regime-detection

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.

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.

def compute_adx(
    high: pd.Series, low: pd.Series, close: pd.Series,
    period: int = 14
) -> pd.Series:
    """Average Directional Index."""
    plus_dm = high.diff().clip(lower=0)
    minus_dm = (-low.diff()).clip(lower=0)
    # Zero out when the other is larger
    plus_dm[plus_dm < minus_dm] = 0
    minus_dm[minus_dm < plus_dm] = 0

    tr = pd.concat([
        high - low,
        (high - close.shift(1)).abs(),
        (low - close.shift(1)).abs()
    ], axis=1).max(axis=1)

    atr = tr.ewm(span=period, adjust=False).mean()
    plus_di = 100 * plus_dm.ewm(span=period, adjust=False).mean() / atr
    minus_di = 100 * minus_dm.ewm(span=period, adjust=False).mean() / atr
    dx = 100 * (plus_di - minus_di).abs() / (plus_di + minus_di)
    return dx.ewm(span=period, adjust=False).mean()

3. EMA Slope + Price Position

def trend_direction(close: pd.Series, period: int = 20) -> pd.Series:
    """Returns +1 (uptrend), -1 (downtrend), 0 (neutral)."""
    ema = close.ewm(span=period, adjust=False).mean()
    slope = ema.diff(5)  # 5-bar slope
    above = (close > ema).astype(int)
    direction = pd.Series(0, index=close.index)
    direction[(slope > 0) & (above == 1)] = 1
    direction[(slope < 0) & (above == 0)] = -1
    return direction

4. Bollinger Band Width Percentile

BB width (upper - lower) / middle as a volatility proxy. A "squeeze" (low percentile) often precedes a breakout.

def bb_width_percentile(
    close: pd.Series, period: int = 20,
    std_dev: float = 2.0, lookback: int = 100
) -> pd.Series:
    """Bollinger Band width percentile."""
    sma = close.rolling(period).mean()
    std = close.rolling(period).std()
    width = (2 * std_dev * std) / sma
    return width.rolling(lookback).apply(
        lambda x: pd.Series(x).rank(pct=True).iloc[-1], raw=False
    )

Statistical Approaches

Rolling Hurst Exponent

The Hurst exponent H classifies time series behavior:

• H < 0.4 → Mean-reverting (anti-persistent)
• 0.4 ≤ H ≤ 0.6 → Random walk (no exploitable structure)
• H > 0.6 → Trending (persistent)

Computed via the Rescaled Range (R/S) method. See references/methodology.md for the full derivation.

def hurst_exponent(series: pd.Series, max_lag: int = 50) -> float:
    """Estimate Hurst exponent using R/S method."""
    lags = range(2, max_lag)
    rs_values = []
    for lag in lags:
        chunks = [series.iloc[i:i+lag] for i in range(0, len(series) - lag, lag)]
        rs_list = []
        for chunk in chunks:
            if len(chunk) < lag:
                continue
            mean_val = chunk.mean()
            devs = chunk - mean_val
            cumdev = devs.cumsum()
            r = cumdev.max() - cumdev.min()
            s = chunk.std(ddof=1)
            if s > 0:
                rs_list.append(r / s)
        if rs_list:
            rs_values.append(np.mean(rs_list))
        else:
            rs_values.append(np.nan)
    valid = [(l, r) for l, r in zip(lags, rs_values) if not np.isnan(r)]
    if len(valid) < 5:
        return 0.5
    log_lags = np.log([v[0] for v in valid])
    log_rs = np.log([v[1] for v in valid])
    coeffs = np.polyfit(log_lags, log_rs, 1)
    return coeffs[0]

Change-Point Detection (CUSUM)

Detects abrupt shifts in mean or variance of a return series.

def cusum_test(
    returns: pd.Series, threshold: float = 2.0
) -> list[int]:
    """CUSUM change-point detection on returns.

    Returns indices where regime changes are detected.
    """
    mean_r = returns.mean()
    std_r = returns.std()
    if std_r == 0:
        return []
    s_pos, s_neg = 0.0, 0.0
    changes = []
    for i, r in enumerate(returns):
        z = (r - mean_r) / std_r
        s_pos = max(0, s_pos + z - 0.5)
        s_neg = max(0, s_neg - z - 0.5)
        if s_pos > threshold or s_neg > threshold:
            changes.append(i)
            s_pos, s_neg = 0.0, 0.0
    return changes

Hidden Markov Models

For 2–3 state regime models using hmmlearn. This is optional — all core functionality works with numpy/pandas only.

# Optional: requires `uv pip install hmmlearn`
from hmmlearn import hmm

def fit_hmm_regimes(
    returns: np.ndarray, n_states: int = 2, n_iter: int = 100
) -> tuple[np.ndarray, object]:
    """Fit a Gaussian HMM to return series."""
    X = returns.reshape(-1, 1)
    model = hmm.GaussianHMM(
        n_components=n_states, covariance_type="full", n_iter=n_iter
    )
    model.fit(X)
    states = model.predict(X)
    return states, model

See references/methodology.md for details on feature selection and state interpretation.

Crypto-Specific Considerations

Regime Speed

Crypto regimes change much faster than equities:

Parameter	Equities	Crypto (large cap)	Crypto (micro cap / PumpFun)
ATR lookback	100–200 bars	50–100 bars	20–50 bars
ADX period	14–28	10–14	7–10
Regime persistence	Weeks–months	Days–weeks	Hours–days
Hurst window	200+ bars	100 bars	50 bars

Volume as a Regime Signal

In crypto, volume confirms regime quality:

• High volume + trend → Strong conviction, ride it
• Low volume + trend → Drift, unreliable, reduce size
• High volume + range → Distribution or accumulation, watch for breakout
• Low volume + range → Dead market, skip

PumpFun Micro-Regimes

New token launches follow a stereotyped sequence:

1. Launch pump (minutes): Vertical move, extreme vol, no mean-reversion
2. First dump (minutes–hours): Profit-taking, high vol, trending down
3. Consolidation (hours–days): Low vol range, potential mean-reversion
4. Second wave or death: Either breaks out again (new trend) or fades to zero

Each micro-regime lasts minutes to hours. Use 1-minute bars with 20–50 bar windows.

Combined Regime Classification

def classify_regime(
    vol_percentile: float, adx: float, hurst: float,
    trend_dir: int
) -> dict[str, str]:
    """Classify into the 4-quadrant model."""
    vol_regime = (
        "low" if vol_percentile < 0.30
        else "high" if vol_percentile > 0.70
        else "normal"
    )
    trend_regime = (
        "trending" if adx > 25
        else "ranging" if adx < 20
        else "transitional"
    )
    direction = (
        "up" if trend_dir > 0
        else "down" if trend_dir < 0
        else "neutral"
    )
    mr_regime = (
        "mean_reverting" if hurst < 0.4
        else "trending" if hurst > 0.6
        else "random"
    )
    return {
        "volatility": vol_regime,
        "trend": trend_regime,
        "direction": direction,
        "mean_reversion": mr_regime,
        "quadrant": f"{vol_regime}_vol_{trend_regime}",
    }

Strategy Adaptation

See references/strategy_adaptation.md for the full regime-strategy matrix.

Quick reference:

Current Regime	Action
Low vol + trending up	Full size trend-following, tight stops
High vol + trending	Half size momentum, wide stops
Low vol + ranging	Mean-reversion / grid strategies
High vol + ranging	Reduce to 25% size or sit out
Regime transition	Flatten or reduce to minimum size

Integration with Other Skills

• pandas-ta: Compute ATR, ADX, Bollinger Bands, EMAs
• volatility-modeling: Advanced vol forecasting (GARCH, realized vol)
• strategy-framework: Route signals through regime filter before execution
• position-sizing: Scale position size by regime volatility
• risk-management: Adjust portfolio risk limits per regime

Files

References

• references/methodology.md — Detailed math for Hurst exponent, HMM, change-point detection, and volatility estimation methods
• references/strategy_adaptation.md — Full regime-strategy matrix with position sizing, stop adaptation, and PumpFun micro-regime playbook

Scripts

• scripts/detect_regime.py — Compute regime indicators on live or demo data, classify into 4-quadrant model
• scripts/regime_backtest.py — Compare regime-adaptive vs static strategy on synthetic data with clear regime transitions