stockbee-momentum-burst-screener
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
- 01Detects 4% breakouts, dollar breakouts, and range expansion
- 02Scores volume, base quality, and closing location
- 03Checks run-ups and recent breakdowns
- 04Provides entry, stop, and risk references
- 05Produces JSON and Markdown reports
Use cases
- 01Screen candidates for 3–5 day momentum swings
- 02Rate daily breakout setup quality from A to C
- 03Screen symbol lists or offline OHLCV data
- 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-screenerAfter installing, type/stockbee-momentum-burst-screenerin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
requirements.txtreferences/entry_exit_rules.mdmomentum_burst_methodology.mdscoring_system.mdscripts/fmp_client.pyscreen_momentum_burst.pyscripts/tests/test_screen_momentum_burst_coverage.pytest_screen_momentum_burst.pyDetails
From the project READMEStockbee 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, ortrader-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-jsoncontaining 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-analystfor manual chart validation, thenposition-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 rejectsstockbee_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 boundariesreferences/scoring_system.md- Component weights, state thresholds, and failure filtersreferences/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.