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/stockbee-momentum-burst-screener

@6cdf015

Screen US stocks for Stockbee-style short-term Momentum Burst setups using 4% breakout, dollar breakout, range expansion, volume expansion, prior range contraction, close-location, failure filters, and risk-distance scoring. Use when the user asks for Stockbee, Pradeep Bonde, momentum burst, 4% breakout, range expansion, dollar breakout, short-term swing momentum candidates, or 3-5 day burst setup review.

Use this Skill: https://skilld.dev/gh/tradermonty/claude-trading-skills/stockbee-momentum-burst-screener

This session only. Nothing lands on disk.

referencesentry_exit_rules.md

≈364 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Entry and Exit Rules

Entry Reference

The default entry reference is the latest close because the script is primarily an end-of-day / near-close screener.

If used intraday with current OHLCV bars, treat the entry reference as an indicative planning price. Manual review should confirm that the breakout is still valid near the intended order time.

Stop Reference

The default stop reference is the trigger-day low.

risk_per_share = entry_reference - stop_reference
risk_pct_to_stop = risk_per_share / entry_reference * 100

Reject candidates where the stop is too far away for the account's risk policy.

Position Sizing Handoff

Send only validated candidates to position-sizer.

position_size = account_risk_dollars / (entry_reference - stop_reference)

The screener should not decide final share count. It only provides the fields needed by a position-sizing skill.

Exit Template

Default template:

  • Stop out if the trigger-day low fails
  • Review after 3-5 sessions
  • Protect gains if the stock advances abnormally fast, especially a 10%+ move in one session
  • Treat a full signal reversal or no follow-through after several sessions as a failed burst

Practical Notes

  • A 4% trigger is not enough by itself. Require setup quality and manageable risk.
  • Avoid using the screener as a standalone buy list.
  • Keep rejected names for model-book review because failures help calibrate pattern recognition.

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub26d

    The skill screens US stocks for momentum patterns by fetching data from a financial API or local files. It uses a Python script to perform technical analysis and generate reports. The code is well-structured and follows standard development practices, with the only noted risk being the inherent surface for indirect prompt injection common to all data-ingestion tools.

  • Socket26d

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  • Snyk26d

    Risk: LOW · No issues

Signed by skilld at 6cdf015. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 3 months ago

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