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: