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staskhstaskh/trading_skills/.claude/skills/technical-analysis/SKILL.md

technical-analysis

Skill405 starsChanged 11 days agoMITView on GitHub

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.

11 other projects use a version of this.

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Projects with it also use fundamental-analysis and stock-screener.

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The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 7 edited versions we read keep these.

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    Distinctive

    This author's own choices. Worth a look.

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  1. Very specific:

    All timestamps and time-based calculations must use the America/New_York timezone.

    line 126

“Common” is measured: kept by at least 6 in 10 of the 7 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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Files
SKILL.md

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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