stockbee-episodic-pivot-analyzer
tradermonty/claude-trading-skills
Identify and score Stockbee-style Day 1 Episodic Pivot candidates from earnings, M&A, FDA, and catalyst events.
What is stockbee-episodic-pivot-analyzer?
Analyzes potential Day 1 Episodic Pivot (EP) trading candidates by combining catalyst quality assessment with price/volume confirmation. Use this when evaluating earnings surprises, guidance raises, M&A announcements, regulatory approvals, or other game-changing news events to separate actionable Day 1 trades from delayed-reaction watchlist candidates.
- Classifies candidates into ACTIONABLE_DAY1, DAY1_WATCH, DELAYED_EP_WATCH, CATALYST_WATCH, or REJECT states
- Scores catalyst quality for earnings, guidance, FDA, M&A, analyst actions, contracts, product launches, and short-squeeze events
- Evaluates price/range expansion, volume shock, close location, and risk-to-EP-day-low
- Accepts catalyst JSON, earnings-trade-analyzer output, or stockbee-momentum-burst-screener enrichment
- Generates structured JSON and markdown reports with handoff flags for downstream skills
How to install stockbee-episodic-pivot-analyzer
npx skills add https://github.com/tradermonty/claude-trading-skills --skill stockbee-episodic-pivot-analyzer- Python 3.10+
- Catalyst/event JSON, earnings-trade-analyzer output, or stockbee-momentum-burst-screener JSON
- Optional: FMP API key for OHLCV and profile enrichment
How to use stockbee-episodic-pivot-analyzer
- 1.Prepare candidate inputs: catalyst JSON, earnings pipeline output, or price/volume enrichment from momentum screener
- 2.Run the analyzer script with your input source (--events-json, --earnings-json, or --momentum-json)
- 3.Optionally set FMP_API_KEY environment variable for enriched OHLCV data
- 4.Review the output JSON and markdown report for each candidate's state, catalyst quality score, and handoff flags
- 5.Send ACTIONABLE_DAY1 candidates to technical-analyst and position-sizer; route earnings EPs with pead_handoff=true to pead-screener
Use cases
- Screening earnings announcements for Day 1 gap-and-range-expansion candidates with volume confirmation
- Evaluating FDA approvals or guidance raises to identify neglect/revaluation pivots
- Combining catalyst events with momentum data to separate high-conviction Day 1 trades from delayed reactions
- Preparing earnings/guidance EPs for PEAD (Post-Earnings Announcement Drift) monitoring via pead-screener
- Building a delayed EP watchlist for controlled pullback entry opportunities
- Day traders and swing traders focused on catalyst-driven price moves
- Traders using Stockbee episodic pivot methodology
- Quantitative researchers building event-driven screening workflows
- Portfolio managers monitoring earnings and regulatory catalysts for tactical entry points
stockbee-episodic-pivot-analyzer FAQ
An EP is a Stockbee-style Day 1 catalyst-driven trade setup where a stock gaps and expands its range on significant news (earnings, guidance, M&A, FDA, etc.) with volume confirmation, often leading to a revaluation or neglect-to-attention shift.
Catalyst/event JSON (with symbol, date, type, headline), earnings-trade-analyzer JSON output, or stockbee-momentum-burst-screener JSON for price/volume enrichment.
ACTIONABLE_DAY1 = high-conviction Day 1 trade; DAY1_WATCH = requires chart confirmation; DELAYED_EP_WATCH = do not chase Day 1, monitor for pullback; CATALYST_WATCH = catalyst important but price/volume not yet sufficient; REJECT = do not trade.
Yes. The skill works offline with catalyst JSON and optional local OHLCV data. FMP enrichment is optional and improves profile and liquidity scoring.
Send ACTIONABLE_DAY1 candidates to technical-analyst and position-sizer for trade validation. Route earnings/guidance EPs with pead_handoff=true to pead-screener for weekly post-earnings drift monitoring.
Full instructions (SKILL.md)
Source of truth, from tradermonty/claude-trading-skills.
name: stockbee-episodic-pivot-analyzer description: Analyze Stockbee-style Day 1 Episodic Pivot candidates from earnings, guidance raises, M&A, FDA/regulatory approvals, analyst actions, major contracts, product launches, short-squeeze catalysts, or theme/story events. Scores catalyst quality together with gap/range expansion, volume shock, neglect/revaluation context, liquidity, and risk to the EP-day low. Use when the user asks for EP candidates, episodic pivots, Day 1 catalyst trades, game-changing news reactions, delayed EP watchlists, or handoffs into PEAD monitoring.
Stockbee Episodic Pivot Analyzer
Classify Day 1 Episodic Pivot (EP) candidates using both catalyst quality and price/volume confirmation. The skill is a candidate-quality analyzer, not an execution engine.
When to Use
- The user asks for Pradeep Bonde / Stockbee style EP candidates
- The user provides earnings, guidance, M&A, FDA, analyst, contract, product, short-squeeze, or theme/news events
- The user wants to separate
ACTIONABLE_DAY1candidates fromDELAYED_EP_WATCHnames - The user wants to hand strong earnings/guidance EPs into
pead-screener - The user wants to combine catalyst analysis with
stockbee-momentum-burst-screenerprice/volume output
Prerequisites
- Python 3.10+
- Optional: FMP API key for OHLCV/profile enrichment
- One of:
- Catalyst/events JSON
earnings-trade-analyzerJSON output- Catalyst JSON plus
stockbee-momentum-burst-screenerJSON enrichment
- This skill does not fetch or discover news by itself. If the catalyst is not supplied, first gather the event/news context using the user's preferred news or research process.
Workflow
Step 1: Prepare Candidate Inputs
Use one or more of these input modes.
Mode A — Catalyst/event JSON:
{
"events": [
{
"symbol": "ABC",
"event_date": "2026-04-25",
"catalyst_type": "guidance_raise",
"headline": "ABC raises FY guidance after record demand",
"summary": "Management raised revenue and EPS guidance."
}
]
}
Mode B — Earnings pipeline:
Use the JSON produced by earnings-trade-analyzer.
Mode C — Price/volume enrichment:
Pass a stockbee-momentum-burst-screener JSON report to reuse day-gain, volume, close-location, and risk-distance fields.
Step 2: Run the Analyzer
# Catalyst JSON + offline OHLCV
python3 skills/stockbee-episodic-pivot-analyzer/scripts/analyze_ep.py \
--events-json data/catalysts.json \
--prices-json data/daily_ohlcv.json \
--output-dir reports/
# Earnings pipeline input
python3 skills/stockbee-episodic-pivot-analyzer/scripts/analyze_ep.py \
--earnings-json reports/earnings_trade_analyzer_YYYY-MM-DD_HHMMSS.json \
--output-dir reports/
# Catalyst JSON + Stockbee momentum enrichment
python3 skills/stockbee-episodic-pivot-analyzer/scripts/analyze_ep.py \
--events-json data/catalysts.json \
--momentum-json reports/stockbee_momentum_burst_YYYY-MM-DD_HHMMSS.json \
--output-dir reports/
Optional FMP enrichment:
export FMP_API_KEY=your_key
python3 skills/stockbee-episodic-pivot-analyzer/scripts/analyze_ep.py \
--events-json data/catalysts.json \
--max-api-calls 200 \
--output-dir reports/
Step 3: Review the Output
For each candidate, present:
state:ACTIONABLE_DAY1,DAY1_WATCH,DELAYED_EP_WATCH,CATALYST_WATCH, orREJECTep_type:EARNINGS_EP,GUIDANCE_EP,FDA_EP,M_AND_A_EP,STORY_EP, etc.- Catalyst quality score and reasons
- Price/range expansion, volume shock, and close-location quality
- Risk to EP-day low
pead_handoffanddelayed_ep_watchflags
Step 4: Handoff Rules
ACTIONABLE_DAY1: Send totechnical-analystandposition-sizerbefore any trade decision.DAY1_WATCH: Keep on the intraday/next-day watchlist; require chart confirmation.DELAYED_EP_WATCH: Do not chase Day 1; monitor for a controlled pullback or new range.CATALYST_WATCH: Catalyst may be important, but price/volume confirmation is not yet sufficient.REJECT: Do not trade from this candidate source.- Earnings/guidance EPs with
pead_handoff=truecan be sent topead-screenerfor weekly red-candle / delayed reaction monitoring.
Output
stockbee_episodic_pivot_YYYY-MM-DD_HHMMSS.json— structured EP scoring reportstockbee_episodic_pivot_YYYY-MM-DD_HHMMSS.md— human-readable candidate report
Resources
references/ep_methodology.md— Stockbee EP interpretation and setup taxonomyreferences/catalyst_quality.md— catalyst classification and quality scoringreferences/handoff_rules.md— downstream workflow handoffs and review rules
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