ftd-detector

FTD Detector Skill

Purpose

Detect Follow-Through Day (FTD) signals that confirm a market bottom, using William O'Neil's proven methodology. Generates a quality score (0-100) with exposure guidance for re-entering the market after corrections.

Complementary to Market Top Detector:

• Market Top Detector = defensive (detects distribution, rotation, deterioration)
• FTD Detector = offensive (detects rally attempts, bottom confirmation)

When to Use This Skill

English:

• 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

Japanese:

• 「底打ちした？」「買い戻して良い？」
• 調整局面（3%以上の下落）からのエントリータイミング
• フォロースルーデーやラリーアテンプトについて
• 直近の反発が持続可能か評価したい
• 調整後のエクスポージャー拡大の判断
• Market Top Detectorが高リスク表示の後の底打ちシグナル確認

Difference from Market Top Detector

Execution 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

State Machine

NO_SIGNAL → CORRECTION → RALLY_ATTEMPT → FTD_WINDOW → FTD_CONFIRMED
                ↑              ↓               ↓              ↓
                └── RALLY_FAILED ←─────────────┘     FTD_INVALIDATED

Quality Score (0-100)

Prerequisites

• FMP API Key: Required. Set FMP_API_KEY environment variable or pass via --api-key flag.
• Python 3.9+: With requests library installed.
• API Budget: 4 calls per execution (well within FMP free tier of 250/day).

Output Files

• JSON: ftd_detector_YYYY-MM-DD_HHMMSS.json
• Markdown: ftd_detector_YYYY-MM-DD_HHMMSS.md

Reference Documents

skills/ftd-detector/references/ftd_methodology.md

• O'Neil's FTD rules in detail
• Rally attempt mechanics and day counting
• Historical FTD examples (2020 March, 2022 October)

skills/ftd-detector/references/post_ftd_guide.md

• Post-FTD distribution day failure rates
• Power Trend definition and conditions
• Success vs failure pattern comparison

When to Load References

• First use: Load skills/ftd-detector/references/ftd_methodology.md for full understanding
• Post-FTD questions: Load skills/ftd-detector/references/post_ftd_guide.md
• Regular execution: References not needed - script handles analysis