I’m building an in-house high-frequency trading stack dedicated to listed equities and need a quant with proven HFT expertise to turn my detailed spec into a live, latency-sensitive algorithmic strategy. The assignment covers the full workflow: transforming raw market data into alpha signals, engineering a sub-millisecond execution engine, and wrapping the whole system with rigorous risk controls. I will supply historical tick data, venue-specific microstructure notes, and target performance metrics; your job is to translate these into clean, production-ready code and verifiable research. Preferred toolchain is Python for research and C++17/20 for the low-latency path, with exchange connectivity over FIX/OUCH/ITCH. Familiarity with kernel-bypass networking (e.g., Solarflare, DPDK) and fast math libraries will be helpful, though not mandatory. Deliverables • Well-documented source code, separated into research modules and production modules • Reproducible back-test and live-simulation notebooks with full performance reports • Latency, throughput, and capacity benchmarks on supplied replay data • Technical document detailing architecture, configuration parameters, and risk controls Acceptance criteria: code compiles cleanly, runs within the agreed latency budget, and reproduces the target Sharpe ratio after modeled costs. I’ll be available for daily syncs and code reviews, and NDA paperwork is ready when you are. AFL Backtesting proven. (for Indian Markets).