Technical Analysis

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

• 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

  )

• 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

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