mmmmm!
Yes, using pure integer values for currency is generally the best way to
deal with it. The Bitcoin standard is that 1 BTC = 10,000,000 'satoshis'.
Many rightly point out that this is a stupidly small precision, but for the
time being is ok. I personally would expand this, and just use a 32/32
split and thus a 2^32 width for the fractional precision, which also makes
for very easy, fast implementation. I don't really think that this would
cause any issues whatsoever, really the differences between currencies
would then merely be a matter of presentation, as 32 bits of fractional
precision will not be exceeded in any near future situation.
On Friday, 30 November 2018 04:14:23 UTC+1, Bakul Shah wrote:
>
> FWIW, in some code I am writing, I considered using
> fixed point decimal numbers but ended up defining a
> *currency* type which is an int64 for value + a separate
> unit + currency kind. Even if I use a unit of millicent, this
> will allow handling amounts close to $100 Trillion. I
> don't expect this limit to be a problem for my personal
> finances! Performance is not an issue for my use. I
> even store all the financial data in text files!
>
> Dealing with stuff such as currency conversion, interest
> rates, stocks etc. gets a bit complicated due to their own
> precision needs but for that one can look at existing
> practices to do the right thing (which is, be able to accurately
> implement the rules your bank etc use).
>
> [Aside:
> Ideally this would be done using a *generic* currency
> type. Something like
>
> import "currency"
> type $ = currency.Type("$")
> type £ = currency.Type("£")
>
> var m1 = $(5)
> var m2 = $(10)
> var m3 = £(2)
>
> m1 + m2 // ok
> m2 + m3 // compile time error
> m1*m2 // compile time error
> m1*5 // ok
> m1+5 // compile time error
>
> I doubt go2 will get generics flexible enough for this!
> ]
>
> On Nov 28, 2018, at 10:47 PM, robert engels <ren...@ix.netcom.com
> <javascript:>> wrote:
>
> For those interesting in financial apps, I have released ‘fixed' at
> https://github.com/robaho/fixed a high performance fixed-point math
> library primarily designed for to work with currencies.
>
> The benchmarks: (Decimal is the shopspring library, big Int/Float are the
> stdlib)
>
> BenchmarkAddFixed-8 2000000000 0.83 ns/op
> 0 B/op 0 allocs/op
> BenchmarkAddDecimal-8 3000000 457 ns/op
> 400 B/op 10 allocs/op
> BenchmarkAddBigInt-8 100000000 19.2 ns/op
> 0 B/op 0 allocs/op
> BenchmarkAddBigFloat-8 20000000 110 ns/op
> 48 B/op 1 allocs/op
> BenchmarkMulFixed-8 100000000 12.4 ns/op
> 0 B/op 0 allocs/op
> BenchmarkMulDecimal-8 20000000 94.2 ns/op
> 80 B/op 2 allocs/op
> BenchmarkMulBigInt-8 100000000 22.0 ns/op
> 0 B/op 0 allocs/op
> BenchmarkMulBigFloat-8 30000000 50.0 ns/op
> 0 B/op 0 allocs/op
> BenchmarkDivFixed-8 100000000 19.3 ns/op
> 0 B/op 0 allocs/op
> BenchmarkDivDecimal-8 1000000 1152 ns/op
> 928 B/op 22 allocs/op
> BenchmarkDivBigInt-8 20000000 68.4 ns/op
> 48 B/op 1 allocs/op
> BenchmarkDivBigFloat-8 10000000 151 ns/op
> 64 B/op 2 allocs/op
> BenchmarkCmpFixed-8 2000000000 0.28 ns/op
> 0 B/op 0 allocs/op
> BenchmarkCmpDecimal-8 100000000 10.8 ns/op
> 0 B/op 0 allocs/op
> BenchmarkCmpBigInt-8 200000000 8.37 ns/op
> 0 B/op 0 allocs/op
> BenchmarkCmpBigFloat-8 200000000 7.74 ns/op
> 0 B/op 0 allocs/op
> BenchmarkStringFixed-8 20000000 99.0 ns/op
> 16 B/op 1 allocs/op
> BenchmarkStringDecimal-8 5000000 326 ns/op
> 144 B/op 5 allocs/op
> BenchmarkStringBigInt-8 10000000 209 ns/op
> 80 B/op 3 allocs/op
> BenchmarkStringBigFloat-8 3000000 571 ns/op
> 272 B/op 8 allocs/op
>
>
>
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