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.

3installs

Sourcetradermonty/claude-trading-skills

Added on2026-08-20

NPX Install

npx skill4agent add tradermonty/claude-trading-skills stockbee-setup-fluency-trainer

SKILL.md Content

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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:

bash

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

bash

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

stockbee-setup-fluency-trainer

NPX Install

Tags

SKILL.md Content

Stockbee Setup Fluency Trainer

When to Use

Prerequisites

Workflow

Step 1: Ingest Momentum Burst Candidates

Step 2: Update 3-Day and 5-Day Outcomes

Step 3: Summarize Cohorts

Step 4: Convert Evidence Into Practice

Model Book Fields

Interpretation Rules

Output

Resources