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0 pointsusefulcat9mo ago0 comments

You can certainly make trading systems that work using floating point, but there are just so many fewer edge cases to consider when using fixed point.

With fixed point and at least 2 decimal places, 10.01 + 0.01 is always exactly equal to 10.02. But with FP you may end up with something like 10.0199999999, and then you have to be extra careful anywhere you convert that to a string that it doesn't get truncated to 10.01. That could be logging (not great but maybe not the end of the world if that goes wrong), or you could be generating an order message and then it is a real problem. And either way, you have to take care every time you do that, as opposed to solving the problem once at the source, in the way the value is represented.

> Using some kind of fixed point math would be entirely inappropriate for most HFT or scientific computing applications.

In the case of HFT, this would have to depend very greatly on the particulars. I know the systems I write are almost never limited by arithmetical operations, either FP or integer.

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gamescr9mo ago

I work on game engines and the problem with floats isn't on small values like 10.01 but on large ones like 400,010.01 that's when the precision wildly varies.

osigurdson9mo ago

The issue with floats is the mental model. The best way to think about them is like a ruler with many points clustered around 0 and exponentially fewer as the magnitude grows. Don't think of it like a real value - assume that there are hardly any values represented with perfect precision. Even "normalish" numbers like 10.1 are not in the set actually. When values are converted to strings, even in debuggers sometimes, they are often rounded which throws people off further ("hey, the value is exactly 10.1 - it is right there in the debugger"). What you can count on however is that integers are represented with perfect precision up to a point (e.g. 2^53 -1 for f64).

The other "metal model" issue is that associative operations in math. Adding a + (b + c) != (a + b) + c due to rounding. This is where fp-precise vs fp-fast comes in. Let's not talk about 80 bit registers (though that used to be another thing to think about).

01HNNWZ0MV43FF9mo ago

Lua is telling me 0.1 + 0.1 == 0.2, but 0.1 + 0.2 != 0.3. That's 64-bit precision. The issue is not with precision, but with 1/10th being a repeating decimal in binary.

anthk9mo ago

Not an issue on Scheme and Common Lisp and even Forth operating directly with rationals with custom words.

malfist9mo ago

Not only that but the precision loss accumulates. Multiply too many numbers with small inaccuracies and you wind up with numbers with large inaccuracies

kolbe9mo ago

It depends on what you're doing. If your system is a linear regression on 30 features, you should probably use floating point. My recollection is fixed is prohibitively slower and with far less FOSS support.

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