What indicators are supported?

Supported indicators are rsi, macd, bb (Bollinger Bands), sma, ema, atr, and adx as listed in the skill's arguments.

How should I interpret RSI or MACD signals?

RSI > 70 suggests overbought; RSI < 30 suggests oversold. MACD crossovers indicate momentum shifts; use them with other indicators for confirmation.

How do I include earnings data in the output?

Add the --earnings flag to include upcoming earnings date and EPS history in the output.

technical-analysis

Scanned

npx machina-cli add skill staskh/trading_skills/technical-analysis --openclaw

Files (1)

SKILL.md

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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

Source

git clone https://github.com/staskh/trading_skills/blob/main/.claude/skills/technical-analysis/SKILL.mdView on GitHub

Overview

This skill computes a suite of technical indicators for stocks using pandas-ta, supporting single or multi-symbol analysis. It covers RSI, MACD, Bollinger Bands, SMA, EMA, and more, producing price data, indicators values, risk metrics, and buy/sell signals, with optional earnings data.

How This Skill Works

The tool fetches historical data, applies selected indicators via pandas-ta, and returns a structured result per symbol (price, indicators, risk_metrics, signals, earnings). You can control behavior with --period, --indicators, and --earnings, including multi-symbol analyses.

When to Use It

When you want momentum guidance from RSI or MACD to confirm entry/exit points
When assessing price behavior near Bollinger Bands for potential reversals
When evaluating trend signals such as Golden Cross from SMA20/SMA50
When measuring volatility and risk-adjusted performance with Sharpe ratio and annualized volatility
When you need earnings data alongside indicators for a fuller picture

Quick Start

Step 1: Run uv run python scripts/technicals.py SYMBOL [--period PERIOD] [--indicators INDICATORS] [--earnings]
Step 2: Review the output fields: price, indicators, risk_metrics, signals, earnings (if requested)
Step 3: Use the signals to guide entries/exits and re-run with adjusted indicators or period as needed

Best Practices

Limit indicators with --indicators to reduce noise and focus on where you have edge
Use multiple indicators for confirmation rather than relying on a single signal
Cross-check RSI with MACD and moving averages to validate momentum
Choose an appropriate period (--period) that matches your trading horizon (1mo, 3mo, 6mo, 1y)
If using earnings, compare indicator signals before/after earnings announcements

Example Use Cases

uv run python scripts/technicals.py AAPL
uv run python scripts/technicals.py AAPL,MSFT --indicators rsi,macd
uv run python scripts/technicals.py NVDA --earnings
uv run python scripts/technicals.py TSLA --period 6mo --indicators bb,sma,ema
uv run python scripts/technicals.py AAPL --indicators rsi,bb --earnings

Frequently Asked Questions

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