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 ACTIONABLEDAY1 candidates from DELAYEDEP_WATCH names
• The user wants to hand strong earnings/guidance EPs into pead-screener
• The user wants to combine catalyst analysis with stockbee-momentum-burst-screener price/volume output

Prerequisites

• Python 3.10+
• Optional: FMP API key for OHLCV/profile enrichment
• One of:

- Catalyst/events JSON - earnings-trade-analyzer JSON output - Catalyst JSON plus stockbee-momentum-burst-screener JSON 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: ACTIONABLEDAY1, DAY1WATCH, DELAYEDEPWATCH, CATALYST_WATCH, or REJECT
• eptype: EARNINGSEP, GUIDANCEEP, FDAEP, MANDAEP, STORYEP, etc.
• Catalyst quality score and reasons
• Price/range expansion, volume shock, and close-location quality
• Risk to EP-day low
• peadhandoff and delayedep_watch flags

Step 4: Handoff Rules

• ACTIONABLE_DAY1: Send to technical-analyst and position-sizer before any trade decision.
• DAY1_WATCH: Keep on the intraday/next-day watchlist; require chart confirmation.
• DELAYEDEPWATCH: 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=true can be sent to pead-screener for weekly red-candle / delayed reaction monitoring.

Output

• stockbeeepisodicpivotYYYY-MM-DDHHMMSS.json — structured EP scoring report
• stockbeeepisodicpivotYYYY-MM-DDHHMMSS.md — human-readable candidate report

Resources

• references/ep_methodology.md — Stockbee EP interpretation and setup taxonomy
• references/catalyst_quality.md — catalyst classification and quality scoring
• references/handoff_rules.md — downstream workflow handoffs and review rules