AgentSkillsCN

technical-analysis

计算某只股票的技术指标，如RSI、MACD、布林带、SMA、EMA。当用户询问技术分析、指标、RSI、MACD、移动平均线、超买/超卖，或进行图表分析时使用。

SKILL.md
--- frontmatter
name: technical-analysis
description: 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.
dependencies: ["trading-skills"]

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.

bash
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

bash
# 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

bash
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

bash
# 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