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/pead-screener

Screen post-earnings gap-up stocks for PEAD (Post-Earnings Announcement Drift) patterns. Analyzes weekly candle formation to detect red candle pullbacks and breakout signals. Supports two input modes - FMP earnings calendar (Mode A) or earnings-trade-analyzer JSON output (Mode

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claude-trading-skills
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Install
$ npx -y skills add tradermonty/claude-trading-skills --skill pead-screener --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/pead-screener

Context preview

The summary Claude sees to decide when to auto-load this skill.

Screen post-earnings gap-up stocks for PEAD (Post-Earnings Announcement Drift) patterns. Analyzes weekly candle formation to detect red candle pullbacks and breakout signals. Supports two input modes - FMP earnings calendar (Mode A) or earnings-trade-analyzer JSON output (Mode

SKILL.md

pead-screener.SKILL.md
name: pead-screener
description: Screen post-earnings gap-up stocks for PEAD (Post-Earnings Announcement Drift) patterns. Analyzes weekly candle formation to detect red candle pullbacks and breakout signals. Supports two input modes - FMP earnings calendar (Mode A) or earnings-trade-analyzer JSON output (Mode B). Use when user asks about PEAD screening, post-earnings drift, earnings gap follow-through, red candle breakout patterns, or weekly earnings momentum setups.

PEAD Screener - Post-Earnings Announcement Drift

Screen post-earnings gap-up stocks for PEAD (Post-Earnings Announcement Drift) patterns using weekly candle analysis to detect red candle pullbacks and breakout signals.

When to Use

  • User asks for PEAD screening or post-earnings drift analysis
  • User wants to find earnings gap-up stocks with follow-through potential
  • User requests red candle breakout patterns after earnings
  • User asks for weekly earnings momentum setups
  • User provides earnings-trade-analyzer JSON output for further screening

Prerequisites

  • FMP API key (set `FMP_API_KEY` environment variable or pass `--api-key`)
  export FMP_API_KEY=your_api_key_here
  • Free tier (250 calls/day) is sufficient for default screening
  • For Mode B: earnings-trade-analyzer JSON output file with schema_version "1.0"

Workflow

Step 1: Prepare and Execute Screening

Run the PEAD screener script in one of two modes:

**Mode A (FMP earnings calendar):**

# Default: last 14 days of earnings, 5-week monitoring window
python3 skills/pead-screener/scripts/screen_pead.py --output-dir reports/

# Custom parameters
python3 skills/pead-screener/scripts/screen_pead.py \
  --lookback-days 21 \
  --watch-weeks 6 \
  --min-gap 5.0 \
  --min-market-cap 1000000000 \
  --output-dir reports/

**Mode B (earnings-trade-analyzer JSON input):**

# From earnings-trade-analyzer output
python3 skills/pead-screener/scripts/screen_pead.py \
  --candidates-json reports/earnings_trade_analyzer_YYYY-MM-DD_HHMMSS.json \
  --min-grade B \
  --output-dir reports/

**Scheduled US-equity routine pitfall:** Prefer Mode B for pre-market / US-equity cron briefs after running `earnings-trade-analyzer`. Mode A can pull the global FMP earnings calendar, spend the API budget on non-US symbols, and return weak/non-actionable foreign listings before reaching the intended US watchlist. If Mode A is used anyway and the script reports budget trimming or non-US symbols, mark PEAD output as degraded and treat it as manual-review only rather than a clean candidate source.

Step 2: Review Results

1. Read the generated JSON and Markdown reports 2. Load `references/pead_strategy.md` for PEAD theory and pattern context 3. Load `references/entry_exit_rules.md` for trade management rules

Step 3: Present Analysis

For each candidate, present:

  • Stage classification (MONITORING, SIGNAL_READY, BREAKOUT, EXPIRED)
  • Weekly candle pattern details (red candle location, breakout status)
  • Composite score and rating
  • Trade setup: entry, stop-loss, target, risk/reward ratio
  • Liquidity metrics (ADV20, average volume)

Step 4: Provide Actionable Guidance

Based on stages and ratings:

  • **BREAKOUT + Strong Setup (85+):** High-conviction PEAD trade, full position size
  • **BREAKOUT + Good Setup (70-84):** Solid PEAD setup, standard position size
  • **SIGNAL_READY:** Red candle formed, set alert for breakout above red candle high
  • **MONITORING:** Post-earnings, no red candle yet, add to watchlist
  • **EXPIRED:** Beyond monitoring window, remove from watchlist

Output

  • `pead_screener_YYYY-MM-DD_HHMMSS.json` - Structured results with stage classification
  • `pead_screener_YYYY-MM-DD_HHMMSS.md` - Human-readable report grouped by stage

Unknown earnings timing

FMP does not confirm a bmo/amc session for every earnings row; unconfirmed rows carry `earnings_timing: "unknown"` in Mode A and the price gap calculation assumes the AMC window as a fallback. The Mode A report shows `timing_unknown_count` out of `timing_candidates_total` so this assumption stays visible (Mode B reports `n/a` since timing is inherited from the input JSON). `timing_candidates_total` is the post-budget-trim population that was actually analyzed, not the raw earnings-calendar row count.

Resources

  • `references/pead_strategy.md` - PEAD theory and weekly candle approach
  • `references/entry_exit_rules.md` - Entry, exit, and position sizing rules
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Ships withclaude-trading-skills

Claude Trading Skills started as a personal project to use AI to improve my own trading process. Claude Trading Skills is a Claude Skills-based trading workflow toolkit for time-constrained individual investors.

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