Pead Screener

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