Decimal Precision Error in Solana Phantoms: a question of token exchange

As a developer building a decentralized application (DAPP) that exchanges tokens of a liquidity pool, one of the most common challenges you will face is dealing with decimal accuracy errors. In this article, we will explore why this problem arises and how to mitigate it using Phantom, the popular Solana wallet platform.

The problem: decimal precision errors in token swaps

The token change involves the exchange of one token to another within a liquidity pool. When performing such an exchange, you need to multiply the amount of input by the exchange rate (ie the proportion of the desired output token and the input token). For example, if you want to exchange 1000 x tokens for tokens y and the exchange rate is 2: 1 (y = x), your calculation would be:

1000 * 2 = 2000

However, when you use Phantom to interact with a Solana node, it does not perform this calculation accurately. Instead, it uses token sun as a base unit for all calculations. This leads to decimal precision errors, especially when dealing with large amounts of input like 1000.

The question: Decimal Phantom Precision

Phantom, being a friendly and friendly wallet platform in Solana, has several limitations that contribute to this problem:

• This means that when you perform decimal calculations, they are made in terms of sun.

• No explicit rounding : Phantom not round or explicitly truncate numbers during calculations. Instead, it performs floating point arithmetic, which can lead to small errors due to precision limitations inherent in binary fractions.

Mitigating decimal precision errors

To avoid these problems and ensure accurate token swaps, you can give some steps:

• Use decimal arithmetic libraries : Consider the use of external libraries such as decimal.jsor JS-Decimal.js, which provide support to decimal arbitrary arithmetic. These libraries allow you to perform high accuracy calculations without converting numbers into sun tokens.

• Explicitly round entries and outputs : When executing calculations, rounding the input is equivalent to adequate accuracy (for example, 18-19 digits) before multiplying or dividing by the exchange rate. This helps ensure accuracy and reduces the likelihood of decimal accuracy errors.

• Use Phantom’s internal rounding feature : Phantom has an internal feature that allows you to activate rounding during calculations. Check the “rounding” option in the Settings menu, which can help improve accuracy.

Conclusion

Decimal precision errors are common when changing solana tokens using Phantom. Understanding underlying problems and applying alternative solutions, such as the use of decimal or explicitly rounding arithmetic libraries, you can ensure accurate token swaps and maintain your DAPP’s integrity. Remember to test thoroughly and monitor performance for ideal results.

Example Code

To demonstrate these concepts, let’s write an example of solidity code of code (Solana programming language) that shows how decimal arithmetic works with Phantom:

`Solidity

Pragma solidity ^0.8,0;

Tokenswap contract {

// Define the input and output token addresses

address the public XTokenaddress;

Add the public ytokenaddress;

// Define the exchange rate as a fraction (for example, 2: 1)

Uint256 Public Swaprate = 2000; // equivalent to 1000 * 2

SwapTokens Function (Uint256 _Xamount, Uint256 _Yamount) PUBLIC {

// Calculate the quantity of output using decimal arithmetic

uint256 outputamount = (_xamunt * swaprate) / (swaprate – 1);

// round the output value to 18-19 digits for readability

Outputamount = Outputamount.