ftd-detector

Detect and quantify Follow-Through Day signals for S&P 500 and NASDAQ using historical price data.

1|Updated Apr 6, 2026
One-click install
npx skills add https://github.com/kavi-lin/stock --skill ftd-detector-kavi-lin
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ftd-detector
Source: https://github.com/kavi-lin/stock/tree/main/skills/ftd-detector
Command: npx skills add https://github.com/kavi-lin/stock --skill ftd-detector-kavi-lin

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Detects Follow-Through Day signals and computes a multi-factor quality score to help traders time market bottoms and manage exposure.

Core Features & Use Cases

  • Dual-index rally tracking (S&P 500 + NASDAQ) to confirm bottom signals and measure cross-index agreement
  • Swing-low identification, rally-tracking, FTD window detection, and post-FTD health monitoring with a 0-100 quality score
  • Automated JSON/Markdown reports for decision support and audit trails

Quick Start

Run the FTD Detector to generate JSON and Markdown reports for the latest market.

Frequently Asked Questions about ftd-detector

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

FAQPage Schema
How do I detect a Follow-Through Day signal to confirm a market bottom?

Dual-index tracking measures cross-index agreement between S&P 500 and NASDAQ to confirm bottom signals. It computes a multi-factor 0-100 quality score by monitoring post-FTD health, swing-low identification, and rally tracking, providing a quantifiable confidence metric for market bottoms.

Can I use Python to automate FTD detection with FMP API price data?

You need Python 3.8+ and a valid FMP API key to fetch historical price data. The detection system applies a state-machine model to identify rally attempts and FTD windows, then outputs automated JSON and Markdown reports for decision support and audit trails.

What is a multi-index health scoring model for FTD analysis?

A multi-index health scoring model evaluates Follow-Through Day signals by quantifying cross-index agreement between S&P 500 and NASDAQ. It produces a 0-100 quality score based on swing-low identification, rally tracking, and post-FTD health monitoring to inform exposure decisions.

How do I generate JSON and Markdown reports for FTD market bottom analysis?

JSON and Markdown reports for FTD analysis are generated automatically after processing historical price data. The system identifies swing lows and FTD windows across S&P 500 and NASDAQ, then outputs structured reports containing the multi-factor quality score for decision support and audit trails.

When should I not rely on a Follow-Through Day signal for market timing?

FTD signals should not be relied upon in isolation when cross-index agreement between S&P 500 and NASDAQ is absent. The multi-factor 0-100 quality score evaluates post-FTD health; low scores indicate weak rally confirmation, suggesting the market bottom may lack sufficient momentum for exposure decisions.