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.
npx skills add BaggaT236/AI-Trading-Skills --skill stockbee-20pct-study --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 artifac
- When to Use
- Prerequisites
- Workflow
- Step 1: Scan for 20% Movers
- Step 2: Enrich and Classify Events
- Step 3: Update Matured Forward Outcomes
- Step 4: Summarize Cohorts
- Step 5: Historical Backfill
- Output Format
- Resources
python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py scan \ python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py enrich \ python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py update-outcomes \ python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py summarize \ python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py backfill \
What does the stockbee-20pct-study skill do?
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.
How do I install it?
Run `npx skills add BaggaT236/AI-Trading-Skills --skill stockbee-20pct-study --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.