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

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/20pct study events.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 NO CLEAR NEWS 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 rule candidates and exported edge hints as research prompts. Require representative chart review, sample size thresholds, and out of sample validation before changing trade rules. Step 5: Historical Backfill Backfill records are marked CURRENT UNIVERSE BACKFILL SURVIVORSHIP BIAS by default. Add survivorship complete only when the supplied OHLCV includes delisted symbols and historical universe coverage. Output Format stockbee 20pct events YYYY MM DD HHMMSS.json — scan metadata and event records stockbee 20pct daily report YYYY MM DD HHMMSS.md — human readable daily 20% study report stockbee 20pct enriched YYYY MM DD HHMMSS.json — enriched event records stockbee 20pct outcome update YYYY MM DD HHMMSS.json/md — matured forward outcome update stockbee 20pct cohort summary YYYY MM DD HHMMSS.json/md — cohort statistics and rule candidates stockbee 20pct edge hints YYYY MM DD HHMMSS.yaml — edge hint export for downstream research skills state/stockbee/20pct study events.jsonl — durable 20% mover model book Resources references/methodology.md — 20% study methodology and review checklist references/event schema.md — JSONL event record schema references/catalyst taxonomy.md — catalyst and risk label definitions references/scoring system.md — event quality and study priority scoring references/cohort mining rules.md — overfitting controls and sample size rules scripts/run 20pct study.py — CLI for scan, enrich, update outcomes, summarize, and backfill

stockbee-20pct-study