Algorithmic Trading Infrastructure

In the realm of algorithmic trading, microseconds matter. Our systems are architected using C++ for core execution paths and Python for strategy development, bridged by high-performance bindings like pybind11. We implement co-location strategies and kernel-bypass networking (DPDK/Solarflare) to minimize packet processing overhead.

For data ingestion, we utilize FPGA-based feed handlers to normalize market data (FIX/SBE) directly from the wire, bypassing the OS network stack entirely. This ensures deterministic latency profiles essential for market making and arbitrage strategies.

A robust backtesting engine is the backbone of any quantitative fund. Our custom-built event-driven backtester simulates order book dynamics (L2/L3 data) rather than simple OHLCV bars, providing a realistic assessment of slippage and market impact.

• Walk-Forward Optimization: Automated sliding window analysis to prevent overfitting.
• Monte Carlo Simulation: Stress testing strategies against thousands of synthetic market scenarios.
• Alpha Factor Library: Pre-built momentum, mean-reversion, and sentiment factors ready for composition.

Our Pre-Trade Risk (PTR) checks operate in the critical path with sub-microsecond latency impact. We enforce limits on max order size, max position notional, and fat-finger protections. Post-trade analysis runs asynchronously to monitor portfolio VaR (Value at Risk) and Greeks exposure in real-time.