This is a handson review of both the how and why to move critical fintech components into Clojure.

After 20+ years of data science in python, why go "allin" on Clojure/ClojureScript?

It will start with a risk modeling problem (i.e MoteCarlo investigation) in which the shortcomings of the python implementation cried out for better approach. Queue Clojure. Beyond the wellknown advantages of immutable data, a discussion of the options for all “whatif” analysis problems with a comparison of the safety afforded by isolation in Clojure threads, Python processes, and Docker containers.

We will review the relative strengths and weaknesses of Pandas DataFrames vs Clojure native structures.

There will be a review of all of the components in an Investment Portfolio Management or Trading Order Management System with real implementations using Clojure with Datomic or XTDB and Pedestal on the backend and ClojureScript with BlueprintJS, VegaLite, Plotly, D3, and Plotly.