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stockbee-episodic-pivot-analyzer

stockbee-episodic-pivot-analyzer

github.com/tradermontyOther3.4k2.7k installsUpdated 2d ago
Repository

About stockbee-episodic-pivot-analyzer

A Python skill for classifying Day 1 Episodic Pivot candidates from catalyst quality and price/volume confirmation. It is for traders reviewing supplied event and market data, and produces actionable, watch, or reject states rather than executing trades.

Key features

  1. 01Scores catalyst quality and price/volume
  2. 02Classifies EP candidate states and types
  3. 03Uses supplied event and OHLCV data
  4. 04Accepts earnings pipeline and screener reports
  5. 05Exports JSON and Markdown reports

Use cases

  1. 01Separate actionable Day 1 setups from delayed watches
  2. 02Review earnings and guidance EP candidates
  3. 03Combine catalysts with momentum screener output

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/tradermonty/claude-trading-skills --skill stockbee-episodic-pivot-analyzer

After installing, type/stockbee-episodic-pivot-analyzerin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

requirements.txt
references/
catalyst_quality.md
ep_methodology.md
handoff_rules.md
scripts/
analyze_ep.py
fmp_client.py
scripts/tests/
test_analyze_ep_coverage.py
test_analyze_ep.py

Details

From the project README

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/

# 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

Shared FMP client

Live FMP access uses the bundled generated scripts/fmp_client.py, loaded only when an FMP run starts, so offline modes still work without requests. CLI key/environment precedence, the API-call budget and successful output shapes are preserved. Requests use stable endpoints only; a denied, empty or failed stable request does not fall back to retired v3 URLs. The shared client masks raw and URL-encoded key echoes, retries malformed JSON once, and retries a 429 at most once, after a 60-second wait, before stopping on a repeated rate limit.

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