Stockbee setup fluency trainer

Skill BaggaT236/AI-Trading-Skills/skills/stockbee-setup-fluency-trainer

Ai agent claude trading skills for disiplined, repeatable trading workflows with a modern typescript platform

Install

npx -y skills add BaggaT236/AI-Trading-Skills --skill stockbee-setup-fluency-trainer

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What its author says it does

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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.

SKILL.md

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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.

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

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