Access structured crypto forecast objects through APIs, webhooks, dashboards, and forecast archives — with confidence scoring, calibration tracking, data freshness awareness, and historical performance monitoring.
Designed for institutions that need probabilistic outputs they can evaluate, monitor, and integrate into existing workflows.
Forward-looking crypto estimates are only useful when teams can understand coverage, confidence, calibration, data freshness, historical performance, and model limitations. Amonhen is built to make probabilistic forecasts easier to evaluate, integrate, and monitor.
Institutions need to understand when a forecast is supported, low-confidence, or unavailable.
Forecasts should be archived and compared against realized outcomes over time.
Teams need consistent forecast objects, clean APIs, webhooks, coverage metadata, and documentation.
Use existing onchain, market, derivatives, stablecoin, liquidity, exchange, and portfolio datasets from data providers and client systems.
Transform flows, volatility, funding pressure, peg stress, liquidity conditions, concentration, correlations, and route context into forecasting features.
Generate probabilities, expected ranges, volatility estimates, risk estimates, regimes, scenarios, and confidence scores.
Adjust confidence based on data quality, liquidity, asset history, venue coverage, freshness, model calibration, and forecast horizon.
Store forecast history, compare against realized outcomes, and track calibration, error, and coverage over time.
Amonhen complements existing data platforms and analytics workflows rather than replacing them.
One typed object model spans every module — each with defined horizons, an output type, and explicit confidence support.
| Forecast object | Module | Description | Horizons | Output type | Confidence |
|---|---|---|---|---|---|
| Directional probability | Token Forecasts | Probability of positive or negative price movement | 1h · 4h · 24h · 7d · 30d | Probability | Yes |
| Expected return range | Token Forecasts | Forecasted return interval | 1h · 4h · 24h · 7d · 30d | Range estimate | Yes |
| Realized volatility forecast | Derivatives Forecasts | Expected realized volatility range | 1d · 7d · 30d | Range estimate | Yes |
| Funding regime forecast | Derivatives Forecasts | Probability of funding pressure normalizing, overheating, or changing regime | 4h · 24h · 7d | Regime probability | Yes |
| Liquidation cascade probability | Derivatives Forecasts | Probability of liquidation-driven stress | 1h · 4h · 24h | Probability / risk status | Yes |
| Depeg probability | Stablecoin Risk | Probability of peg deviation outside monitoring band | 1h · 24h · 7d · 30d | Probability | Yes |
| Repeg probability | Stablecoin Risk | Probability of returning to peg range after deviation | 1h · 24h · 7d | Probability / status | Yes |
| Route-risk estimate | Stablecoin Risk | Forward-looking route-level risk estimate | 1h · 24h | Risk estimate | Yes |
| Portfolio drawdown probability | Portfolio & Trade | Probability of portfolio breaching drawdown threshold | 24h · 7d · 30d | Probability | Yes |
| Transaction risk estimate | Portfolio & Trade | Risk estimate for selected transaction context | 1h · 24h | Risk estimate | Yes |
Amonhen does not treat every forecast as equally reliable. Forecast confidence is adjusted based on coverage, liquidity, asset history, data freshness, model calibration, forecast horizon, and market regime stability.
Input completeness and reliability across providers.
Depth and stability of tradable liquidity.
Length and richness of historical record.
Recency of the inputs behind a forecast.
Uncertainty grows with forecast horizon.
How past probabilities matched realized outcomes.
Deep history, strong liquidity, fresh data, stable model calibration.
Sufficient data, changing volatility, moderate liquidity, mixed input quality.
Limited history, thin liquidity, unstable regime, weaker coverage.
Insufficient data quality or coverage — no forecast is issued.
Every forecast can be archived and evaluated against what actually occurred. Amonhen supports calibration tracking, error monitoring, confidence distribution analysis, and historical performance review.
Sample values shown for interface demonstration only.
A calibrated forecast system should be evaluated by how its probability estimates compare to realized outcomes over time.
| Asset / market | Module | Supported horizons | Data coverage | Data freshness | Confidence status | Coverage notes |
|---|---|---|---|---|---|---|
| BTC | Token + Derivatives | 1h · 4h · 24h · 7d · 30d | High | Fresh | High confidence | Deep liquidity and extended history |
| ETH | Token + Derivatives | 1h · 4h · 24h · 7d · 30d | High | Fresh | High confidence | Deep liquidity and extended history |
| SOL | Token + Derivatives | 1h · 4h · 24h · 7d · 30d | High | Fresh | Medium-high confidence | Higher volatility regime |
| PEPE | Token | 4h · 24h · 7d | Medium | Fresh | Low confidence | Limited history and thin liquidity |
| USDC | Stablecoin Risk | 1h · 24h · 7d · 30d | High | Fresh | High confidence | Broad venue and chain coverage |
| USDe | Stablecoin Risk | 1h · 24h · 7d | Medium | Fresh | Medium confidence | More complex collateral and liquidity structure |
Amonhen exposes structured forecast objects through APIs and webhooks for institutional platforms, dashboards, data workflows, and embedded product experiences.
Every forecast object carries its value, confidence, regime, driver attribution, generation time, and disclaimer.
{ "asset": "ETH", "module": "token_forecasts", "horizon": "24h", "forecast_type": "directional_probability", "probability_positive_move": 0.61, "expected_return_range": { "lower": -0.015, "upper": 0.034 }, "volatility_forecast": "elevated", "confidence": "medium", "regime": "trend_continuation", "attribution": [ "positive_dex_flow", "neutral_cex_flow", "funding_pressure_normalizing", "liquidity_depth_stable" ], "generated_at": "2026-07-02T09:42:00Z", "disclaimer": "model_generated_informational_estimate" }
Subscribe to material changes in model output, confidence, coverage, and data freshness — delivered to your systems as they occur.
Notify when model output changes materially.
Notify when forecast confidence changes.
Notify when a stablecoin risk estimate changes.
Notify when portfolio drawdown probability changes.
Notify when data freshness affects coverage.
Notify when coverage status changes for an asset or market.
A forecast archive helps institutional teams evaluate model performance, review historical outputs, and support internal governance workflows.
| Timestamp | Asset / market | Forecast object | Horizon | Forecast value | Confidence | Realized outcome | Status |
|---|---|---|---|---|---|---|---|
| 2026-07-02 09:42Z | ETH | Directional probability | 24h | 0.61 | medium | positive move | aligned |
| 2026-07-02 06:10Z | BTC | Realized volatility | 7d | 42–51% | high | 47% realized | within range |
| 2026-07-01 21:30Z | USDC | Depeg probability | 24h | 1.8% | high | no depeg | aligned |
| 2026-07-01 18:05Z | SOL | Directional probability | 24h | 0.55 | medium | negative move | miss |
| 2026-07-01 12:00Z | demo_portfolio | Drawdown probability | 7d | 18% (>10%) | medium | −4.2% max | no breach |
Integrate structured forecast objects into dashboards, internal tools, embedded widgets, and data workflows.
Monitor confidence changes, forecast performance, calibration, data freshness, and model coverage.
Embed neutral forecasted risk estimates and confidence labels into product experiences.
Use probabilistic forecast objects as decision-support inputs inside existing research and risk workflows.
Track stablecoin risk forecasts, collateral stress estimates, route-risk changes, and coverage conditions.
Structured objects, confidence scoring, and an auditable archive — not a black-box prediction feed.
Amonhen provides structured forecast objects, confidence scoring, calibration tracking, historical archives, APIs, and webhooks for institutional crypto workflows.