Using Hidden Markov Models for Crypto Regime Detection

Most market regime classifiers use simple threshold rules: SMA above 200 = bull, below = bear. These work but they're noisy and laggy. Hidden Markov Models (HMMs) offer a probabilistic alternative that can detect regime transitions 6-12 hours earlier.

Here's how we built one.

Why HMMs for Regime Detection

Markets transition between regimes (bull/bear/chop) without announcing it. You can observe symptoms — volatility changes, funding rate shifts, sentiment swings — but the underlying regime is hidden. This is exactly what HMMs are designed for: inferring hidden states from observable emissions.

Architecture

Our HMM has:

The Observations

Each observation is discretized from continuous market data:

``python

Simplified observation generation

def make_observation(snapshot):

return {

'sma_ratio': 'bullish' if snapshot.sma50 > snapshot.sma200 else 'bearish',

'volatility': 'high' if snapshot.atr20/snapshot.atr90 > 1.3 else 'low',

'funding': 'positive' if snapshot.funding_rate > 0.01 else 'negative' if snapshot.funding_rate < -0.01 else 'neutral',

'fear_greed': 'greed' if snapshot.fg > 60 else 'fear' if snapshot.fg < 40 else 'neutral',

'volume': 'high' if snapshot.volume_ratio > 1.2 else 'low',

'dominance': 'rising' if snapshot.btc_dom_delta > 0.5 else 'falling' if snapshot.btc_dom_delta < -0.5 else 'flat',

}

`

Training with Baum-Welch

The Baum-Welch algorithm (a special case of EM) estimates the transition and emission probabilities:

`

Transition matrix (what we learned):

→ Bull → Bear → Chop

Bull: [ 0.017 0.821 0.162 ]

Bear: [ 0.012 0.935 0.053 ]

Chop: [ 0.089 0.456 0.455 ]

`

Key finding: Bear states are extremely sticky (93.5% self-transition probability). Once the market enters a bear regime, it tends to stay there. Bull detections are transient (1.7% stay probability) — the HMM is detecting bounces within bear markets rather than true regime shifts.

Results vs Weighted Classifier

We compared the HMM against our 10-signal weighted ensemble:

| Metric | HMM | Weighted Ensemble |

|---|---|---|

| Agreement rate | 68.4% | — |

| Bear detection | Earlier by 6-12h | More stable |

| Bull detection | Noisy (false positives) | More reliable |

| Chop detection | Poor (rare state) | Better (threshold-based) |

The HMM excels at detecting transitions early. The weighted ensemble excels at steady-state classification. We use both:

  • Weighted ensemble → primary regime classification (what the API returns)
  • HMM → early warning signal for upcoming transitions (available in intelligence brief)

    • Accessing the HMM

    The HMM regime is available via the Regime API (Pro tier):

    `bash

    curl -H "Authorization: Bearer YOUR_KEY" \

    https://getregime.com/api/v1/intelligence/hmm-regime

    `

    Returns:

    `json

    {

    "hmmRegime": "bear",

    "hmmConfidence": 0.89,

    "weightedRegime": "bear",

    "agreement": true,

    "transitionProbability": 0.065,

    "lastRetrained": "2026-03-26T04:00:00Z"

    }

    `

    When agreement is false, a regime transition may be imminent. The transitionProbability indicates how likely the current regime is to change in the next evaluation window.

    Key Takeaways

  • HMMs detect transitions earlier but have more false positives in steady state
  • Bear states are sticky — once bear, 93.5% chance of staying bear
  • Bull "detections" are often bounces — the HMM finds temporary rallies within larger bear trends
  • Combining HMM + threshold classifier gives the best of both worlds

    • Free regime endpoint (weighted ensemble): curl https://getregime.com/api/v1/market/regime`

    Full intelligence including HMM: Pro tier

    GitHub: getregime.com