Aleatoric Systems
We build deterministic synthetic market data infrastructure for quantitative finance. Our technology enables traders, researchers, and protocol developers to stress test strategies against infinite market scenarios.
Company Overview
Aleatoric Systems is a synthetic market data company that provides deterministic, reproducible market scenarios for backtesting, stress testing, and financial modeling. We serve quantitative trading firms, crypto protocols, and financial institutions who need to validate their systems against market conditions that haven't happened yet.
Our platform generates synthetic order books, funding rates, and price dynamics that preserve the statistical properties of real markets while exploring counterfactual regimes. Every dataset is fully reproducible via cryptographic manifest, enabling audit-ready compliance and deterministic CI/CD pipelines.
Founders
Founded by quantitative researchers with backgrounds in algorithmic trading, stochastic modeling, and financial engineering. Our team combines deep expertise in market microstructure with modern infrastructure engineering to deliver production-grade synthetic data at scale.
Location
Methodology: Stochastic Resonance vs Monte Carlo
Traditional approaches to synthetic market data rely on Monte Carlo simulation - generating random noise sampled from assumed distributions. While computationally straightforward, Monte Carlo fundamentally fails to capture the complex dependencies, regime dynamics, and tail behaviors that characterize real financial markets.
Traditional Monte Carlo
- Generates independent random samples
- Assumes known, static distributions
- Fails to capture volatility clustering
- Produces unrealistic tail events
- Ignores cross-asset dependencies
- No regime awareness
Aleatoric Stochastic Resonance
- Preserves autocorrelation structure
- Adapts to regime-dependent dynamics
- Maintains realistic volatility clustering
- Generates coherent tail scenarios
- Models cross-asset correlation breakdown
- Explores counterfactual regimes
Why Stochastic Resonance?
Stochastic resonance is a phenomenon where adding controlled noise to a nonlinear system can amplify weak signals rather than obscure them. In market modeling, this principle allows us to generate synthetic scenarios that:
- Preserve Statistical Properties: Synthetic data maintains the same distributional characteristics, autocorrelation, and higher moments as real market data.
- Explore Counterfactual Regimes: Generate plausible market conditions that haven't occurred historically but are consistent with market dynamics - flash crashes, liquidity crises, and correlation breakdowns.
- Enable Deterministic Reproducibility: Unlike pure Monte Carlo, our approach uses cryptographic seeds to ensure every scenario is exactly reproducible, critical for auditing and regulatory compliance.
- Capture Microstructure: Model realistic order book dynamics, queue position effects, and venue-specific behaviors that Monte Carlo cannot represent.
The result: synthetic market data that actually behaves like markets, not random noise with the right mean and variance.