Scan US large-cap option chains for volume-to-open-interest anomalies.
npx skills add https://github.com/mthli/skills --skill unusual-options-scan
Please help me install this Agent Skill. Skill: unusual-options-scan Source: https://github.com/mthli/skills/tree/main/unusual-options-scan Command: npx skills add https://github.com/mthli/skills --skill unusual-options-scan
Unusual-options-scan turns daily end-of-day options chain data into a focused watchlist of US large-cap tickers showing anomalous options activity, helping you identify where smart-money positioning may be building ahead of catalysts.
Use the unusual-options-scan skill to scan US large caps after the close and return the top anomalous tickers with cross-day OI confirmation.
High-intent search queries and answers about installing and using this skill.
You can find unusual options activity in large-cap stocks by scanning end-of-day option chains for volume-to-open-interest spikes, far-OTM short-dated accumulation, and extreme call/put skew. This identifies where institutional positioning may be building ahead of catalysts.
Volume-to-open-interest ratio detects smart money positioning by flagging contracts where daily volume significantly exceeds existing open interest. High ratios indicate new positioning rather than existing trades, especially when notional size is outsized versus the stock's average dollar ADV.
Yes, you can use yfinance to fetch equities and option chains for US large-cap tickers. The scan computes derived metrics including Vol/OI, notional value, distance from spot, and call/put ratio to identify contract-level anomalies.
You confirm persistence by comparing daily markdown snapshots in state history to measure overnight open-interest change. This cross-day confirmation categorizes prior flags into strong OI growth, partial retention, or closed-out noise.
End-of-day option chain snapshots limit equity screening to post-close analysis, missing intraday flow. They also require Python execution and yfinance data availability, meaning real-time catalyst detection or pre-market positioning is not supported.
Yes, pandas and numpy are required dependencies to compute derived metrics like notional value, call/put skew, and notional-to-ADV ratios. These libraries handle the quantitative processing of option chain data fetched via yfinance.
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