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