---
name: technical-analysis
description: Compute technical indicators like RSI, MACD, Bollinger Bands, SMA, EMA for a stock. Use when user asks about technical analysis, indicators, RSI, MACD, moving averages, overbought/oversold, or chart analysis.
dependencies: ["trading-skills"]
---
# Technical Analysis
Compute technical indicators using pandas-ta. Supports multi-symbol analysis and earnings data.
## Instructions
> **Note:** If `uv` is not installed or `pyproject.toml` is not found, replace `uv run python` with `python` in all commands below.
```bash
uv run python scripts/technicals.py SYMBOL [--period PERIOD] [--indicators INDICATORS] [--earnings]
```
## Arguments
- `SYMBOL` - Ticker symbol or comma-separated list (e.g., `AAPL` or `AAPL,MSFT,GOOGL`)
- `--period` - Historical period: 1mo, 3mo, 6mo, 1y (default: 3mo)
- `--indicators` - Comma-separated list: rsi,macd,bb,sma,ema,atr,adx (default: all)
- `--earnings` - Include earnings data (upcoming date + history)
## Output
Single symbol returns:
- `price` - Current price and recent change
- `indicators` - Computed values for each indicator
- `risk_metrics` - Volatility (annualized %) and Sharpe ratio
- `signals` - Buy/sell signals based on indicator levels
- `earnings` - Upcoming date and EPS history (if `--earnings`)
Multiple symbols returns:
- `results` - Array of individual symbol results
### Crossovers
- `indicators.macd.crossover` - Most recent MACD line/signal crossover, or `null`:
- `direction` - `"up"` (MACD crossed above signal = bullish) or `"down"` (crossed below = bearish)
- `days_ago` - Trading bars since the crossover (0 = happened on the most recent bar)
- `indicators.ema.crossover` - Most recent EMA9/EMA21 crossover (same shape; `null` if none).
`indicators.ema` also reports `ema9` and `ema21` alongside `ema12`/`ema26`.
## Interpretation
- RSI > 70 = overbought, RSI < 30 = oversold
- MACD crossover = momentum shift; `crossover.days_ago` of 0-5 = fresh signal
- EMA9/21 crossover confirms short-term momentum; MACD typically leads, EMA confirms
- Price near Bollinger Band = potential reversal
- Golden cross (SMA20 > SMA50) = bullish
- ADX > 25 = strong trend
- Sharpe ratio > 1 = good risk-adjusted returns, > 2 = excellent
- Volatility (annualized) = standard deviation of returns scaled to annual basis
## Examples
```bash
# Single symbol with all indicators
uv run python scripts/technicals.py AAPL
# Multiple symbols
uv run python scripts/technicals.py AAPL,MSFT,GOOGL
# With earnings data
uv run python scripts/technicals.py NVDA --earnings
# Specific indicators only
uv run python scripts/technicals.py TSLA --indicators rsi,macd
```
---
# Correlation Analysis
Compute price correlation matrix between multiple symbols for diversification analysis.
## Instructions
```bash
uv run python scripts/correlation.py SYMBOLS [--period PERIOD]
```
## Arguments
- `SYMBOLS` - Comma-separated ticker symbols (minimum 2)
- `--period` - Historical period: 1mo, 3mo, 6mo, 1y (default: 3mo)
## Output
- `symbols` - List of symbols analyzed
- `period` - Time period used
- `correlation_matrix` - Nested dict with correlation values between all pairs
## Interpretation
- Correlation near 1.0 = highly correlated (move together)
- Correlation near -1.0 = negatively correlated (move opposite)
- Correlation near 0 = uncorrelated (independent movement)
- For diversification, prefer low/negative correlations
## Examples
```bash
# Portfolio correlation
uv run python scripts/correlation.py AAPL,MSFT,GOOGL,AMZN
# Sector comparison
uv run python scripts/correlation.py XLF,XLK,XLE,XLV --period 6mo
# Check hedge effectiveness
uv run python scripts/correlation.py SPY,GLD,TLT
```
## Dependencies
- `numpy`
- `pandas`
- `pandas-ta`
- `yfinance`
## Timezone
All timestamps and time-based calculations must use the `America/New_York` timezone. All JSON output must include `generated_at` (NY time string) and `data_delay` fields.