# Stockbee Episodic Pivot Analyzer

Analyze Stockbee-style Day 1 Episodic Pivot candidates from earnings, guidance raises...

- Repository: https://github.com/tradermonty/claude-trading-skills (3536 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add tradermonty/claude-trading-skills --skill stockbee-episodic-pivot-analyzer -g -a claude-code -y`
Codex: `npx skills add tradermonty/claude-trading-skills --skill stockbee-episodic-pivot-analyzer -g -a codex -y`
Cursor: `npx skills add tradermonty/claude-trading-skills --skill stockbee-episodic-pivot-analyzer -g -a cursor -y`
Gemini CLI: `npx skills add tradermonty/claude-trading-skills --skill stockbee-episodic-pivot-analyzer -g -a gemini-cli -y`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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:**

```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

```bash
# 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/

The rest: https://github.com/tradermonty/claude-trading-skills/blob/HEAD/skills/stockbee-episodic-pivot-analyzer/SKILL.md