Agent skill · data analytics · tradermonty
stockbee-setup-fluency-trainer
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.
Why this skill is useful
Adds specific workflows and commands for analyzing Stockbee Momentum Burst setups that the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. Requires fmp account access. About 2k tokens when loaded. Last updated 2026-08-06. 2,581 stars on the source repository.
What this skill does
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/modelbook.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 STRONGWINNER, WORKED, FAILEDSTOP, FAILEDFADE, CHOPPYFAILURE, or NEUTRAL Step 3: Summarize Cohorts Review the generated Markdown and JSON reports. Treat rulecandidates as evidence prompts, not automatic rule changes. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills tradermonty/stockbee-setup-fluency-trainer