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Data Analysis · Business / AGENT SKILL
stockbee-setup-fluency-trainer
0
Builds a momentum-burst example collection and tracks forward outcomes to support trading setup study.
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.
Understand the trade-offs.
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Original instructions from the publisher’s SKILL.md
# 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