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Compare performance of multiple stocks side-by-side. Use when the user asks to "compare", "contrast", "which is better", or analyze "multiple stocks" together. Calculates returns, volatility, and correlations.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
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| name | comparative-analysis |
| description | Compare performance of multiple stocks side-by-side. Use when the user asks to "compare", "contrast", "which is better", or analyze "multiple stocks" together. Calculates returns, volatility, and correlations. |
This skill compares the performance of multiple stocks over the same time period, providing side-by-side metrics to help with investment decisions.
Use this skill when the user:
Extract all ticker symbols from the user's request:
Use the same period for all stocks to ensure fair comparison:
The helper script compare_stocks.py handles multi-stock comparison:
uv run python .claude/skills/comparative-analysis/compare_stocks.py \
--tickers AAPL MSFT GOOGL \
--period 6mo \
--output tmp/comparison_results.json
Arguments:
--tickers (required): Space-separated list of ticker symbols--period (optional): Time period (1mo, 3mo, 6mo, 1y, 2y, 5y). Default: 6mo--output (required): Output file path (use absolute path in tmp/)--benchmark (optional): Benchmark ticker for comparison (e.g., SPY for S&P 500)After running the comparison, interpret the results:
The helper script returns JSON with this structure:
{
"comparison_date": "2026-02-03",
"period": "6mo",
"date_range": {
"start": "2025-08-03",
"end": "2026-02-03"
},
"stocks": [
{
"ticker": "AAPL",
"start_price": 219.57,
"end_price": 270.01,
"total_return_pct": 22.97,
"annualized_return_pct": 45.02,
"volatility_pct": 21.46,
"sharpe_ratio": 2.10,
"max_drawdown_pct": -8.54
},
{
"ticker": "MSFT",
"start_price": 519.01,
"end_price": 423.37,
"total_return_pct": -18.43,
"annualized_return_pct": -39.25,
"volatility_pct": 23.39,
"sharpe_ratio": -1.68,
"max_drawdown_pct": -22.12
}
],
"correlations": {
"AAPL_MSFT": 0.085,
"AAPL_GOOGL": 0.421,
"MSFT_GOOGL": 0.060
},
"rankings": {
"best_return": "AAPL",
"worst_return": "MSFT",
"most_volatile": "MSFT",
"least_volatile": "AAPL",
"best_risk_adjusted": "AAPL"
}
}
2.0: Excellent
User: "Compare Tesla, Ford, and GM stock performance over the last year"
Agent Workflow:
uv run python .claude/skills/comparative-analysis/compare_stocks.py --tickers TSLA F GM --period 1y --output tmp/ev_comparison.jsonuv run python to execute (not plain python)