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 STRONGWINNER, WORKED, FAILEDSTOP, FAILEDFADE, CHOPPYFAILURE, 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:

• recordid, symbol, setupdate, primary_trigger
• rating, setupscore, setuptags
• entryreference, stopreference, riskpctto_stop
• humanlabel, humandecision, human_notes
• outcomes.3d and outcomes.5d
• overalloutcome, matured, rawcandidate

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
• stockbeesetupfluencyingestYYYY-MM-DD_HHMMSS.json/md
• stockbeesetupfluencyupdateYYYY-MM-DD_HHMMSS.json/md
• stockbeesetupfluencysummaryYYYY-MM-DD_HHMMSS.json/md

Resources

• references/modelbookschema.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