/technical-analysis

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.

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

# 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

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Correlation Analysis

Compute price correlation matrix between multiple symbols for diversification analysis.

Instructions

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

# 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.