Downtrend Duration Analyzer

Workflow

Step 1: Fetch Historical Price Data

Run the analysis script to fetch OHLC data for a universe of stocks and identify downtrend periods.

python3 skills/downtrend-duration-analyzer/scripts/analyze_downtrends.py \
  --sector "Technology" \
  --lookback-years 5 \
  --output-dir reports/

Step 2: Analyze Downtrend Durations

The script automatically:

1. Identifies local peaks and troughs using rolling window analysis
2. Calculates duration (trading days) and depth (% decline) for each downtrend
3. Segments results by sector and market cap tier (Mega, Large, Mid, Small)
4. Computes summary statistics (median, mean, percentiles)

Step 3: Generate Interactive HTML Visualization

python3 skills/downtrend-duration-analyzer/scripts/generate_histogram_html.py \
  --input reports/downtrend_analysis_*.json \
  --output-dir reports/

This creates an interactive HTML file with:

• Histogram of downtrend durations
• Filters for sector and market cap
• Hover tooltips with percentile information
• Summary statistics table

Step 4: Review Distribution Insights

Load the generated markdown report to interpret the findings:

• Short corrections (5-15 days): Typical pullbacks within uptrends
• Medium corrections (15-40 days): Standard sector rotations
• Extended corrections (40+ days): Trend changes or bear markets

Output Format

JSON Report

{
  "schema_version": "1.0",
  "analysis_date": "2026-03-28T07:00:00Z",
  "parameters": {
    "lookback_years": 5,
    "sector_filter": "Technology",
    "peak_window": 20,
    "trough_window": 20
  },
  "summary": {
    "total_downtrends": 1234,
    "median_duration_days": 18,
    "mean_duration_days": 24.5,
    "p25_duration_days": 10,
    "p75_duration_days": 32,
    "p90_duration_days": 55
  },
  "by_sector": {
    "Technology": {
      "count": 456,
      "median_days": 15,
      "mean_days": 20.3
    }
  },
  "by_market_cap": {
    "Mega": {"count": 200, "median_days": 12},
    "Large": {"count": 300, "median_days": 16},
    "Mid": {"count": 400, "median_days": 22},
    "Small": {"count": 334, "median_days": 28}
  },
  "downtrends": [
    {
      "symbol": "AAPL",
      "sector": "Technology",
      "market_cap_tier": "Mega",
      "peak_date": "2025-01-15",
      "trough_date": "2025-02-10",
      "duration_days": 18,
      "depth_pct": -12.5
    }
  ]
}

Markdown Report

# Downtrend Duration Analysis

**Date**: 2026-03-28
**Lookback**: 5 years
**Sector**: Technology

## Summary Statistics

| Metric | Value |
|--------|-------|
| Total Downtrends | 1,234 |
| Median Duration | 18 days |
| Mean Duration | 24.5 days |
| 25th Percentile | 10 days |
| 75th Percentile | 32 days |
| 90th Percentile | 55 days |

## By Market Cap Tier

| Tier | Count | Median | Mean |
|------|-------|--------|------|
| Mega ($200B+) | 200 | 12 days | 15.2 days |
| Large ($10-200B) | 300 | 16 days | 20.1 days |
| Mid ($2-10B) | 400 | 22 days | 28.4 days |
| Small (<$2B) | 334 | 28 days | 35.6 days |

## Key Insights

1. Larger companies recover faster from corrections
2. Technology sector shows shorter median correction than market average
3. 90% of corrections resolve within 55 trading days

HTML Visualization

Interactive histogram saved to reports/downtrend_histogram_YYYY-MM-DD.html with:

• Plotly.js-based interactive charts
• Sector and market cap dropdown filters
• Duration distribution with bin controls
• Percentile markers (P25, P50, P75, P90)

Reports are saved to reports/ with filenames:

• downtrend_analysis_YYYY-MM-DD_HHMMSS.json
• downtrend_analysis_YYYY-MM-DD_HHMMSS.md
• downtrend_histogram_YYYY-MM-DD_HHMMSS.html

Resources

• scripts/analyze_downtrends.py -- Main analysis script for fetching data and computing downtrend durations
• scripts/generate_histogram_html.py -- HTML visualization generator with interactive histograms
• references/downtrend_methodology.md -- Peak/trough detection algorithms and market cap tier definitions

Key Principles

1. Statistical Rigor: Use robust peak/trough detection to avoid noise-induced false signals
2. Segmentation Matters: Always analyze by sector and market cap; averages hide important differences
3. Realistic Expectations: Use percentiles (not just means) to understand the full distribution of outcomes