Building Real-Time Trading & Streaming Systems

Engineering Context

Real-time market interfaces have a different failure mode from ordinary web applications. A page that is correct but late is wrong. Users read figures to make decisions, so staleness, jitter, and inconsistency are product defects.

Live streaming adds a second pressure: many people consuming the same thing at the same moment. Work that is trivial for one user becomes a capacity question at thousands.

Copartner combined both: trading analytics and live-streaming infrastructure.

Trading Analytics

The analytics interface is built with React and D3.js. React owns application structure and state. D3.js handles the market-data visualization, where charts need precise control over scales, axes, and rendering.

I built the real-time trading analytics dashboards. They work with trading performance and risk metrics, the figures a trader reads to understand how positions are performing and what exposure they carry.

Backend Services

The backend is Node.js. I developed the services that produce and serve trading and risk metrics to the interface.

Their responsibility is the data path between market-data inputs and the dashboards: preparing figures so the interface can display them accurately and quickly. How that path is implemented internally is not described here.

Performance Engineering

Backend data latency was reduced by 30%.

That is the reported project outcome. The baseline, the final latency, and the measurement method are not stated here, and this page does not infer them. The result is attributed to this engagement, not to a benchmark run for this website.

Streaming Infrastructure

I optimized the live-streaming server infrastructure, which supports 10,000+ concurrent users.

That is the reported scale of the system. As above, the protocols, hosting, and topology behind it are not described, and the diagram on this page does not depict them.

Engineering Ownership

I worked across the stack on this engagement: the React and D3.js analytics dashboards, the Node.js backend services behind them, the backend data-latency reduction, and optimization of the live-streaming server infrastructure, in the role of Full-Stack Developer.

Technical Reflection

The points below are general real-time design considerations. They are not claims about techniques used on this project.

General consideration: latency is end to end. Users experience the sum of the data path. Improving one stage helps only if it was the bottleneck, so measure before optimizing.

General consideration: concurrency is a capacity question. The cost of a connection, multiplied by the number of connections, sets the ceiling. Per-connection work is the first place to look.

General consideration: fan-out. Delivering the same data to many clients rewards doing shared work once and keeping per-client work minimal.

General consideration: rendering. In data-dense interfaces, update frequency can outrun what the screen needs. Deciding what to redraw, and how often, protects both responsiveness and readability.