"Clojure in the Fintech Ecosystem"
summary
Recounts converting a Python Monte Carlo risk model to Clojure and ClojureScript, showing a reimplementation that runs about 20% faster, produces binaries 25% smaller and proves “at least 1000% more reliable” by using threads with immutable data instead of Python multiprocessing. Describes missing fractional matrix power solved via Java interop and libpython-clj, uses Datomic/XTDB temporal stores, Pedestal + Re-frame/Helix front end, and Tech.ml.dataset/Tablecloth for dataframe-style analytics with Plotly/Vega-Lite/D3 visualizations.
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chapters (19)
- 0:00Intro
- 4:45Four Wins for Clojure
- 6:01DFAST, CCAR and RWA
- 7:51A three way choice
- 10:06Safety in Isolation
- 13:54Warn on Reflection Frustration
- 14:48Initial Clojure Adoption Friction
- 16:29Financial Risk Data
- 17:36Standards
- 20:03RDF Clojure stores
- 21:41Database Time
- 23:14Web Frameworks
- 24:29ClojureScript
- 26:47Front end
- 30:43DataFrames in Clojure
- 34:39Pipeline processing
- 35:31Libraries have improved
- 36:33Tools have improved
- 37:57DataScience Stack Radar
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