pead-screener

Q: How do I install pead-screener?

Run npx skills add https://github.com/tradermonty/claude-trading-skills --skill pead-screener in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/pead-screener/ (personal) or .claude/skills/pead-screener/ (project).

by tradermontyProductivity & Automation

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.

Installs: 3.3K
Last 24h: +19
Trending rank: #6053
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/tradermonty/claude-trading-skills --skill pead-screener

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads pead-screener automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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

Read the generated JSON and Markdown reports
Load references/pead_strategy.md for PEAD theory and pattern context
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

FAQ

What does the pead-screener skill do?

How do I install pead-screener?

Run npx skills add https://github.com/tradermonty/claude-trading-skills --skill pead-screener in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/pead-screener/ (personal) or .claude/skills/pead-screener/ (project).

Which AI agents can use pead-screener?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is pead-screener safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains pead-screener?

It is published by tradermonty in tradermonty/claude-trading-skills. It has 3,326 recorded installs.