Regime Detection

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:

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:

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