[hansvm]

I think the point they're going after is that algorithmic trading behavior can be meaningfully sensitive to rounding errors (which seems plausible if you profit by amplifying tiny signals), so in the context of a simulation you might still have components like Black Scholes, but for the trades themselves (even simulated) you need to take more care or risk an excessive error.

In other words, they're describing a scenario where 1 in 10^14 error is potentially not tolerable because of some amplified discrete behavior.

[bfrink]

Agree - real world discrete things should be modeled as such. If MPV was $0.23, then model $0.23 increments - whether you use fixed point, or the cardinality of increments, who cares. But all the other math leading up to a discrete decision on the increment is almost certain to be best described with, and faster to implement in, floats.

[proverbialbunny]

On modern day Intel cpus (no idea on AMD's side) math using longs is faster than math using floats. ymmv depending on architecture.

https://stackoverflow.com/questions/2550281/floating-point-v...

Floats were not historically adopted for speed, but because you can get more decimal places with small numbers like 8 bit numbers.

Today with 64 and 128 bit numbers, floating point loses most of its historical benefit, but of course it still has its benefits.