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stockbee-episodic-pivot-analyzer — Claude Code skill setup & review

Evaluates Day 1 Episodic Pivot setups using Stockbee criteria. It pairs corporate catalysts with volume shocks and opening range price expansion.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add tradermonty/claude-trading-skills --skill stockbee-episodic-pivot-analyzer -a claude-code
Open on GitHub

What stockbee-episodic-pivot-analyzer does

The skill evaluates Episodic Pivot (EP) stock candidates driven by earnings surprises, guidance upgrades, FDA decisions, or contract wins. Its analyze_ep.py script checks catalyst significance against volume shocks and price extension from the day's low. It categorizes each setup into states like ACTIONABLE_DAY1, DELAYED_EP_WATCH, or REJECT.

It requires Python 3.10 or newer. Crucially, the script will not search or parse headlines on its own. You must feed it catalyst events in a structured JSON file or pipe in results from earnings-trade-analyzer. Historical prices can come from offline OHLCV files or an optional Financial Modeling Prep API token via FMP_API_KEY.

Outputs generate as timestamped JSON and Markdown reports. If an earnings pivot flags pead_handoff, you can route the ticker straight into pead-screener for post-earnings drift monitoring.

What it helps with

  • filter news catalysts against volume shocks and price expansion
  • measure trade risk distance against the Day 1 EP low
  • separate immediate Day 1 trades from delayed pullback watchlists
  • pass earnings winners into pead-screener for drift tracking

Example prompts for the agent

  • Run the episodic pivot analysis on data/catalysts.json using prices from data/daily_ohlcv.json
  • Evaluate candidate quality from reports/earnings_trade_analyzer.json using Stockbee EP rules
  • Screen my event JSON for ACTIONABLE_DAY1 setups and output the markdown report

How to install stockbee-episodic-pivot-analyzer

1Skills CLI (Vercel)

npx skills add tradermonty/claude-trading-skills --skill stockbee-episodic-pivot-analyzer -a claude-code

2Manually: put the files in place

~/.claude/skills/stockbee-episodic-pivot-analyzer/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/stockbee-episodic-pivot-analyzer/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about stockbee-episodic-pivot-analyzer

How to install stockbee-episodic-pivot-analyzer in Claude Code?

Run npx skills add tradermonty/claude-trading-skills --skill stockbee-episodic-pivot-analyzer -a claude-code. The definition is saved to ~/.claude/skills/stockbee-episodic-pivot-analyzer/SKILL.md.

What does stockbee-episodic-pivot-analyzer do?

It classifies stock setups using Pradeep Bonde's Stockbee Episodic Pivot framework. The analyzer tests whether news impact is confirmed by trading volume and price extension.

Does this skill fetch market news automatically?

No, the tool does not scan the web for news catalysts. You must provide events in a JSON file, while live price enrichment requires an optional FMP_API_KEY.

Source text

SKILL.md · MIT
---
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_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/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:**

```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/

# 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:

```bash
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`, or `REJECT`
- `ep_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_handoff` and `delayed_ep_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.
- `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=true` can be sent to `pead-screener` for weekly red-candle / delayed reaction monitoring.

## Output

- `stockbee_episodic_pivot_YYYY-MM-DD_HHMMSS.json` — structured EP scoring report
- `stockbee_episodic_pivot_YYYY-MM-DD_HHMMSS.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

© tradermonty · MIT · tradermonty/claude-trading-skills

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