$27

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

Interpretation

• RSI > 70 = overbought, RSI < 30 = oversold

• MACD crossover = momentum shift

• 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

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.