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 fl

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. 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: Use offline OHLCV JSON: 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 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