Como usar
- Copie o comando e verifique o pacote antes de instalar no seu ambiente de agente.
- Consulte no original as ferramentas, credenciais e ambientes necessários.
- Descreva os dados de entrada e a entrega esperada; confira a saída com as fontes.
npx skills add https://github.com/tradermonty/claude-trading-skills --skill stockbee-setup-fluency-trainerAntes de começar
- A instalação não inclui assinatura de modelo, franquia API ou acesso aos seus arquivos.
- Estrelas e instalações indicam atividade, não um teste independente de qualidade ou segurança.
Documento original do Skill
Renderizado do repositório de origem, preservando títulos, exemplos, código, tabelas, links e imagens.
Stockbee Setup Fluency Trainer
Build and maintain a model book for Stockbee-style Momentum Burst setups. This skill turns daily screener candidates into structured study records, updates them after the 3-day and 5-day windows mature, and summarizes which setup features are working or failing.
When to Use
- User wants to study Stockbee Momentum Burst setups systematically
- User asks to build a model book from
stockbee-momentum-burst-screeneroutput - User wants to review failed candidates, missed trades, or A/B setup quality
- User wants 3-day / 5-day forward returns, MFE, MAE, and stop-hit outcomes
- User wants to improve setup recognition before increasing position size
- User asks which Stockbee tags should be promoted, downgraded, or filtered
Prerequisites
- Python 3.10+
- A
stockbee-momentum-burst-screenerJSON report, or compatible candidate JSON - Optional: FMP API key for outcome updates when offline OHLCV JSON is not supplied
- Recommended local state path:
state/stockbee/model_book.jsonl
Workflow
Step 1: Ingest Momentum Burst Candidates
Run after the Stockbee Momentum Burst screener has produced a JSON report.
bash
python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py ingest \
--screener-json reports/stockbee_momentum_burst_YYYY-MM-DD_HHMMSS.json \
--model-book state/stockbee/model_book.jsonl \
--output-dir reports/Use --include-rejects when intentionally building a negative-example set. Otherwise rejected candidates are skipped.
Step 2: Update 3-Day and 5-Day Outcomes
Use FMP:
bash
python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py update \
--model-book state/stockbee/model_book.jsonl \
--horizons 3,5 \
--output-dir reports/Use offline OHLCV JSON:
bash
python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py update \
--model-book state/stockbee/model_book.jsonl \
--prices-json data/daily_ohlcv.json \
--horizons 3,5 \
--output-dir reports/The update step records:
- Forward close return for each horizon
- MFE and MAE over each horizon
- Stop-hit status and first stop-hit date
- Outcome tags such as
STRONG_WINNER,WORKED,FAILED_STOP,FAILED_FADE,CHOPPY_FAILURE, orNEUTRAL
Step 3: Summarize Cohorts
bash
python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py summarize \
--model-book state/stockbee/model_book.jsonl \
--group-by rating,primary_trigger,setup_tags \
--min-sample 5 \
--output-dir reports/Review the generated Markdown and JSON reports. Treat rule_candidates as evidence prompts, not automatic rule changes.
Step 4: Convert Evidence Into Practice
For cohorts with enough examples:
- Promote tags with high win rate, positive 5-day expectancy, and acceptable average MAE
- Downgrade or filter tags with weak 5-day expectancy, frequent stop hits, or repeated fade failures
- Inspect representative charts manually before changing trade rules
- Log accepted lessons in
trader-memory-coreor the monthly review process
Model Book Fields
Each JSONL record includes:
record_id,symbol,setup_date,primary_triggerrating,setup_score,setup_tagsentry_reference,stop_reference,risk_pct_to_stophuman_label,human_decision,human_notesoutcomes.3dandoutcomes.5doverall_outcome,matured,raw_candidate
Interpretation Rules
STRONG_WINNER: 5-day close return >= 8% or MFE >= 12%, with no stop hitWORKED: 5-day close return >= 4% or MFE >= 6%, with no stop hitFAILED_STOP: Stop was touched within the horizonFAILED_FADE: Forward return <= -2% without a recorded stop hitCHOPPY_FAILURE: Adverse excursion was large and forward progress was poorNEUTRAL: No decisive follow-through or failurePENDING: Not enough future bars yet
Output
state/stockbee/model_book.jsonl- Durable setup model bookstockbee_setup_fluency_ingest_YYYY-MM-DD_HHMMSS.json/mdstockbee_setup_fluency_update_YYYY-MM-DD_HHMMSS.json/mdstockbee_setup_fluency_summary_YYYY-MM-DD_HHMMSS.json/md
Resources
references/model_book_schema.md- JSONL schema and lifecycle statesreferences/outcome_tags.md- Outcome classification and tag definitionsreferences/review_workflow.md- Daily, 3-day, 5-day, and monthly review routine
