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stockbee-setup-fluency-trainer

stockbee-setup-fluency-trainer

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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.

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Updated 9/6/2026
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SKILL.md
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stockbee-setup-fluency-trainer
description

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.

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-screener output
  • 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-screener JSON 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.

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:

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:

  • 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, or NEUTRAL

Step 3: Summarize Cohorts

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-core or the monthly review process

Model Book Fields

Each JSONL record includes:

  • record_id, symbol, setup_date, primary_trigger
  • rating, setup_score, setup_tags
  • entry_reference, stop_reference, risk_pct_to_stop
  • human_label, human_decision, human_notes
  • outcomes.3d and outcomes.5d
  • overall_outcome, matured, raw_candidate

Interpretation Rules

  • STRONG_WINNER: 5-day close return >= 8% or MFE >= 12%, with no stop hit
  • WORKED: 5-day close return >= 4% or MFE >= 6%, with no stop hit
  • FAILED_STOP: Stop was touched within the horizon
  • FAILED_FADE: Forward return <= -2% without a recorded stop hit
  • CHOPPY_FAILURE: Adverse excursion was large and forward progress was poor
  • NEUTRAL: No decisive follow-through or failure
  • PENDING: Not enough future bars yet

Output

  • state/stockbee/model_book.jsonl - Durable setup model book
  • stockbee_setup_fluency_ingest_YYYY-MM-DD_HHMMSS.json/md
  • stockbee_setup_fluency_update_YYYY-MM-DD_HHMMSS.json/md
  • stockbee_setup_fluency_summary_YYYY-MM-DD_HHMMSS.json/md

Resources

  • references/model_book_schema.md - JSONL schema and lifecycle states
  • references/outcome_tags.md - Outcome classification and tag definitions
  • references/review_workflow.md - Daily, 3-day, 5-day, and monthly review routine