FTD Detector

Detects Follow-Through Day (FTD) signals for market bottom confirmation using William O’Neil’s methodology. Dual-index tracking (S&P 500 + NASDAQ) with state machine for rally attempt, FTD qualification, and post-FTD health monitoring. Use when user asks about market bottom signals, follow-through days, rally attempts, re-entry timing after corrections, or whether it’s safe to increase equity exposure. Complementary to market-top-detector (defensive) - this skill is offensive (bottom confirmation).

FMP Required

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

FTD Detector identifies market bottom signals using William O’Neil’s Follow-Through Day methodology. It tracks S&P 500 and NASDAQ/QQQ simultaneously through a state machine that progresses from correction to rally attempt to FTD confirmation, with post-FTD health monitoring including distribution day counting, invalidation detection, and power trend analysis.

2. When to Use

  • User asks “Is the market bottoming?” or “Is it safe to buy again?”
  • User observes a market correction (3%+ decline) and wants re-entry timing
  • User asks about Follow-Through Days or rally attempts
  • User wants to assess if a recent bounce is sustainable
  • User asks about increasing equity exposure after a correction
  • Market Top Detector shows elevated risk and user wants bottom signals

3. Prerequisites

  • FMP API Key: Required. Set FMP_API_KEY environment variable or pass via --api-key flag.
  • Python 3.8+: With requests library installed.
  • API Budget: 4 calls per execution (well within FMP free tier of 250/day).

4. Quick Start 

python3 skills/ftd-detector/scripts/ftd_detector.py --api-key $FMP_API_KEY

5. Workflow

Phase 1: Execute Python Script

Run the FTD detector script:

python3 skills/ftd-detector/scripts/ftd_detector.py --api-key $FMP_API_KEY

The script will:

  1. Fetch S&P 500 and QQQ historical data (60+ trading days) from FMP API
  2. Fetch current quotes for both indices
  3. Run dual-index state machine (correction → rally → FTD detection)
  4. Assess post-FTD health (distribution days, invalidation, power trend)
  5. Calculate quality score (0-100)
  6. Generate JSON and Markdown reports

API Budget: 4 calls (well within free tier of 250/day)

Phase 2: Present Results

Present the generated Markdown report to the user, highlighting:

  • Current market state (correction, rally attempt, FTD confirmed, etc.)
  • Quality score and signal strength
  • Recommended exposure level
  • Key watch levels (swing low, FTD day low)
  • Post-FTD health (distribution days, power trend)

Phase 3: Contextual Guidance

Based on the market state, provide additional guidance:

If FTD Confirmed (score 60+):

  • Suggest looking at leading stocks in proper bases
  • Reference CANSLIM screener for candidate stocks
  • Remind about position sizing and stops

If Rally Attempt (Day 1-3):

  • Advise patience, do not buy ahead of FTD
  • Suggest building watchlists

If No Correction:

  • FTD analysis is not applicable in uptrend
  • Redirect to Market Top Detector for defensive signals

6. Resources

References:

  • skills/ftd-detector/references/ftd_methodology.md
  • skills/ftd-detector/references/post_ftd_guide.md

Scripts:

  • skills/ftd-detector/scripts/fmp_client.py
  • skills/ftd-detector/scripts/ftd_detector.py
  • skills/ftd-detector/scripts/post_ftd_monitor.py
  • skills/ftd-detector/scripts/rally_tracker.py
  • skills/ftd-detector/scripts/report_generator.py