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BaggaT236

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downtrend-duration-analyzer

Analyze historical downtrend durations and generate interactive HTML histograms showing typical correction lengths by sector and market cap.

Revisar el código fuenteVer en GitHub
Precio sin confirmar★ 127 Estrellas de GitHubRegistro actualizado · 4 oct 2026agent-skill

Resumen

Analyze historical downtrend durations and generate interactive HTML histograms showing typical correction lengths by sector and market cap.

Leer documentación completa

Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

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:

  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
Ver fuente ↗
Metadatos del archivo
name: downtrend-duration-analyzer
description: Analyze historical downtrend durations and generate interactive HTML histograms showing typical correction lengths by sector and market cap.
Ver texto original
---
name: downtrend-duration-analyzer
description: Analyze historical downtrend durations and generate interactive HTML histograms showing typical correction lengths by sector and market cap.
---

# 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.

```bash
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

```bash
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

```json
{
  "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

```markdown
# 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

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Licencia: MIT

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Financial research output is not financial advice; require human review before any live investment decision
  • The skill requires an external API key (FMP) which may be a barrier for some users, but this is clearly documented.
  • The SKILL.md excerpt is truncated at the end, but the full file appears complete based on the provided content.
  • Financial research output is not financial advice; require human review before any live investment decision.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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Indexado

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Repositorio fuente
BaggaT236/AI-Trading-Skills
Licencia
MIT
Versión
1.0.0
Último push de GitHub
14 sept 2026
Registro actualizado
4 oct 2026
Ruta de instrucciones
skills/downtrend-duration-analyzer/SKILL.md @ 8d77f8949c76

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

67/100

Prometedor

Confianza

58/100

Do not auto-install

Auditoría

74/100

Requiere revisión

Abrir auditoría completaVer informe de evaluación
  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Financial research output is not financial advice; require human review before any live investment decision
  • The skill requires an external API key (FMP) which may be a barrier for some users, but this is clearly documented.
  • The SKILL.md excerpt is truncated at the end, but the full file appears complete based on the provided content.
  • Financial research output is not financial advice; require human review before any live investment decision.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 67,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "29d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "The skill requires an external API key (FMP) which may be a barrier for some users, but this is clearly documented.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "The SKILL.md excerpt is truncated at the end, but the full file appears complete based on the provided content."
  ],
  "agent_contract": {
    "task_input": "Use downtrend-duration-analyzer in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 66/100 Manual review",
      "Audit: 74/100 Needs review",
      "Safety: 34/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "baggat236-downtrend-duration-analyzer (downtrend-duration-analyzer)",
      "install_command": "npx skills add BaggaT236/AI-Trading-Skills --skill downtrend-duration-analyzer",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "baggat236-downtrend-duration-analyzer",
      "task": "Use downtrend-duration-analyzer in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/baggat236-downtrend-duration-analyzer",
    "api": "https://www.openagentskill.com/api/agent/skills/baggat236-downtrend-duration-analyzer",
    "audit": "https://www.openagentskill.com/skills/baggat236-downtrend-duration-analyzer/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=baggat236-downtrend-duration-analyzer&task=Use%20downtrend-duration-analyzer%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20downtrend-duration-analyzer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20downtrend-duration-analyzer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/baggat236-downtrend-duration-analyzer/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/baggat236-downtrend-duration-analyzer"
  }
}

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Creador
BaggaT236
Indexado por
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