Statistical Model

Quantitative modeling to estimate outcome likelihood.

Polystrat analyzes crypto price targets using multiple statistical approaches and historical behavior to generate model-based probabilities. These probabilities are designed to support better decisions, giving you a clearer sense of risk before you commit capital.

Probability Engine Overview

This endpoint estimates the likelihood of a crypto asset (e.g. BTC) reaching a given price level within a defined time horizon.

It is designed for probabilistic decision support, not deterministic prediction.

The engine aggregates multiple independent probabilistic perspectives into a single bounded estimate, with explicit confidence signals.

High-level Architecture

The system follows an ensemble-based approach. Instead of relying on a single statistical assumption, it combines several complementary probability engines, each modeling a different market behavior.

The final probability reflects both the estimate itself and the degree of agreement between models.

Volatility Regime Detection

Before aggregation, the engine classifies the current market into a volatility regime.

Regime	Description	Effect
Low	Compressed volatility	Tighter estimates, higher confidence
Normal	Typical market conditions	Standard behavior
High	Elevated or stressed volatility	Wider uncertainty, confidence penalty

High volatility regimes explicitly reduce confidence, even when raw probabilities appear attractive.

Ensemble Aggregation

All model outputs are combined into a single probability estimate.

Core principles:

• No single model dominates across all regimes
• Model disagreement increases uncertainty
• Model alignment tightens confidence intervals

The aggregation step produces both a probability and a model-consensus signal.

Contextual Adjustments

After aggregation, the estimate is adjusted using market context signals.

These adjustments account for:

• Market positioning and sentiment imbalance
• Distance between spot price and target
• Time remaining to expiry

Adjustments are intentionally conservative and designed to dampen crowd-driven overconfidence.

Probability Bounding

The engine never outputs absolute certainty.

Probability bounds are applied based on:

• Target distance (moneyness)
• Time horizon
• Detected volatility regime

This ensures outputs remain probabilistic rather than declarative.

Output Structure

probability · Final bounded probability estimate (0–1)

Represents the final adjusted probability after ensemble aggregation, contextual adjustments, and bounding.

confidence_interval · Lower and upper bounds derived from model dispersion

Indicates the uncertainty range implied by disagreement between internal models.

confidence_score · Model agreement score indicating estimate reliability

Measures how closely aligned the internal models are with each other. This reflects consensus, not predictive certainty.

model_breakdown · Abstracted per-model contribution summary

Provides a high-level summary of how different probabilistic engines contributed to the final estimate, without exposing internal mechanics.

recommendation · Human-readable probability classification

A categorical label derived from the probability range, intended for fast scanning and UI display.

Intended Use

This system is optimized for:

• Scenario comparison
• Risk-weighted decision making
• Probabilistic hedging logic

This engine is not a price oracle, trading signal, or guarantee of outcome.