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 B). Use when user asks about PEAD screening, post-earnings drift, earnings gap follow-through, red candle breakout patterns, or weekly earnings momentum setups.

FMP Required

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

PEAD Screener - Post-Earnings Announcement Drift

2. 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

3. Prerequisites

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

4. Quick Start 

# Mode A: FMP earnings calendar (standalone)
python3 skills/pead-screener/scripts/screen_pead.py \
  --lookback-days 14 --min-gap 3.0 --max-api-calls 200 \
  --output-dir reports/

# Mode B: Pipeline from earnings-trade-analyzer output
python3 skills/pead-screener/scripts/screen_pead.py \
  --candidates-json reports/earnings_trade_*.json \
  --min-grade B --output-dir reports/

5. 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/

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

6. Resources

References:

  • skills/pead-screener/references/entry_exit_rules.md
  • skills/pead-screener/references/pead_strategy.md

Scripts:

  • skills/pead-screener/scripts/fmp_client.py
  • skills/pead-screener/scripts/report_generator.py
  • skills/pead-screener/scripts/scorer.py
  • skills/pead-screener/scripts/screen_pead.py