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Regime Detection

Regime detection identifies the current market "behavior mode" (trending, ranging, volatile). Different strategies work in different regimes.

Related:

Why Regimes Matter

Markets cycle through distinct behavioral phases:

Regime Characteristics Strategy Implication
Low Volatility / Ranging Price oscillates in a band Mean reversion works; trend-following fails
Trending Directional momentum Trend-following works; mean reversion fails
High Volatility / Crisis Large unpredictable swings Reduce size or stay out

A strategy that profits in one regime may lose in another. Regime detection helps you:

  1. Filter trades: Only enter when conditions match your strategy
  2. Adjust sizing: Reduce risk in unfavorable regimes
  3. Detect breakouts: Identify when the market is transitioning

Methods

1. Hidden Markov Model (HMM)

What it does: Classifies each bar into one of N hidden "states" based on return and volatility patterns.

How it works:

  1. Assumes the market switches between N underlying states
  2. Each state has characteristic mean return and volatility
  3. Uses observed data to estimate which state is currently active

Example:

mtdata-cli regime_detect EURUSD --timeframe H1 --method hmm --params "n_states=2"

Output:

summary:
  last_state: 0
  state_shares:
    0: 0.623
    1: 0.377
  state_sigma:
    0: 0.000303   # Low volatility state
    1: 0.000739   # High volatility state

Interpretation:

  • State 0: Low volatility (σ = 0.0003) — ranging/quiet market
  • State 1: High volatility (σ = 0.0007) — trending/active market
  • Currently in State 0 (low volatility)

Parameters:

Parameter Default Description
n_states 2 Number of regimes to detect

When to use:

  • Ongoing regime classification
  • Filtering strategies by market type
  • Multi-day to multi-week analysis

2. Change-Point Detection (BOCPD)

What it does: Detects moments when the market's statistical properties changed.

How it works:

  • Bayesian Online Change Point Detection
  • Estimates probability that each bar marks a regime transition
  • Doesn't classify regimes—just detects when changes occur

Example:

mtdata-cli regime_detect EURUSD --timeframe H1 --method bocpd --threshold 0.5 --detail summary

Output:

summary:
  last_cp_prob: 0.471
  max_cp_prob: 0.471
  change_points_count: 0

Interpretation:

  • last_cp_prob: 0.471 — 47% probability that a regime change just occurred
  • Below threshold (0.5), so not flagged as a change point
  • Higher probabilities indicate likely structural breaks

Parameters:

Parameter Default Description
threshold 0.5 Probability cutoff to flag a change point
lookback 300 Historical bars to analyze

When to use:

  • Detecting breakouts
  • Alerting on market structure changes
  • Invalidating stale forecasts

3. Markov-Switching AR (MS-AR)

What it does: Combines HMM with autoregressive modeling. Each regime has its own AR parameters.

Example:

mtdata-cli regime_detect EURUSD --timeframe H1 --method ms_ar --params "k_regimes=2 order=1"

When to use:

  • When regime changes affect both mean and autocorrelation structure
  • Academic research contexts

4. Clustering (clustering)

What it does: Groups bars into regimes using distance-based clustering (e.g. KMeans on return/volatility features). Does not assume a hidden Markov structure.

Example:

mtdata-cli regime_detect EURUSD --timeframe H1 --method clustering --params "n_clusters=3"

When to use:

  • Exploratory regime discovery without a parametric model
  • When HMM convergence is unstable

Practical Strategies

Strategy 1: Regime Filter

Only enter trend-following trades when HMM detects high-volatility state:

# Check current regime
mtdata-cli regime_detect EURUSD --timeframe H1 --method hmm --params "n_states=2"

# If state_sigma shows current state is high-volatility:
#   → Enable trend-following entries
# If current state is low-volatility:
#   → Disable trend-following or switch to mean reversion

Strategy 2: Change-Point Alert

Monitor for regime transitions and reduce exposure when detected:

# Check for recent change points
mtdata-cli regime_detect EURUSD --timeframe H1 --method bocpd --threshold 0.6

# If last_cp_prob > 0.6:
#   → Tighten stops
#   → Reduce position size
#   → Consider closing positions

Strategy 3: Regime-Conditional Barrier Analysis

Run barrier optimization separately for each regime:

# In low-volatility regime: tighter barriers
mtdata-cli forecast_barrier_optimize EURUSD --timeframe H1 --horizon 12 \
  --tp-min 0.15 --tp-max 0.5 --sl-min 0.1 --sl-max 0.4

# In high-volatility regime: wider barriers
mtdata-cli forecast_barrier_optimize EURUSD --timeframe H1 --horizon 12 \
  --tp-min 0.5 --tp-max 2.0 --sl-min 0.3 --sl-max 1.5

Output Formats

Compact Output (Default)

mtdata-cli regime_detect EURUSD --timeframe H1 --method hmm --detail compact

Shows regime segments: start time, end time, duration, state ID.

Summary Output

mtdata-cli regime_detect EURUSD --timeframe H1 --method hmm --detail summary

Shows aggregate statistics: current state, state distributions, volatility per state.

Full Output

mtdata-cli regime_detect EURUSD --timeframe H1 --method hmm --detail full

Shows per-bar state assignments and probabilities.

Quick Reference

Task Command
Classify regimes (2 states) mtdata-cli regime_detect EURUSD --method hmm --params "n_states=2"
Classify regimes (3 states) mtdata-cli regime_detect EURUSD --method hmm --params "n_states=3"
Detect change points mtdata-cli regime_detect EURUSD --method bocpd --threshold 0.5
Markov-switching AR mtdata-cli regime_detect EURUSD --method ms_ar --params "k_regimes=2"
Clustering mtdata-cli regime_detect EURUSD --method clustering --params "n_clusters=3"

See Also