--- 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