Agent skill · research science · tradermonty
stockbee-20pct-study
Build and maintain a Stockbee-style daily 20% mover study for US equities by scanning +20%/-20% movers, classifying catalysts and setup context, updating forward outcomes, and summarizing cohort patterns. Use when the user asks to run a daily 20% study, backfill historical 20% movers, find recurring edge patterns, or build a model book of explosive market moves.
Why this skill is useful
Adds specific scripts for scanning, enriching, and summarizing stock movements that are not commonly available in public resources.
What it needs
Requires python3 installed locally. Requires fmp account access. About 3k tokens when loaded. Last updated 2026-08-06. 2,581 stars on the source repository.
What this skill does
Stockbee 20% Study Build a daily event study of US equities that moved +20% or -20% over a defined window. Convert large movers into structured study records, classify the catalyst and chart context, update forward outcomes, and summarize recurring patterns for research. This skill is a research, model-book, and setup-fluency workflow. It does not generate buy/sell signals, place orders, or output broker execution instructions. When to Use User wants to run a Stockbee-style daily 20% mover study User asks which stocks moved +20% or -20% today, this week, or over a configurable lookback window User wants to backfill historical 20% movers and study what happened next User wants to identify continuation, reversal, exhaustion, or theme-cluster patterns User wants to build a model book of explosive winners, major failures, and failed low-quality pops User wants edge hints for downstream strategy research rather than immediate trade signals Prerequisites Python 3.9+ FMP API key for live US universe scans, or offline OHLCV JSON via --prices-json Optional structured news/catalyst JSON for higher-quality catalyst classification Recommended market regime artifact from market-regime-daily Recommended local state path: state/stockbee/20pctstudyevents.jsonl Workflow Step 1: Scan for 20% Movers Run after the US market close, or against the latest complete daily bar in an offline OHLCV file. Use offline data instead of FMP: Step 2: Enrich and Classify Events Use structured catalyst data when available. The enrichment step is best-effort: if no news record is found, the event remains a price-only NOCLEARNEWS study record. Step 3: Update Matured Forward Outcomes Update 1-day, 3-day, 5-day, 10-day, and 20-day forward outcomes after enough future bars exist. The update records close return, MFE, MAE, direction-adjusted continuation return, and outcome tags. Step 4: Summarize Cohorts Treat rulecandidates and exported edge hints as research prompts. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills tradermonty/stockbee-20pct-study