Research Infrastructure for Markets Beyond Historical Data

A generative market environment for stress testing, validation, and robust quant research.

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Overcome Back-Testing Pitfalls with Generative AI

Drawbacks of Traditional Monte Carlo Simulations

Monte Carlo simulations rely on a static distribution, which can underplay tail risks and overlook key insights.

Underestimating Volatility and Drawdown

Conventional risk metrics often downplay tail risks, leaving investors vulnerable during periods of market stress.

Underestimating Volatility and Drawdown

Conventional risk metrics often downplay tail risks, leaving investors vulnerable during periods of market stress.

Reliance Solely on Historical Data

Relying on historical data and static assumptions limits the ability to capture non-linear correlations and incorporate unique perspectives.

Reliance Solely on Historical Data

Relying on historical data and static assumptions limits the ability to capture non-linear correlations and incorporate unique perspectives.

Generative Market Environment

We build diffusion-based generative models that learn a joint representation of market dynamics.

Generate realistic cross-asset market paths

Stress test beyond observed history

Condition on volatility, macro states, correlation regimes

Frequently Asked Questions

How does your AI architecture work?

The generative AI we use is our proprietary architecture. It is a development of the denoising-diffusion and cross-attention mechanisms concepts adapted to market data modeling challenges. At high level, it learns high-dimensional sparse

How can we trust your AI?

Can you prove that synthetic data make trading algorithm perform better?

Quantitative Analysts

Risk Managers

Portfolio Managers

Research Infrastructure for Markets Beyond Historical Data

Diffusion-based generative models that simulate realistic cross-asset market environments, enabling robust strategy validation beyond the limits of history.

Book a Demo