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Analyze historical downtrend durations and generate interactive HTML histograms showing typical correction lengths by sector and market cap.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
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| name | downtrend-duration-analyzer |
| description | Analyze historical downtrend durations and generate interactive HTML histograms showing typical correction lengths by sector and market cap. |
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.
FMP_API_KEY environment variable or use --api-key)requests, pandas, numpy (standard data analysis stack)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/
The script automatically:
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:
Load the generated markdown report to interpret the findings:
{
"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
}
]
}
# 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
Interactive histogram saved to reports/downtrend_histogram_YYYY-MM-DD.html with:
Reports are saved to reports/ with filenames:
downtrend_analysis_YYYY-MM-DD_HHMMSS.jsondowntrend_analysis_YYYY-MM-DD_HHMMSS.mddowntrend_histogram_YYYY-MM-DD_HHMMSS.htmlscripts/analyze_downtrends.py -- Main analysis script for fetching data and computing downtrend durationsscripts/generate_histogram_html.py -- HTML visualization generator with interactive histogramsreferences/downtrend_methodology.md -- Peak/trough detection algorithms and market cap tier definitions