How does it Work?
The SagaHalla Oracle is designed to reveal the “why” behind every decision. Its deterministic three-layer architecture — Signals → Regimes → Simulations — transforms raw market uncertainty into transparent, risk-adjusted clarity. Every layer is explainable. Every step is traceable.
Five Pillars of Transparency
- Deterministic Statistical Signals — transparent rules, no opaque decision-making.
- Regime Integrity Checks — filters out unstable or decaying environments.
- Forward-Looking Risk — simulations reveal likely paths and tail-risk extremes.
- Conviction Scaling — position size adapts to risk, alignment, and confidence.
- Proof-of-Trade Logging — every input, regime state, and confirmation is recorded.
1. Market Signal Layer
The Oracle begins by detecting high-confidence patterns across price, structure, and volatility. Signals are fully deterministic and include:
- Multi-window trend and structure alignment
- Z-score deviations and return extremes
- Market structure changes and extrema
- Volatility compression & expansion
- Momentum vs mean-reversion autocorrelation
- Trend weakening & exhaustion
These raw signals provide the “what” — but they never act alone.
2. Regime Context Layer
Markets operate in regimes, not in a vacuum. The regime engine evaluates when the environment supports momentum, reversal, accumulation, instability, or decay.
Key Regime Components
- Volatility Regimes — expansion, compression, instability, decay
- Trend Integrity — identifies stable versus failing trends
- Price Integrity — detects structural stability or weakness
The regime layer provides the “when” — preventing trades during chaotic or low-integrity environments.
Regime awareness prevents false confidence.
3. Forward-Path Simulation Layer
Where most systems end, SagaHalla begins. Every potential trade is evaluated through thousands of Monte Carlo paths to estimate:
- Expected return distributions
- Worst-case scenarios and tail risk
- Alignment between future paths and current regime
Simulations answer the “how likely” — exposing the full distribution of outcomes, not a single guess.
4. Transparent Confirmation Logic
A trade executes only when all three layers align:
- Signals indicate directional opportunity
- Regimes support the behavior expected by those signals
- Simulations show favorable asymmetry
Multiple confirmations reduce false entries and adjust sizing based on conviction.
Why This Approach Works
- Minimizes false positives by requiring multi-layer alignment
- Controls risk by filtering chaotic or decaying regimes
- Adapts sizing using confidence, integrity, and forward-path dispersion
- Provides clarity with documented, explainable decisions
Engineered for Explainable Evolution
SagaHalla is hand-tuned today for full explainability. Future versions may incorporate machine learning for optimization only — such as adaptive thresholds or scenario ranking — but never in ways that obscure decision-making.
Explainability remains the core requirement of the system’s design.