Why We Build Systematic Trading Infrastructure in R
The Case for R in Quant Finance
Python dominates the quant finance conversation, and for good reason — it has excellent libraries, a large community, and strong integration with machine learning frameworks. So why did we build Kwiz Quants primarily in R?
Statistical Depth
R’s statistical ecosystem is unmatched. The breadth of packages for time series analysis, financial econometrics, and statistical testing is deeper than any other language. When you’re implementing combinatorial purged cross-validation or computing Deflated Sharpe Ratios, R’s statistical foundations make the work cleaner and more reliable.
Production-Ready R
The perception that R is “just for analysis” is outdated. Modern R infrastructure makes production deployment viable: Plumber for REST APIs, Docker for containerisation, renv for reproducible environments, and Rhino for application architecture.
The key is engineering discipline. We apply the same practices used in any production software stack: modular code with box, 95%+ test coverage with testthat, CI/CD pipelines, and structured logging.
The Kwiz Quants Stack
Our trading infrastructure connects R-based strategy engines to MetaTrader 5 execution via MetaSocket, with PostgreSQL for logging and Shiny for monitoring. Each component is containerised, tested, and designed for resilience — atomic writes, hot standby replicas, and versioned snapshots.
R isn’t the easy choice for trading infrastructure. But it’s the right choice for a system where statistical rigour is the core differentiator.