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Trading_skills technical-analysis

Compute technical indicators like RSI, MACD, Bollinger Bands, SMA, EMA for a stock. Use when user asks about technical analysis, indicators, RSI, MACD, moving averages, overbought/oversold, or chart analysis.

install
source · Clone the upstream repo
git clone https://github.com/staskh/trading_skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/staskh/trading_skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/technical-analysis" ~/.claude/skills/staskh-trading-skills-technical-analysis && rm -rf "$T"
manifest: .claude/skills/technical-analysis/SKILL.md
source content

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