Decimal precision error in Phantoms Solana: a swap token problem
As a developer who builds a decentralized application (DAP) that exchanges tokens from a liquidity pool, one of the most common challenges you will face is to face decimal precision errors. In this article, we will explore because this problem arises and how to mitigate it using Phantom, the popular portfolio platform of Solana.
The problem: Decimal precision errors in the swap token
The exchange of token involves the exchange of one token with another inside a liquidity pool. When performing this exchange, it is necessary to multiply the amount of the APUT for the swap rate (i.e. the desired output token report and the input token). For example, if you want to exchange 1000 x token for tokens y and the swap speed is 2: 1 (y = x), the calculation would be:
1000 * 2 = 2000
However, when using Phantom to interact with a Solana node, it does not perform this calculation with precision. Instead, use the token Sol as a basic unit for all calculations. This leads to decimal precision errors, especially when it comes to great input amounts such as 1000.
The problem: Decimal precision of Phantom
Phantom, being an intuitive and integrated portfolio platform on Solana, has different limitations that contribute to this problem:
- Sol token as a basic unit : as mentioned above, Phantom uses the token Sol (Sol) as a basic unit for all calculations. This means that when performing decimal calculations, they are performed in terms of Sol.
2 Instead, it performs arithmetic with a mobile point, which can lead to small errors due to the intrinsic precision limits of the binary fractions.
Decimal precision errors mitigation
To avoid these problems and guarantee careful swap token, you can take some steps:
- Use decimal arithmetic bookcases : consider the use of external bookcases as
decimal.jsJs-Decimal.js, which provide support for the arithmetic arbitrary precision. These bookstores allow you to perform calculations with high precision without converting the numbers to token Sol.
- Round outputs and outputs explicitly : when you perform calculations, the entrance round is equivalent to a suitable precision (for example, 18-19 digits) before multiplying or dividing by swap speed. This helps to guarantee accuracy and reduces the probability of decimal precision errors.
- Use the integrated rounding function of Phantom : Phantom has an integrated function that allows to allow rounding during the calculations. Check the “rounding” option in the Settings menu, which can help improve precision.
Conclusion
Decimal precision errors are common when they exchange token on Solana using Phantom. By understanding the underlying problems and by applying alternative solutions, such as the use of decimal arithmetic bookcases or explicitly rounding input and output, it is possible to guarantee accurate token swaps and maintain the integrity of your DAP. Remember to test carefully and monitor performance for optimal results.
Example code
To demonstrate these concepts, we write a Snappet of example in solidity (Solana programming language) which shows off how decimal arithmetic works with phantom:
Solidity `
Pragma solidity ^0.8.0;
Takenswap contract {
// Define the token and output token addresses
Public XTKOKENDDress address;
Direct the public Ytkenaddress;
// define the swap rate as a fraction (e.g. 2: 1)
Uint256 Swaprate public = 2000; // Equivalent to 1000 * 2
Swaptokens function (UINT256 _XAMOUNT, UINT256 _YAMOUNT) public {
// Calculate the exit amount using decimal arithmetic
Uint256 Outputamount = (_xamount * swaprate) / (Swaprate – 1);
// round the output output at 18-19 digits for readability
Outputamount = Outputamount.
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