pead-screener

Screen post-earnings drift candidates by analyzing weekly candle patterns and generating JSON and Markdown reports.

Updated Mar 25, 2026

Search Tags:#earnings #risk-reward #screener #earnings-calendar #pead #breakout #weekly-candles

Ready to run

npx skills add https://github.com/MileniumTick/skills --skill pead-screener-mileniumtick

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pead-screener
Source: https://github.com/MileniumTick/skills/tree/main/skills/pead-screener
Command: npx skills add https://github.com/MileniumTick/skills --skill pead-screener-mileniumtick

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Automates the screening of post-earnings drift candidates by analyzing weekly candle patterns to identify PEAD breakouts, saving time and improving consistency.

Core Features & Use Cases

Mode A: FMP earnings calendar integration to fetch earnings events and apply min-gap/market-cap filters.
Mode B: accepts earnings-trade-analyzer JSON outputs for grading and weekly-scan.
Weekly candle analysis: identifies red candle pullbacks and breakout signals.
Composite scoring and stage classification; generates JSON and Markdown reports.
Top-N reporting with API usage metrics and methodology references.

Quick Start

Run the PEAD screener in Mode A to fetch the latest earnings calendar, analyze weekly patterns, and generate the PEAD reports.

Frequently Asked Questions about pead-screener

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is PEAD screening and how do weekly candle patterns identify breakouts?▼

PEAD screening identifies post-earnings-announcement drift candidates by analyzing weekly candle patterns to detect red candle pullbacks and subsequent breakouts. It computes composite scores to classify the stage of the drift for structured review.

How do I screen post-earnings drift candidates from an FMP earnings calendar?▼

You can screen post-earnings drift candidates by running Mode A, which fetches earnings events from an FMP earnings calendar. It applies minimum gap and market-cap filters, computes weekly candles, and generates structured JSON and Markdown reports.

Can I use earnings-trade-analyzer JSON outputs for weekly candle pattern analysis?▼

Yes, Mode B accepts earnings-trade-analyzer JSON outputs to perform weekly candle analysis. It grades the inputs, scans for red candle pullbacks and breakouts, and computes composite scores for post-earnings drift candidates.

Do I need any specific dependencies to run a PEAD screener for weekly candle analysis?▼

You need the requests library installed to run the PEAD screener. The Skill uses it to fetch earnings calendar data in Mode A and processes weekly candle patterns to output Top-N reports with API usage metrics.

What is the best way to automate post-earnings drift candidate screening and reporting?▼

Automating post-earnings drift screening is best handled by computing weekly candle patterns to identify breakouts and red candle pullbacks. This Skill generates composite scores, stage classifications, and structured JSON and Markdown reports for consistent review.

What limitations exist when filtering earnings calendar data for PEAD breakouts?▼

Limitations include relying on FMP earnings calendar data availability in Mode A and requiring pre-processed earnings-trade-analyzer JSON outputs for Mode B. Screening accuracy depends on the quality of weekly candle computation and market-cap filter inputs.