# Stockbee 20pct Study

Build and maintain a Stockbee-style daily 20% mover study for US equities by scanning...

- Repository: https://github.com/tradermonty/claude-trading-skills (3536 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add tradermonty/claude-trading-skills --skill stockbee-20pct-study -g -a claude-code -y`
Codex: `npx skills add tradermonty/claude-trading-skills --skill stockbee-20pct-study -g -a codex -y`
Cursor: `npx skills add tradermonty/claude-trading-skills --skill stockbee-20pct-study -g -a cursor -y`
Gemini CLI: `npx skills add tradermonty/claude-trading-skills --skill stockbee-20pct-study -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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.

```bash
python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py scan \
  --fmp-universe \
  --max-symbols 300 \
  --as-of 2026-06-28 \
  --lookback-days 5 \
  --min-abs-return-pct 20 \
  --min-price 5 \
  --min-dollar-volume 20000000 \
  --include-down-movers \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --output-dir reports/
```

Use offline data instead of FMP:

The rest: https://github.com/tradermonty/claude-trading-skills/blob/HEAD/skills/stockbee-20pct-study/SKILL.md