# regime-detection

A Claude skill that classifies the current market as trending or ranging and tells you how volatile it is. Useful before you pick a strategy.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 574 installs · ★ 443
- Author: agiprolabs
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/regime-detection/SKILL.md
- Updated: 2026-09-03
- HTML: https://affpapa.org/en/nevibecode/skills/regime-detection

## What it does

The skill computes four things: ATR percentile, ADX trend strength, EMA slope with price position, and Bollinger Band width percentile. Together they place the market in one of four quadrants — clean trend, volatile trend, quiet range, choppy chaos. A rolling Hurst exponent adds a third dimension: below 0.4 means mean reversion, above 0.6 means persistence. CUSUM change-point detection flags where the regime actually flipped.

Everything ships as plain functions on `pandas` and `numpy`: `atr_percentile`, `compute_adx`, `trend_direction`, `bb_width_percentile`, `hurst_exponent`, `cusum_test`. No hidden dependencies. The Hidden Markov Model part is optional and needs `hmmlearn`, installed separately with `uv pip install hmmlearn`; skip it and the rest still runs.

Thresholds are hardcoded: ADX above 25 is a trend, below 20 is a range, ATR above the 75th percentile is high volatility. Trade something that moves differently and you will be editing those numbers yourself. The full R/S derivation sits in `references/methodology.md`.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill regime-detection -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/regime-detection/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/regime-detection/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill regime-detection -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/regime-detection/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill regime-detection -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/regime-detection/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install agiprolabs/claude-trading-skills regime-detection
```

2. **Manually: put the files in place**
- File: `.github/skills/regime-detection/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill regime-detection -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/agiprolabs/claude-trading-skills --path skills/regime-detection
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/regime-detection/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill regime-detection -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/regime-detection/SKILL.md

## When to use it
- tell a trend from a range before switching a strategy on
- cut position size when volatility clears the 75th percentile
- widen stops in high-vol regimes and tighten them in quiet trends
- spot the moment a regime flips with CUSUM on a return series
- separate mean reversion from a random walk using the Hurst exponent

## Example prompts
- compute the ATR percentile and ADX on daily BTC candles for the past year and tell me the current regime
- run hurst_exponent on the return series and say whether price mean-reverts
- find regime change points with cusum_test on hourly returns and plot them

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 9eb53f3aafad659c.

BEGIN THIRD-PARTY FILE 9eb53f3aafad659c
````text
---
name: regime-detection
description: Market regime identification using volatility clustering, trend detection, and statistical methods for adaptive trading
---

# Regime Detection

Identify the current market regime so you can pick the right strategy, size positions correctly, and avoid deploying trend-following logic in a ranging market (or vice versa).

## Why Regime Detection Matters

Every strategy has a "home regime." A momentum strategy prints money in a clean uptrend but bleeds in a choppy range. A mean-reversion grid thrives in low-volatility consolidation but gets steamrolled by a trending breakout. Regime detection tells you **which playbook to use right now**.

Key benefits:
- **Strategy selection**: Route signals to the right strategy for the current environment
- **Position sizing**: Reduce exposure in hostile regimes, increase in favorable ones
- **Stop adaptation**: Wider stops in high-vol regimes, tighter in low-vol trends
- **Drawdown control**: Sit out "danger zone" regimes (high vol + no trend)

## Core Regime Dimensions

Two orthogonal axes define the four-quadrant regime model:

| | Low Volatility | High Volatility |
|---|---|---|
| **Trending** | Q1: Clean trend — best for trend following | Q2: Volatile trend — momentum with caution |
| **Ranging** | Q3: Quiet range — mean-reversion paradise | Q4: Choppy chaos — reduce or sit out |

A third dimension — **mean-reversion tendency** (Hurst exponent) — refines Q3 by telling you how reliably price reverts.

## Simple Approaches (No ML Required)

### 1. ATR Volatility Percentile

Rank the current ATR against its own recent history to get a 0–100 percentile score.

```python
import pandas as pd
import numpy as np

def atr_percentile(
    high: pd.Series, low: pd.Series, close: pd.Series,
    atr_period: int = 14, lookback: int = 100
) -> pd.Series:
    """ATR percentile rank over a rolling window."""
    tr = pd.concat([
        high - low,
        (high - close.shift(1)).abs(),
        (low - close.shift(1)).abs()
    ], axis=1).max(axis=1)
    atr = tr.rolling(atr_period).mean()
    return atr.rolling(lookback).apply(
        lambda x: pd.Series(x).rank(pct=True).iloc[-1], raw=False
    )
```

- **< 25th percentile** → Low volatility regime
- **25th–75th** → Normal volatility
- **> 75th percentile** → High volatility regime

### 2. ADX Trend Strength

ADX above 25 signals a trending market; below 20 signals a range.

```python
````
END THIRD-PARTY FILE 9eb53f3aafad659c

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