big.js VS 0.30000000000000004

ii) Third-Party Libraries There are various libraries like math.js, decimal.js, big.js that solve the problem. Each library functions according to its documentation. This approach is comparatively better.

For financial applications, APIs commonly return currency values, balances, stock/currency positions, and other amounts as strings. The best way I've found to deal with this is to use a library like big.js.

Just for fun, using javascript library:

Another different approach maybe to have the money represented in the decimal in your database at the decimal places of the currency with the highest decimal point you are going to be dealing with for example using Decimal(n, 2) in case of fiat, and perform your calculations in arbitrary-precision Decimal type. In javascript, using libraries like Big.js , Decimal.js will be of help.

With that said, using a library like big.js will solve both this and the potentially unsafe eval use:

It will explain this weird behavior. You have to use a special lib for precise floating point math. Here is one: big.js.

https://0.30000000000000004.com/

Although it would be good to move in the direction of using a BigDecimal equivalent by default when ingesting unknown data.

There's no such thing as precision for floats. Floating-point calculations are always inaccurate: read this: https://0.30000000000000004.com/

Edit: This specific example even has its own website: https://0.30000000000000004.com/

In everyday math, we know adding 0.6 + 0.3 equals 0.9, right? But when we turn to computers it results in 0.8999999999999999. Surprisingly, this doesn’t just happen only in JavaScript; it’s the same in many programming languages like Python, Java, C too. Also, it’s not just about this specific calculation. There are many more decimal calculations showing similar not-quite-right answers.

Surprisingly common values like 0.1 don't have a precise representation in binary for most formats, including standard floating point number formats. See https://0.30000000000000004.com/ for more detail than you can shake a stick at.

Also if the local tax code states using 5 decimal places for intermediate values when you will introduce “errors” using formats that give greater precision as well as those that give less precision. Having work on mortgage and pension calculations I can state that the (very) small errors seen at individual steps because of this can balloon significantly through repeated calculations.

Furthmore, the name floating point gives away the other issue. Floating point numbers are accurate to a given number of significant figures not decimal places. For large numbers any decimal places you have in the result are at best an estimate, and as above any rounding errors at each stage can compound into a much larger error by the end of a calculation.

You'll love this https://0.30000000000000004.com/