Institutional-Grade Backtesting
& Research Platform
Our proprietary event-driven backtesting framework is engineered to eliminate look-ahead and survivorship biases mechanically. Simulating execution at the tick level, the engine models latency profiles, exchange-specific matching logic, and dynamic borrow costs. By integrating walk-forward validation and synthetic data augmentation via generative adversarial networks (GANs), we stress-test hypotheses under unseen regime shifts to guarantee that simulated Sharpe ratios reflect true production potential.
Key Competencies:
- Tick-level execution simulation with high-fidelity limit order book (LOB) modeling.
- Adversarial synthetic data generation for rare-event stress testing.
- Continuous walk-forward optimization and out-of-sample degradation monitoring.