PEAD Screener - Post-Earnings Announcement Drift

SkillCommerce & finance

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.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the PEAD Screener - Post-Earnings Announcement Drift skill

What this skill tells your AI

The instructions your AI receives, as published by baggat236/ai-trading-skills in skills/pead-screener/SKILL.md and read by ahel’s review.

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

Resources

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

Signals

GitHub stars
122
Forks
960
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
pead-screener
Source
github.com/baggat236/ai-trading-skills