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:
{
"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, 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
---
name: stockbee-episodic-pivot-analyzer
description: Analyzes Stockbee-style Day 1 Episodic Pivot candidates by scoring catalyst quality alongside price/volume confirmation, gap/range expansion, volume shock, neglect/revaluation context, liquidity, and risk to the EP-day low.
---
# 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