Stockbee Setup Fluency Trainer

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.

No API FMP Optional

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

Stockbee Setup Fluency Trainer

2. 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

3. 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

4. Quick Start 

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/

5. 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

6. Resources

References:

  • skills/stockbee-setup-fluency-trainer/references/model_book_schema.md
  • skills/stockbee-setup-fluency-trainer/references/outcome_tags.md
  • skills/stockbee-setup-fluency-trainer/references/review_workflow.md

Scripts:

  • skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py