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Join us on DiscordOur quant regime-probability engine — a Hidden Markov Model (HMM) trained on years of hourly market data — classifies the whole market and every Hyperliquid crypto perp into one of 6 short-term regimes, with calibrated probabilities for direction, volatility and liquidation risk, refreshed every 15 minutes. Nothing is a black box: every number ships with the features, posteriors and calibration that produced it.
explain block: feature z-scores, raw state posteriors, the hysteresis rule, raw-vs-calibrated confidence. If you can't audit a probability, you shouldn't trade on it.{
"scope": "BTC",
"symbol": "BTC",
"timestamp": "2026-10-04T08:50:51.233000Z",
"horizon": "24h",
"regime": {
"label": "squeeze",
"name": "Squeeze",
"id": 5,
"confidence": 0.5483,
"confidence_raw": 0.7316,
"label_raw": "squeeze",
"candles_in_regime": 1,
"in_regime_since": "2026-10-04T07:00:00Z",
"pending": null,
"hysteresis": {
"margin": 0.1,
"bars": 2,
"override": 0.7,
"incumbent_floor": 0.1,
"min_dwell_bars": 0
}
},
"probabilities": {
"directional": {
"strong_down": 0.0835,
"mild_down": 0.2416,
"flat": 0.3775,
"mild_up": 0.2135,
"strong_up": 0.084,
"confidence": 0.0954
},
"volatility": {
"low": 0.496,
"medium": 0.3286,
"high": 0.1754,
"confidence": 0.0726
},
"funding": {
"falls": 0.2604,
"stable": 0.3257,
"rises": 0.4139,
"confidence": 0.0161
},
"liquidation_risk": {
"low": 0.496,
"medium": 0.3286,
"high": 0.1754,
"confidence": 0.0726
},
"open_interest": {
"contracting": 0.3251,
"neutral": 0.3774,
"expanding": 0.2975,
"confidence": 0.0045
},
"regime_transitions": {
"stays_same": 0.5635,
"to_range_low_vol": 0.2129,
"to_strong_trend_bull": 0.1371,
"to_strong_trend_bear": 0.0661,
"confidence": 0.3393
}
},
"meta": {
"model_version": "2.0.0",
"model_family": "hmm",
"last_retrain": "2026-06-20T17:03:26.739290Z",
"data_staleness_ms": 3051233,
"feature_coverage": 1.0,
"insufficient_history": false,
"status": "ok",
"backfilled": false
},
"explain": {
"features": {
"ret_1h_z": 0.24967739416809998,
"ret_24h_z": 0.15572159769964955,
"vol_ewma_z": -1.869367374891609,
"vol_ratio_log": -4.0,
"bb_width_pctile": -0.33333333333333326,
"rel_btc_24h_z": 0.0,
"funding_z": 0.3910758582856916,
"trend_7d": 0.042667954684265985
},
"label_posteriors": {
"strong_trend_bull": 0.0,
"strong_trend_bear": 0.0,
"range_low_vol": 0.26836,
"choppy_high_vol": 0.0,
"vol_spike": 0.0,
"squeeze": 0.73164
},
"hysteresis": {
"rule": "challenger > incumbent + 0.1 for 2 bars, or challenger > 0.7 instant, or incumbent < 0.1 (collapsed)",
"pending_challenger": null
},
"calibration": {
"confidence_raw": 0.731636546684749,
"confidence_calibrated": 0.5482581827334237,
"scale_applied": 1.0
},
"thresholds_resolved": {
"sigma_1h": 0.002277,
"mild": "±0.28%",
"strong": "±1.12%"
}
}
}
The HMM is a nowcast: it tells you which regime the market is in right now, within hours of a state change. During vol_spike, 73–79% of coin-hours across the entire Hyperliquid universe saw elevated volatility over the next 24h. Use it to size positions, widen stops and set liquidation buffers across your whole book.
A 24h-momentum strategy that is flat over all hours splits sharply by regime: mean-reversion worked during bear and vol_spike on every dataset we tested (BTC, majors, full HL universe), momentum leaned positive in squeeze. Long-BTC-only-during-bull historically halved max drawdown vs a 200-day trend filter at similar return. Use the regime to pick which strategy runs, not which way to bet.
P(BTC up next 24h | bull) was 52.8% vs a 52.1% base rate — no edge. And don't short bear: by the time the model flips bearish, ~−2.5% has already happened and the next 7 days averaged positive. The historical timeline above matches price so well because the regime is computed from the move already underway — those bands are recognitions, not calls. No regime model (ours or anyone's) predicts short-horizon direction; vendors who claim otherwise aren't showing you their base rates.
GET /api/v1/quant/regimes/history and score it yourself. Calibrated forecast heads (volatility, liquidation-cascade risk, squeeze resolution) with published scorecards are the next release on this engine.
curl -H "X-API-Key: cdk_live_yourkey" \ "https://cryptodataapi.com/api/v1/quant/market?horizon=24h"
Market-wide regime with probabilities; /quant/coins/{symbol} is the per-coin read and /quant/regimes/history the 2020-to-now Parquet history.
The HMM regime is a nowcast, not a forecast — its measured value is as a strategy SELECTOR: run trend systems in trend regimes, mean-reversion in ranges, and cut size in volatility spikes. That selection is exactly what the 2020-to-now history lets you test.
Build me a regime-switching strategy on the CryptoDataAPI quant regimes.
Read the live market regime from /api/v1/quant/market and per-coin regimes from /api/v1/quant/coins/{symbol}. Assign one sub-strategy per regime — trend-following in the two trend states, mean-reversion in range/low-vol, reduced size or flat in choppy and volatility-spike states — and define what happens to an open position on a regime change.
Then backtest on the point-in-time history at /api/v1/quant/regimes/history (Parquet, 2020 to now, no hindsight relabeling) joined to /api/v1/backtesting/klines.
Compare against running the single best sub-strategy all the time. Report per-regime hit rates. If the switching does not beat the best single strategy after fees, the regimes are not adding selection value — say so.