Solana: Decimal precision error for token in phantom


Error for the accuracy of decimal accuracy in Solan Phantoms: problem with exchange token

As the developer is building a decentralized application (DAPP) that will replace the liquidity tokens, one of the most common challenges you are facing is a solution to the accuracy of decimal accuracy. In this article, we will examine why this problem arises and how to alleviate it with Phantom, the popular Solana wallet platform.

Problem: Mistakes for accuracy of decimal accuracy in token swaps

The replacement of the token involves the replacement of one token for another within the liquidation fund. When performing such an exchange, you must multiply the input amount at the rate of exchange (ie J. For example, if you want to replace 1,000 x tokens for y tokens and the exchange rate is 2: 1 (y = x), your calculation would be:

1000 * 2 = 2000

However, when you use Phantom to interact with the Solana node, it does not accurately perform this calculation. Instead, it uses token SOL as a basic unit for all calculations. This leads to errors for the accuracy of decimal accuracy, especially in solving large inputs than 1000.

Problem: Phantom’s decimal accuracy

Phantom, which is a user -friendly and integrated wallet platform on Solane, has several restrictions that contribute to the problem:

  • This means that they are carried out in terms of SOL when performing decimal calculations.

  • No explicit rounding : Phantom explicitly rounded or shortened during calculations. Instead, he performs arithmetics with a moving point, which can lead to small errors due to inherent restrictions on binary fractions.

Relieving the accuracy of decimal accuracy

To avoid these problems and secure the exact token swaps, you can take a few steps:

  • These libraries allow you to carry out calculations with high accuracy without converting numbers into a salt tokens.

  • This helps to ensure accuracy and reduces the likelihood of decimal accuracy accuracy.

3
Use the built -in rounding feature of Phantom

Solana: Decimal precision error for token in phantom

: Phantom has a built -in feature that allows you to enable rounding during calculations. Check the “rounding” option in the Settings menu that can help improve accuracy.

Conclusion

When changing tokens on Solane using Phantom, common mistakes for the accuracy of decimal accuracy are common. By understanding the basic problems and applying solutions, such as the use of decimal arithmetic libraries or explicit rounding inputs and outputs, you can ensure accurate token swaps and maintain the integrity of your DAPP. Be sure to test and monitor the performance of optimal results thoroughly.

Example code

To demonstrate these concepts, write an example of an excerpt of the Solan’s Programming Language, which shows how decimal arithmetic works with Phantom:

`Solidity

Pragma of solidity ^0.8.0;

TOKENSWAP contract {

// Define the addresses of the input and output tokens

address the public xtokenaddress;

address the public ytkenaddress;

// Define the exchange rate as a fraction (eg 2: 1)

Uint256 public swaprate = 2000; // equivalent 1000 * 2

Function Swaptokens (UInt256 _xamount, Uint256 _yamount) public {

// Calculate the amount of output using decimal arithmetics

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

// rounding output of 18-19 digits for readability

Outputamount = output.

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