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stockbee-momentum-burst-screener

stockbee-momentum-burst-screener

github.com/tradermontyOther3.4k2.8k installsUpdated 2d ago
Repository

About stockbee-momentum-burst-screener

stockbee-momentum-burst-screener is a short-term momentum candidate screener for US equities. It detects Stockbee-style breakouts and range expansions, then rates setup quality; it generates candidates for review rather than trading signals or automatic execution.

Key features

  1. 01Detects 4% breakouts, dollar breakouts, and range expansion
  2. 02Scores volume, base quality, and closing location
  3. 03Checks run-ups and recent breakdowns
  4. 04Provides entry, stop, and risk references
  5. 05Produces JSON and Markdown reports

Use cases

  1. 01Screen candidates for 3–5 day momentum swings
  2. 02Rate daily breakout setup quality from A to C
  3. 03Screen symbol lists or offline OHLCV data
  4. 04Pass survivors to chart review and position planning

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/tradermonty/claude-trading-skills --skill stockbee-momentum-burst-screener

After installing, type/stockbee-momentum-burst-screenerin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

requirements.txt
references/
entry_exit_rules.md
momentum_burst_methodology.md
scoring_system.md
scripts/
fmp_client.py
screen_momentum_burst.py
scripts/tests/
test_screen_momentum_burst_coverage.py
test_screen_momentum_burst.py

Details

From the project README

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:
    export FMP_API_KEY=your_api_key_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

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

Resources

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

Shared FMP client

Live FMP access uses the bundled generated scripts/fmp_client.py, loaded only when an FMP run starts, so offline modes still work without requests. CLI key/environment precedence, the API-call budget and successful output shapes are preserved. Requests use stable endpoints only; a denied, empty or failed stable request does not fall back to retired v3 URLs. The shared client masks raw and URL-encoded key echoes, retries malformed JSON once, and retries a 429 at most once, after a 60-second wait, before stopping on a repeated rate limit.

Comments

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