Build a Stockbee-style setup model book from momentum-burst screener candidates, then update 3-day and 5-day forward outcomes with MFE/MAE, stop-hit status, outcome tags, and cohort statistics. Use when the user wants to study Stockbee Momentum Burst examples, track failed candidates, build setup fluency, review A/B setup quality, or convert screener outputs into a learning loop rather than immediate trade signals.
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Build a Stockbee-style setup model book from momentum-burst screener candidates, then update 3-day and 5-day forward outcomes with MFE/MAE, stop-hit status, outcome tags, and cohort statistics. Use when the user wants to study Stockbee Momentum Burst examples, track failed candidates, build setup fluency, review A/B setup quality, or convert screener outputs into a learning loop rather than immediate trade signals.
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.
stockbee-momentum-burst-screener outputVersion history
stockbee-momentum-burst-screener JSON report, or compatible candidate JSONstate/stockbee/model_book.jsonlRun after the Stockbee Momentum Burst screener has produced a JSON report.
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.
Use FMP:
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:
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:
STRONG_WINNER, WORKED, FAILED_STOP, FAILED_FADE, CHOPPY_FAILURE, or NEUTRALpython3 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.
For cohorts with enough examples:
trader-memory-core or the monthly review processEach 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.3d and outcomes.5doverall_outcome, matured, raw_candidateSTRONG_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 yetstate/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/mdreferences/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 routineIn these kits
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