Functional-Programming

Two years. Long enough that the novelty is gone and what’s left is the actual experience of living with the decision. Here’s the retrospective I’d want to have read before starting. ...

When I joined the bank’s risk team, Clojure was already in production for risk calculation. The code I inherited used Clojure’s persistent maps and vectors everywhere — not as a philosophical statement but because the team had found them practically useful in a specific way. The specific way: concurrent reads and occasional writes to a shared state snapshot, with no locks. ...

The risk calculation pipeline processed end-of-day positions: take all the day’s trades, aggregate them to net positions, apply mark-to-market prices, and compute risk metrics. The input was ~800,000 trade records; the output was ~12,000 position records with P&L and Greeks. The initial implementation used standard Clojure sequence operations: 1 2 3 4 5 6 (->> trades (filter open-trade?) (map enrich-with-market-data) (group-by :currency-pair) (map-vals aggregate-position) (map-vals compute-risk-metrics)) Clean. Readable. And it created four intermediate collections of 800,000 elements each before producing the final output. That’s 3.2M intermediate objects for a 12K result. Transducers changed this. ...

The first time someone told me the risk calculation system was written in Clojure, I assumed it was a prototype or a skunkworks project. It wasn’t. It processed end-of-day risk for a significant portion of the firm’s trading book and had been in production for two years. Here’s why that decision made sense, and what it was actually like to work in. ...