Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/tradermonty/claude-trading-skills --skill stockbee-20pct-studyAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
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 recordsstockbee_20pct_daily_report_YYYY-MM-DD_HHMMSS.md— human-readable daily 20% study reportstockbee_20pct_enriched_YYYY-MM-DD_HHMMSS.json— enriched event recordsstockbee_20pct_outcome_update_YYYY-MM-DD_HHMMSS.json/md— matured forward outcome updatestockbee_20pct_cohort_summary_YYYY-MM-DD_HHMMSS.json/md— cohort statistics and rule candidatesstockbee_20pct_edge_hints_YYYY-MM-DD_HHMMSS.yaml— edge-hint export for downstream research skillsstate/stockbee/20pct_study_events.jsonl— durable 20% mover model book
Resources
references/methodology.md— 20% study methodology and review checklistreferences/event_schema.md— JSONL event record schemareferences/catalyst_taxonomy.md— catalyst and risk label definitionsreferences/scoring_system.md— event quality and study priority scoringreferences/cohort_mining_rules.md— overfitting controls and sample-size rulesscripts/run_20pct_study.py— CLI for scan, enrich, update-outcomes, summarize, and backfill
