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

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