢 Oversold territory - potential buy")
else:
print("Neutral zone")
# Volume analysis
avg_volume = df['volume'].rolling(window=20).mean().iloc[-1]
current_volume = df['volume'].iloc[-1]
print(f"\nVolume:")
print(f"Current: {current_volume:,.0f}")
print(f"20-day Avg: {avg_volume:,.0f}")
print(f"Relative: {(current_volume / avg_volume):.2f}x")
6. AI-Powered Insights
Generate investment insights using Claude's analysis:
# Prepare summary for AI analysis
summary = {
"ticker": ticker,
"current_price": current_price,
"52w_high": df['high'].max(),
"52w_low": df['low'].min(),
"ytd_return": ((df['close'].iloc[-1] / df['close'].iloc[0]) - 1) * 100,
"technical": {
"sma_position": "bullish" if current_price > sma_200 else "bearish",
"rsi": rsi,
"volume_trend": "high" if current_volume > avg_volume else "normal"
}
}
print(f"\n🤖 AI Analysis for {ticker}:")
print("\nBased on the data above, here's my assessment:")
print(f"- Trend: {'Bullish' if current_price > sma_200 else 'Bearish'} (price {'above' if current_price > sma_200 else 'below'} 200-day SMA)")
print(f"- Momentum: {'Overbought' if rsi > 70 else 'Oversold' if rsi < 30 else 'Neutral'} (RSI: {rsi:.1f})")
print(f"- Volume: {'Elevated' if current_volume > avg_volume * 1.5 else 'Normal'} trading activity")
print(f"\n💡 Recommendation: Consider {summary} in context of your investment strategy and risk tolerance.")
7. Generate Report
Create a formatted analysis report:
print(f"\n{'='*60}")
print(f"EQUITY ANALYSIS REPORT: {ticker}")
print(f"{'='*60}")
print(f"Generated: {pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')}")
print(f"Data Source: OpenBB Platform")
print(f"\nAnalysis Type: {analysis_type.upper()}")
print(f"Period Analyzed: {period}")
print(f"\n{'='*60}")
Examples
Basic equity analysis
/openbb-equity AAPL
Fundamental analysis only
/openbb-equity TSLA --analysis=fundamental
Technical analysis with custom period
/openbb-equity NVDA --analysis=technical --period=6m
Complete analysis
/openbb-equity GOOGL --analysis=all --period=1y
Data Coverage
- Price Data: Historical OHLCV, real-time quotes
- Fundamentals: Income statements, balance sheets, cash flow, ratios
- Technical: SMA, EMA, RSI, MACD, Bollinger Bands, volume
- Analyst Data: Ratings, price targets, recommendations
- Insider Trading: Recent insider transactions
- News: Latest company news and sentiment
Tips
- Compare Multiple Stocks: Run for different tickers to compare
- Track Over Time: Save reports to monitor changes
- Combine with AI Agents: Use with equity-analyst agent for deeper insights
- Export Data: Save dataframes to CSV for further analysis
- Set Alerts: Monitor key technical levels (support/resistance)
Integration with Other Commands
# Compare with crypto
/openbb-crypto BTC --compare=equity
# Portfolio context
/openbb-portfolio --add=AAPL
# Macro correlation
/openbb-macro --impact=equity
Requirements
- OpenBB Platform installed (
pip install openbb) - Python 3.9.21 - 3.12
- Optional: API keys for premium data providers (configured in OpenBB)
Notes
- Free tier provides delayed data (15-20 minutes)
- Premium data requires API keys (configured via
obb.user.credentials) - All financial data is for informational purposes only
- Not financial advice - always do your own research