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.