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>Nothing precludes you from doing integer arithmetic in a dynamic language. You would be surprised. The second you use pandas with a custom data type (let alo
by proverbialbunny 6y ago
>Nothing precludes you from doing integer arithmetic in a dynamic language.
You would be surprised. The second you use pandas with a custom data type (let alone any other library you'd want to use) it can randomly auto convert it to a float. Furthermore identifying when it randomly converts the type on you is a pain.
>so floating point basically must be used.
Quants tend to use fixed precision types. It is like a float in every way, except base 10 instead of base 2 so there is no floating point error.
- hntrader 6y agoQuants don't care about floating point precision in research. It's just applied stats
- proverbialbunny 6y agoI do, because the results from my research varies when I'm validating the model.
- dragonwriter 6y ago> The second you use pandas with a custom data type That's a pandas (and maybe numpy) issue, not a dynamic language issue. (If you want to generalize from the specific libraries more accurately than “dynamic language”, it's “using a low-level library whose type system doesn't match the host language type system” issue. > Quants tend to use fixed precision types. It is like a float in every way, except base 10 instead of base 2 so there is no floating point error. No, a type that is like binary floating point in every way except base 10 instead of base 2 would be decimal floating point, not fixed point. Decimal fixed point is different from binary floating point in more ways than base.