"Clojure in the Fintech Ecosystem"

on YouTube ↗40m

" 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.

AI-written summary · about

" chapters (19)

1. 0:00Intro
2. 4:45Four Wins for Clojure
3. 6:01DFAST, CCAR and RWA
4. 7:51A three way choice
5. 10:06Safety in Isolation
6. 13:54Warn on Reflection Frustration
7. 14:48Initial Clojure Adoption Friction
8. 16:29Financial Risk Data
9. 17:36Standards
10. 20:03RDF Clojure stores
11. 21:41Database Time
12. 23:14Web Frameworks
13. 24:29ClojureScript
14. 26:47Front end
15. 30:43DataFrames in Clojure
16. 34:39Pipeline processing
17. 35:31Libraries have improved
18. 36:33Tools have improved
19. 37:57DataScience Stack Radar

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