Technology•6 min read

Inside VigQuant's Prediction Engine: 12 Models, One Verdict

February 15, 2026

Why Multiple Models?

No single prediction model works in all market conditions. Transformer-based models excel at capturing complex temporal patterns. Statistical models handle mean-reverting markets well. Foundation models pre-trained on billions of time series bring transfer learning that task-specific models can't match.

VigQuant runs 12 models in parallel and combines their predictions using dynamic weighting that adapts to the current market regime.

The Model Architecture

Foundation Models

VigQuant deploys multiple time-series foundation models — large-scale models pre-trained on billions of data points across diverse domains. These models bring transfer learning capabilities that task-specific models can't match, capturing patterns across economic cycles, volatility regimes, and cross-asset correlations.

Deep Learning Models

Purpose-built transformer and attention-based architectures trained specifically on financial time series. These models capture the unique characteristics of market data: non-stationarity, regime shifts, fat tails, and volatility clustering.

Ensemble Methods

Gradient-boosted and statistical models that operate on hand-engineered features — technical indicators, volatility metrics, flow signals, and macro variables. These models excel at capturing non-linear relationships that pure time-series models miss.

Dynamic Weighting

Not all models are equal in all conditions. The weighting system:

  • Tracks each model's recent accuracy by regime
  • Up-weights models that perform well in the current regime
  • Down-weights models that are underperforming
  • Ensures no single model dominates the ensemble

    • In trending markets, momentum-sensitive models and pattern recognition architectures get more weight. In volatile markets, probabilistic models that output uncertainty estimates get more influence.

    Calibrated Confidence

    The ensemble doesn't just output a direction — it produces calibrated probability distributions. When VigQuant says "72% probability of upward movement," that percentage is continuously validated against real outcomes to ensure it actually corresponds to a 72% success rate.

    GPU-Accelerated Inference

    All 12 models run in parallel on GPU infrastructure, typically completing in under 40 seconds. Results are cached cross-user, so if someone else analyzed the same ticker recently, you get near-instant results.

    The prediction engine powers ARES, DEEPARES, and direct prediction commands across all tiers.

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