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IhsanDanish25
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stockbee-setup-fluency-trainer

Create and maintain a Stockbee-style setup model book from momentum-burst candidates.

2|Updated Jun 14, 2026
Search Tags:#performance tracking#trading#setup analysis#model book#momentum-burst
One-click install
npx skills add https://github.com/IhsanDanish25/claude-trading-skills --skill stockbee-setup-fluency-trainer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stockbee-setup-fluency-trainer
Source: https://github.com/IhsanDanish25/claude-trading-skills/tree/main/external-skills/claude-trading-skills/skills/stockbee-setup-fluency-trainer
Command: npx skills add https://github.com/IhsanDanish25/claude-trading-skills --skill stockbee-setup-fluency-trainer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, requests, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps traders build and maintain a model book for Stockbee-style setups, allowing them to study and track the performance of momentum-burst candidates over time.

Core Features & Use Cases

  • Model Book Creation: Build a model book from momentum-burst screener candidates.
  • Outcomes Update: Update model book entries with 3-day and 5-day forward outcomes, including MFE/MAE, stop-hit status, and outcome tags.
  • Cohort Statistics: Summarize cohort statistics for performance review.
  • Use Case: A trader can use this Skill to systematically study Stockbee Momentum Burst setups, track failed candidates, and review A/B setup quality.

Quick Start

Run the following command to ingest momentum-burst candidates into the model book:

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/

Frequently Asked Questions about stockbee-setup-fluency-trainer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I track momentum-burst setup performance over time?▼

A Stockbee setup model book ingests momentum-burst screener candidates as JSONL entries, records 3-day and 5-day forward outcomes, and summarizes cohort statistics to study A/B setup quality over time.

How do I update forward outcomes for trading setup candidates?▼

Update forward outcomes by processing your JSONL model book to append 3-day and 5-day MFE/MAE, stop-hit status, and outcome tags to each momentum-burst candidate entry.

Do I need Python to run the Stockbee setup fluency trainer?▼

Yes, Python is required to execute the scripts, process JSON screener data, and maintain the JSONL model book state for momentum-burst setup tracking.

What format does the screener data need to be in for setup analysis?▼

Screener data must be structured as a JSON file, passed via the --screener-json argument, to ingest momentum-burst candidates into the JSONL model book for analysis.

Can I review failed momentum-burst candidates with this setup tracking approach?▼

Yes, the model book records stop-hit status and outcome tags for failed momentum-burst candidates, enabling you to review A/B setup quality and summarize cohort statistics.

What is the best way to study Stockbee Momentum Burst setups systematically?▼

Systematically study Stockbee setups by maintaining a model book that ingests screener candidates, appends forward MFE/MAE outcomes, and summarizes cohort performance statistics.

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