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_DAY1 candidates from DELAYED_EP_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 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/