Research Infrastructure for Markets Beyond Historical Data
Research Infrastructure for Markets Beyond Historical Data
Research Infrastructure for Markets Beyond Historical Data
A generative market environment for stress testing, validation, and robust quant research.
A generative market environment for stress testing, validation, and robust quant research.
A generative market environment for stress testing, validation, and robust quant research.
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.
Drawbacks of Traditional Monte Carlo Simulations
Monte Carlo simulations rely on a static distribution, which can underplay tail risks and overlook key insights.
Drawbacks of Traditional Monte Carlo Simulations
Monte Carlo simulations rely on a static distribution, which can underplay tail risks and overlook key insights.
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.
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.
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.
We build diffusion-based generative models that learn a joint representation of market dynamics.
We build diffusion-based generative models that learn a joint representation of market dynamics.
Generate realistic cross-asset market paths
Generate realistic cross-asset market paths
Generate realistic cross-asset market paths
Stress test beyond observed history
Stress test beyond observed history
Stress test beyond observed history
Condition on volatility, macro states, correlation regimes
Condition on volatility, macro states, correlation regimes
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?
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?
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?
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.
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.
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.
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.