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