Synthetic Market Data for Quantitative Finance
Generate infinite, deterministic market scenarios for backtesting and stress testing. Break free from the constraints of historical data.
What is Synthetic Market Data?
Synthetic market data is artificially generated financial data that mimics the statistical properties of real markets without being derived from actual historical records. Unlike replayed tick data, synthetic data can explore scenarios that have never occurred - stress events, liquidity crises, and regime changes that your strategy must survive.
Why Synthetic Over Historical?
Historical Data
- Finite - you cannot create more 2008 crashes
- Leads to overfitting and curve-fitting
- Expensive to license quality data
- Gaps, errors, and survivorship bias
- Past correlations may not repeat
Synthetic Data
- Unlimited scenarios on demand
- Deterministic and reproducible
- Cost-effective at scale
- Clean, consistent, no gaps
- Test against unseen regimes
How Aleatoric Generates Synthetic Data
Our Kinetic Stress framework does not just randomize numbers. We model the mechanical physics of markets:
- L2 Order Book Simulation: Realistic bid/ask depth with queue position modeling
- Funding Rate Mechanics: Venue-specific perpetual funding (Binance, HyperLiquid, CME, SGX)
- Latency Injection: Network jitter and packet loss for execution testing
- Liquidity Stress: Simulate adverse selection and toxic flow
- Deterministic Seeds: Every dataset is reproducible via manifest
Use Cases
Strategy Backtesting
Test trading strategies against millions of counterfactual scenarios.
Risk Stress Testing
Generate tail-risk events and black swan scenarios.
ML Training Data
Augment limited historical datasets with synthetic samples.
Execution Simulation
Test order routing against realistic market microstructure.
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