stockbee-20pct-study

Q: How do I install stockbee-20pct-study?

Run npx skills add https://github.com/tradermonty/claude-trading-skills --skill stockbee-20pct-study in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/stockbee-20pct-study/ (personal) or .claude/skills/stockbee-20pct-study/ (project).

by tradermontyBusiness & Finance

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.

Installs: 2.6K
Last 24h: +19
Trending rank: #6102
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/tradermonty/claude-trading-skills --skill stockbee-20pct-study

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads stockbee-20pct-study automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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.

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:

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py scan \
  --prices-json data/us_daily_ohlcv.json \
  --as-of 2026-06-28 \
  --lookback-days 5 \
  --include-down-movers \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --output-dir reports/

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.

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py enrich \
  --events-json reports/stockbee_20pct_events_YYYY-MM-DD_HHMMSS.json \
  --news-json data/catalysts_YYYY-MM-DD.json \
  --market-regime reports/market_regime_latest.json \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --output-dir reports/

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.

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py update-outcomes \
  --prices-json data/us_daily_ohlcv.json \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --horizons 1,3,5,10,20 \
  --output-dir reports/

The update records close return, MFE, MAE, direction-adjusted continuation return, and outcome tags.

Step 4: Summarize Cohorts

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py summarize \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --group-by direction,catalyst.label,technical_context.pattern_label,technical_context.close_quality \
  --min-sample 10 \
  --output-dir reports/

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

python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py backfill \
  --from 2020-01-01 \
  --to 2026-06-28 \
  --prices-json data/us_daily_ohlcv.json \
  --min-abs-return-pct 20 \
  --include-down-movers \
  --state-file state/stockbee/20pct_study_events.jsonl \
  --output-dir reports/

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

Shared FMP client

Live FMP access uses the bundled generated scripts/fmp_client.py, loaded only when an FMP run starts, so offline modes still work without requests. CLI key/environment precedence, the API-call budget and successful output shapes are preserved. Requests use stable endpoints only; a denied, empty or failed stable request does not fall back to retired v3 URLs. The shared client masks raw and URL-encoded key echoes, retries malformed JSON once, and retries a 429 at most once, after a 60-second wait, before stopping on a repeated rate limit.

FAQ

What does the stockbee-20pct-study skill do?

How do I install stockbee-20pct-study?

Run npx skills add https://github.com/tradermonty/claude-trading-skills --skill stockbee-20pct-study in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/stockbee-20pct-study/ (personal) or .claude/skills/stockbee-20pct-study/ (project).

Which AI agents can use stockbee-20pct-study?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is stockbee-20pct-study safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains stockbee-20pct-study?

It is published by tradermonty in tradermonty/claude-trading-skills. It has 2,625 recorded installs.