Downtrend Duration Analyzer

Overview

Analyze historical price data to identify downtrend periods (peak-to-trough) and build statistical distributions of correction durations. Generate interactive HTML visualizations with histograms segmented by sector and market cap to help traders understand typical recovery timeframes and set realistic expectations for mean reversion strategies.

When to Use

Trader asks about typical correction lengths for a sector or market cap tier
User wants to understand historical drawdown recovery times
Building mean reversion or pullback strategies that need realistic holding period estimates
Comparing correction behavior across different market segments
Setting stop-loss timeouts or position holding period limits

Prerequisites

Python 3.9+
FMP API key (set FMP_API_KEY environment variable or use --api-key)
Required packages: requests, pandas, numpy (standard data analysis stack)

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:

Identifies local peaks and troughs using rolling window analysis
Calculates duration (trading days) and depth (% decline) for each downtrend
Segments results by sector and market cap tier (Mega, Large, Mid, Small)
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

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