Agent Skills › staskh/trading_skills › technical-analysis

technical-analysis

GitHub

计算RSI、MACD等技术指标及风险指标，支持多股票分析与财报数据。新增相关性分析功能，用于评估资产间价格关联度以辅助分散投资，需使用纽约时区处理时间数据。

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

Trigger Scenarios

用户询问技术指标如RSI、MACD、布林带等请求进行股票或投资组合的相关性分析

Install

npx skills add staskh/trading_skills --skill technical-analysis -g -y

More Options

Non-standard path

npx skills add https://github.com/staskh/trading_skills/tree/main/.claude/skills/technical-analysis -g -y

Use without installing

npx skills use staskh/trading_skills@technical-analysis

指定 Agent (Claude Code)

npx skills add staskh/trading_skills --skill technical-analysis -a claude-code -g -y

安装 repo 全部 skill

npx skills add staskh/trading_skills --all -g -y

预览 repo 内 skill

npx skills add staskh/trading_skills --list

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.

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

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.