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.
npx skills add https://github.com/BaggaT236/AI-Trading-Skills --skill pead-screener
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.
FMP_API_KEY environment variable or pass --api-key) export FMP_API_KEY=your_api_key_here
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.
references/pead_strategy.md for PEAD theory and pattern contextreferences/entry_exit_rules.md for trade management rulesFor each candidate, present:
Based on stages and ratings:
pead_screener_YYYY-MM-DD_HHMMSS.json - Structured results with stage classificationpead_screener_YYYY-MM-DD_HHMMSS.md - Human-readable report grouped by stagereferences/pead_strategy.md - PEAD theory and weekly candle approachreferences/entry_exit_rules.md - Entry, exit, and position sizing rulesThis skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions
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Take baggat236/pead-screener from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.