Screen US stocks for Stockbee-style selling-exhaustion hammer setups using prior momentum, pullback depth, undercut/reclaim, long lower-wick geometry, close-location, volume confirmation, quality/liquidity gates, and risk-distance scoring. Use when the user asks for Stockbee, Pradeep Bonde, exhaustion setup, selling exhaustion, hammer reversal, undercut reclaim, near-close reversal candidates, or pullback entries in high-quality funds-owned stocks.
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Screen US stocks for Stockbee-style selling-exhaustion hammer setups using prior momentum, pullback depth, undercut/reclaim, long lower-wick geometry, close-location, volume confirmation, quality/liquidity gates, and risk-distance scoring. Use when the user asks for Stockbee, Pradeep Bonde, exhaustion setup, selling exhaustion, hammer reversal, undercut reclaim, near-close reversal candidates, or pullback entries in high-quality funds-owned stocks.
Screen US equities for Stockbee-style selling-exhaustion hammer candidates. The skill is a candidate-generation and setup-quality workflow, not a signal service or an auto-execution system.
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technical-analyst, position-sizer, trader-memory-core, or stockbee-setup-fluency-trainerexport FMP_API_KEY=your_api_key_here
--prices-json containing daily OHLCV bars by symbol. For the intended near-close use case, the latest bar should be a provisional current-day bar captured near the close.--profiles-json can add quality metadata such as marketCap, mutualFundHolders, institutionalHolders, or institutionalOwnershipPct.Use one of three modes:
Mode A: FMP universe scan
python3 skills/stockbee-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
--fmp-universe \
--max-symbols 300 \
--market-gate allowed \
--output-dir reports/
Mode B: Explicit symbols
python3 skills/stockbee-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
--symbols APP ENPH NVDA TSLA \
--market-gate allowed \
--output-dir reports/
Mode C: Offline / near-close OHLCV JSON
python3 skills/stockbee-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
--prices-json data/near_close_daily_ohlcv.json \
--profiles-json data/quality_profiles.json \
--market-gate allowed \
--output-dir reports/
For a best-effort FMP near-close run, use quote override. This costs one additional quote call per symbol and depends on provider freshness:
python3 skills/stockbee-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
--fmp-universe \
--use-quote-latest \
--max-api-calls 700 \
--market-gate allowed \
--output-dir reports/
The script detects these setup families:
It then scores setup quality using:
Read the generated JSON and Markdown reports. For each candidate, present:
Use the output conservatively:
position-sizerstockbee_exhaustion_hammer_YYYY-MM-DD_HHMMSS.json - Structured candidate list, metadata, thresholds, score components, and rejectsstockbee_exhaustion_hammer_YYYY-MM-DD_HHMMSS.md - Human-readable report grouped by rating/statereferences/exhaustion_hammer_methodology.md - Stockbee-style method summary and implementation boundariesreferences/scoring_system.md - Component weights, state thresholds, and failure filtersreferences/near_close_operations.md - Near-close operational checklist and scheduling notesIn these kits
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