Stock Liquidity AnalysisFinance Skill

A stock liquidity analysis skill using yfinance, pandas, and numpy. 3-step process: (1) ensure dependencies (auto-install yfinance/pandas/numpy via pip if missing); (2) route to correct sub-skill via intent classification table; (3) present results with caveats. Six sub-skills: A — Liquidity Dashboard: comprehensive snapshot combining all key metrics (spread, volume, turnover, Amihud illiquidity, market impact estimate for 1% ADV), liquidity grade (Very High/High/Moderate/Low/Very Low based on dollar volume, spread %, Amihud ×10⁹), multi-ticker comparison; B — Spread Analysis: current quoted spread (absolute, relative %, bps), bid/ask sizes, near-the-money options spreads for context; C — Volume Analysis: ADV, median volume, dollar volume, RVOL (today vs average, >1.5 elevated, <0.5 quiet), volume trend (linear regression slope), day-of-week pattern, top 5 high/low volume days; D — Order Book Depth: top-of-book (bid/ask/sizes), intraday volume distribution (5-min bars over 5 days, grouped by time-of-day), options OI as derivatives depth proxy, honest limitation about Yahoo Finance providing top-of-book only (no Level 2); E — Market Impact: square-root model Impact = σ × √(Q/V) where σ = daily volatility, Q = order shares, V = ADV, impact curve for 0.1%-50% of ADV, flag if impact > 50 bps suggesting algorithmic execution; F — Turnover Ratio: daily/annualized turnover vs outstanding and float, days to trade float, turnover trend, interpretation table (>500% extremely active, <30% thinly traded). Defaults: 3mo lookback, daily interval, square-root impact model, 5m intrayday when needed. Includes reference file with detailed formulas, extended code templates, metric interpretation guides, and academic references. Uses yfinance (required), pandas, numpy. Python 3.8+. Works on all platforms.

Snapshot quality

88/100

Snapshot risk

Medium

Audit conclusion

Needs review