When you think of programming languages in finance, Python and C++ usually come to mind. But for decades, OCaml has quietly powered some of the most latency-sensitive and correctness-critical trading systems on Wall Street. In this episode, Lucas and Luna explore why quantitative finance firms like Jane Street and Citadel Securities bet big on this functional programming language — from its strong type system that catches errors at compile time to its runtime performance that rivals C++. They dig into real numbers: how OCaml's pattern matching speeds up financial instrument modeling by 30-40% compared to object-oriented alternatives, and why its garbage collector matters less when the real bottleneck is algorithm expressiveness. You'll learn how OCaml's multi-core architecture, released in its 5.x series since 2023, has made it even more viable for modern high-throughput trading systems. If you think functional programming is just for academia, this episode will change your mind.

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