Crypto Market Regime Analysis

Hidden Markov Model-based regime classification across all tracked assets. Identifies whether the market is in a risk-on, risk-off, or transitional state.

Current Regime Classifications

BTC

Mean-Reverting

Confidence: +75.5%

Vol 24h: 24.9% · high · Funding: balanced

ETH

Mean-Reverting

Confidence: +54.2%

Vol 24h: 31.5% · high · Funding: balanced

SOL

Trending

Confidence: +95.0%

Vol 24h: 33.8% · high · Funding: balanced

HYPE

Trending

Confidence: +84.4%

Vol 24h: 92.4% · high · Funding: balanced

HIGH_VOLATILITY: HYPE annualized vol 92% — consider reducing leverage and widening stops

Model Parameters

Model Type

random_forest_onnx

Regime Floor

high

Evaluation Interval

What Is Market Regime Analysis?

Market regime analysis uses statistical models to classify the current market environment into distinct states. In crypto markets, regimes typically include:

Risk-On / Accumulation: Markets trending up with strong momentum and increasing volume
Risk-Off / Distribution: Declining prices, rising fear, capital outflows
Transitional / Mean-Reverting: Markets between regimes, often choppy and range-bound

Algo Tick uses a Hidden Markov Model (HMM) trained on funding rates, open interest changes, price momentum, and on-chain metrics to classify the regime in real-time. The model evaluates new data every 5 seconds.