Nexus Engine
High-frequency trading data pipeline and predictive infrastructure.
- Scenario
- Quantitative trading desk
- Year
- 2024
- Timeline
- 5 months
- Team
- 3 engineers
Problem
Quant trading desks routinely lose measurable edge to latency: market data lands in their models 40–60ms after origination, and ingestion layers tend to fall over during volatility spikes — precisely when accuracy matters most.
Approach
The concept rebuilds the ingestion and feature-computation path as a set of low-latency, horizontally-partitioned services, replacing a monolithic batch job with a streaming architecture. Predictive models sit on the same event bus so inference happens in-line with data arrival rather than downstream of it.
Outcome
In this illustrative scenario, end-to-end latency drops to single-digit milliseconds at the 99th percentile, and the architecture is designed to hold through repeated volatility spikes without manual intervention.
- Rust
- Kafka
- ClickHouse
- Kubernetes