Decimal Precision Error in Solana Phantoms: A Token Swap Issue
As a developer building a decentralized application (dApp) that swaps tokens from a liquidity pool, one of the most common challenges you’ll face is dealing with decimal precision errors. In this article, we’ll explore why this issue arises and how to mitigate it using Phantom, Solana’s popular wallet platform.
The Problem: Decimal Precision Errors in Token Swaps
Token swapping involves exchanging one token for another within a liquidity pool. When performing such an exchange, you need to multiply the input amount by the swap rate (i.e., the ratio of the desired output token to the input token). For example, if you want to swap 1000 X tokens for Y tokens and the swap 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 doesn’t perform this calculation precisely. Instead, it uses the sol token as a base unit for all calculations. This leads to decimal precision errors, especially when dealing with large input amounts like
The Issue: Phantom’s Decimal Accuracy
Phantom, being a user-friendly and integrated wallet platform on Solana, has several limitations that contribute to this issue:
- Sol token as the base unit
: As mentioned earlier, Phantom uses the sol token (SOL) as a base unit for all calculations. This means that when you perform decimal calculations, they are performed in terms of SOL.
- No explicit rounding: Phantom doesn’t explicitly round or truncate numbers 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
To avoid these issues and ensure accurate token swaps, you can take a few steps:
- Use decimal arithmetic libraries: Consider using external libraries like
decimal.jsorjs-decimal.js, which provide support for arbitrary-precision decimal arithmetic. These libraries allow you to perform calculations with high precision without converting numbers to sol tokens.
- Round inputs and outputs explicitly: When performing calculations, round the input amounts to a suitable precision (e.g., 18-19 digits) before multiplying or dividing by the swap 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. Check the “Rounding” option in the settings menu, which can help improve accuracy.
Conclusion
Decimal precision errors are common when swapping tokens on Solana using Phantom. By understanding the underlying issues and applying workarounds, such as using decimal arithmetic libraries or explicitly rounding inputs and outputs, you can ensure accurate token swaps and maintain the integrity of your dApp. Remember to test thoroughly and monitor performance for optimal results.
Sample Code
To demonstrate these concepts, let’s write an example code snippet in Solidity (Solana’s programming language) that showcases how decimal arithmetic works with Phantom:
“`solidity
pragma solidity ^0.8.0;
contract TokenSwap {
// Define the input and output token addresses
address public xTokenAddress;
public address yTokenAddress;
// Define the swap rate as a fraction (e.g., 2:1)
uint256 public swapRate = 2000; // equivalent to 1000 * 2
function swapTokens(uint256 _xAmount, uint256 _yAmount) public {
// Calculate the output amount using decimal arithmetic
uint256 outputAmount = (_xAmount * swapRate) / (swapRate – 1);
// Round the output amount to 18-19 digits for readability
outputAmount = outputAmount.