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.

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. 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 Correlation Analysis Compute price correlation matrix between multiple symbols for diversification analysis. Instructions 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 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.

technical-analysis