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. < 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. 3. EMA Slope + Price Position 4. Bollinger Band Width Percentile BB width (upper lower) / middle as a volatility proxy. A "squeeze" (low percentile) often precedes a breakout. 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. Change Point Detection (CUSUM) Detects abrupt shifts in mean or variance of a return series. Hidden Markov Models For 2–3 state regime models using hmmlearn . This is optional — all core functionality works with numpy/pandas only. 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 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

regime-detection