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Technical Analysis Agent Skill

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.

2k tokens
context cost
the whole folder, loaded on every use
3
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
308
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/staskh/trading_skills --skill technical-analysis

The instruction itself

15 sections, as written by the author

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

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.

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How to use it

Copy the folder

Take staskh/technical-analysis from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.