Decimal Precision Error in Solana Phantoms: Token Swap Issue
As a developer building a decentralized application (dApp) that exchanges tokens from a liquidity pool, one of the most common challenges you face is dealing with decimal precision errors. In this article, we explore why this issue occurs and how it can be mitigated in Solana’s popular wallet platform Phantom.
Problem: Decimal Precision Errors in Token Swap
Token swapping means exchanging one token for another in a liquidity pool. When performing such a swap, you need to multiply the input amount by the swap ratio (i.e., the ratio of the desired output token to the input token). For example, if you want to exchange 1000 X tokens for Y tokens and the exchange ratio is 2:1 (Y=X), your calculation would be as follows:
1000 * 2 = 2000
However, when you use Phantom to interact with a Solana node, it does not perform this calculation precisely. Instead, it uses the sol token as the base unit for all calculations. This results in decimal precision, especially when dealing with large input quantities such as 1000.
Problem: Phantom’s Decimal Precision
Phantom, being Solana’s user-friendly and integrated wallet platform, has several limitations that contribute to this issue:
- Sol token as base unit: As mentioned earlier, Phantom uses the sol token (SOL) as the base unit for all calculations. This means that when you perform decimal calculations, they are performed in SOL.
- No explicit rounding
: Phantom does not round or truncate numbers separately during calculations. Instead, it performs floating point arithmetic, which can lead to small errors due to the inherent precision limitations of binary fractions.
Mitigating Decimal Precision Errors
You can avoid these issues and ensure accurate token exchange by following a few steps:
- Use decimal arithmetic libraries: Consider using external libraries such as “decimal.js” or “js-decimal.js” that support arbitrary precision decimal arithmetic. These libraries allow you to perform calculations with high precision without converting numbers to sol tokens.
- Explicitly round inputs and outputs: When performing calculations, round the entered amounts to the appropriate precision (e.g. 18-19 digits) before multiplying or dividing by the exchange rate. This helps ensure accuracy and reduces the likelihood of decimal precision errors.
- Use Phantom’s built-in rounding feature: Phantom has a built-in feature that allows you to enable rounding during calculations. Select Rounding from the settings menu, which can improve accuracy.
Conclusion
Decimal precision errors are common when exchanging tokens on Solana with Phantom. By understanding the underlying issues and using workarounds, such as using decimal arithmetic libraries or explicitly rounding inputs and outputs, you can ensure accurate token exchanges and maintain the integrity of your dApp. Be sure to test carefully and monitor performance for best results.
Example Code
To illustrate these concepts, let’s write a sample code snippet in Solidity (Solana’s programming language) that demonstrates how decimal arithmetic works with Phantom:
“`solidity
pragma solidity ^0,8,0;
contract TokenSwap {
// Specify input and output token addresses
public address xTokenAddress;
address public yTokenAddress;
// Specify the swap rate as a fraction (e.g. 2:1)
uint256 public swapRate = 2000; // equals 1000 * 2
function swapTokens(uint256 _xAmount, uint256 _yAmount) public {
// Calculate the number of results using decimal arithmetic
uint256 outputAmount = (_xAmount * swapRate) / (swapRate – 1);
// Round the output amount to 18-19 digits for readability
outputAmount = outputAmount.