Stockbee Momentum Burst Screener

Screen US equities for Stockbee-style short-term Momentum Burst candidates. The skill is a candidate-generation and setup-quality workflow, not a signal service or an auto-execution system.

When to Use

• User asks for Stockbee / Pradeep Bonde style Momentum Burst screening
• User wants 4% breakout, dollar breakout, or range expansion candidates
• User asks for short-term 3-5 day swing momentum setups
• User wants to review whether a daily breakout has A/B/C setup quality
• User provides a symbol list, universe file, or historical OHLCV JSON for screening
• User wants candidate outputs to feed into technical-analyst, position-sizer, or trader-memory-core

Prerequisites

• FMP API key for live universe and historical OHLCV screening:

``bash export FMPAPIKEY=yourapikey_here ``

• Optional no-API path: provide --prices-json containing daily OHLCV bars by symbol.
• Run only after the market-regime workflow allows new swing risk, or mark output as manual-review-only.

Workflow

Step 1: Choose Input Mode

Use one of three modes:

Mode A: FMP universe scan

python3 skills/stockbee-momentum-burst-screener/scripts/screen_momentum_burst.py \
  --fmp-universe \
  --max-symbols 300 \
  --output-dir reports/

Mode B: Explicit symbols

python3 skills/stockbee-momentum-burst-screener/scripts/screen_momentum_burst.py \
  --symbols NVDA SMCI PLTR TSLA \
  --output-dir reports/

Mode C: Offline OHLCV JSON

python3 skills/stockbee-momentum-burst-screener/scripts/screen_momentum_burst.py \
  --prices-json data/daily_ohlcv.json \
  --output-dir reports/

Step 2: Run the Screening Pass

The script detects these trigger families:

• 4% Breakout: close / previous_close >= 1.04, volume above previous day, and volume above the liquidity floor
• Dollar Breakout: close - open >= 0.90, volume above the liquidity floor
• Range Expansion: current daily range exceeds the prior three daily ranges while the prior day was not already extended

It then scores setup quality using:

• Trigger strength
• Volume expansion
• Prior base / range contraction quality
• Close location near the high of day
• Risk distance to the trigger-day low
• Failure filters such as prior 3-day run-up or recent 4% breakdown
• Market gate alignment

Step 3: Review Output

Read the generated JSON and Markdown reports. For each candidate, present:

• Trigger type and all matched trigger tags
• Day gain, dollar gain, volume ratio, and close-location percentage
• Prior base length and base width
• Entry reference, stop reference, and risk percentage to stop
• Setup score, rating, state, and reject reasons
• Suggested downstream action

Step 4: Send Survivors to Trade Planning

Use the output conservatively:

• A / A- candidates: send to technical-analyst for manual chart validation, then position-sizer
• B candidates: watchlist or smaller-risk review only
• Watch-only candidates: keep in model book; do not plan a trade unless chart review upgrades the setup
• Rejected candidates: retain for post-analysis, not for execution

Output

• stockbeemomentumburstYYYY-MM-DDHHMMSS.json - Structured candidate list, metadata, thresholds, score components, and rejects
• stockbeemomentumburstYYYY-MM-DDHHMMSS.md - Human-readable report grouped by rating/state

Resources

• references/momentumburstmethodology.md - Stockbee-style method summary and implementation boundaries
• references/scoring_system.md - Component weights, state thresholds, and failure filters
• references/entryexitrules.md - Entry reference, stop, sizing handoff, and exit template