# Stockbee Setup Fluency Trainer

Build a Stockbee-style setup model book from momentum-burst screener candidates, then...

- Repository: https://github.com/tradermonty/claude-trading-skills (3536 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add tradermonty/claude-trading-skills --skill stockbee-setup-fluency-trainer -g -a claude-code -y`
Codex: `npx skills add tradermonty/claude-trading-skills --skill stockbee-setup-fluency-trainer -g -a codex -y`
Cursor: `npx skills add tradermonty/claude-trading-skills --skill stockbee-setup-fluency-trainer -g -a cursor -y`
Gemini CLI: `npx skills add tradermonty/claude-trading-skills --skill stockbee-setup-fluency-trainer -g -a gemini-cli -y`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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.

```bash
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:

```bash
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:

The rest: https://github.com/tradermonty/claude-trading-skills/blob/HEAD/skills/stockbee-setup-fluency-trainer/SKILL.md