MCPInstall

technical-analysis

by staskhBusiness & Finance

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.

Installs
587
Last 24h
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Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/staskh/trading_skills --skill technical-analysis
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads technical-analysis automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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.

FAQ

What does the technical-analysis skill do?

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.

How do I install technical-analysis?

Run npx skills add https://github.com/staskh/trading_skills --skill technical-analysis in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/technical-analysis/ (personal) or .claude/skills/technical-analysis/ (project).

Which AI agents can use technical-analysis?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is technical-analysis safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains technical-analysis?

It is published by staskh in staskh/trading_skills. It has 587 recorded installs.

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