Fixed Point Arithmetic

pallets/fixed-point Try on playground View on GitHub

pallets/compounding-interest Try on playground View on GitHub

When programmers learn to use non-integer numbers in their programs, they are usually taught to use floating points. In blockchain, we use an alternative representation of fractional numbers called fixed point. There are several ways to use fixed point numbers, and this recipe will introduce three of them. In particular we'll see:

  • Substrate's own fixed point structs and traits
  • The substrate-fixed library
  • A manual fixed point implementation (and why it's nicer to use a library)
  • A comparison of the two libraries in a compounding interest example

What's Wrong with Floats?

Floats are cool for all kinds of reasons, but they also have one important drawback. Floating point arithmetic is nondeterministic which means that different processors compute (slightly) different results for the same operation. Although there is an IEEE spec, nondeterminism can come from specific libraries used, or even hardware. In order for the nodes in a blockchain network to reach agreement on the state of the chain, all operations must be completely deterministic. Luckily fixed point arithmetic is deterministic, and is often not much harder to use once you get the hang of it.

Multiplicative Accumulators


The first pallet covered in this recipe contains three implementations of a multiplicative accumulator. That's a fancy way to say the pallet lets users submit fractional numbers and keeps track of the product from multiplying them all together. The value starts out at one (the multiplicative identity), and it gets multiplied by whatever values the users submit. These three independent implementations compare and contrast the features of each.

Permill Accumulator

We'll be using the most common approach which takes its fixed point implementation from Substrate itself. There are a few fixed-point structs available in Substrate, all of which implement the PerThing trait, that cover different amounts of precision. For this accumulator example, we'll use the PerMill struct which represents fractions as parts per million. There are also Perbill, PerCent, and PerU16, which all provide the same interface (because it comes from the trait). Substrate's fixed-point structs are somewhat unique because they represent only fractional parts of numbers. That means they can represent numbers between 0 and 1 inclusive, but not numbers with whole parts like 2.718 or 3.14.

To begin we declare the storage item that will hold our accumulated product. You can see that the trait provides a handy function for getting the identity value which we use to set the default storage value to 1.

pub(super) fn PermillAccumulatorDefaultValue<T: Config>() -> Permill {

#[pallet::getter(fn permill_value)]
pub(super) type PermillAccumulator<T: Config> =
	StorageValue<_, Permill, ValueQuery, PermillAccumulatorDefaultValue<T>>;

The only extrinsic for this Permill accumulator is the one that allows users to submit new Permill values to get multiplied into the accumulator.

pub fn update_permill(
	origin: OriginFor<T>,
	new_factor: Permill,
) -> DispatchResultWithPostInfo {

	let old_accumulated = Self::permill_value();

	// There is no need to check for overflow here. Permill holds values in the range
	// [0, 1] so it is impossible to ever overflow.
	let new_product = old_accumulated.saturating_mul(new_factor);

	// Write the new value to storage

	// Emit event
	Self::deposit_event(Event::PermillUpdated(new_factor, new_product));

The code of this extrinsic largely speaks for itself. One thing to take particular note of is that we don't check for overflow on the multiplication. If you've read many of the recipes you know that a Substrate runtime must never panic, and a developer must be extremely diligent in always checking for and gracefully handling error conditions. Because Permill only holds values between 0 and 1, we know that their product will always be in that same range. Thus it is impossible to overflow or saturate. So we can happily use saturating_mul and move on.

Substrate-fixed Accumulator

Substrate-fixed takes a more traditional approach in that their types represent numbers with both whole and fractional parts. For this implementation, we'll use the U16F16 type. This type contains an unsigned number (indicated by the U at the beginning) and has 32 total bits of precision - 16 for the integer part, and 16 for the fractional part. There are several other types provided that follow the same naming convention. Some examples include U32F32 and I32F32 where the I indicates a signed number, just like in Rust primitive types.

As in the Permill example, we begin by declaring the storage item. With substrate-fixed, there is not a one function, but there is a from_num function that we use to set the storage item's default value. This from_num method and its counterpart to-num are your primary ways of converting between substrate-fixed types and Rust primitive types. If your use case does a lot of fixed-point arithmetic, like ours does, it is advisable to keep your data in substrate-fixed types.

We're able to use U16F16 as a storage item type because it, and the other substrate-fixed types, implements the parity scale codec.

pub(super) fn FixedAccumulatorDefaultValue<T: Config>() -> U16F16 {

#[pallet::getter(fn fixed_value)]
pub(super) type FixedAccumulator<T: Config> =
	StorageValue<_, U16F16, ValueQuery, FixedAccumulatorDefaultValue<T>>;

Next we implement the extrinsic that allows users to update the accumulator by multiplying in a new value.

pub fn update_fixed(
	origin: OriginFor<T>,
	new_factor: U16F16,
) -> DispatchResultWithPostInfo {

	let old_accumulated = Self::fixed_value();

	// Multiply, handling overflow
	let new_product = old_accumulated

	// Write the new value to storage

	// Emit event
	Self::deposit_event(Event::FixedUpdated(new_factor, new_product));

This extrinsic is quite similar to the Permill version with one notable difference. Because U16F16 handles numbers greater than one, overflow is possible, and we need to handle it. The error handling here is straightforward, the important part is just that you remember to do it.

This example has shown the fundamentals of substrate-fixed, but this library has much more to offer as we'll see in the compounding interest example.

Manual Accumulator

In this final accumulator implementation, we manually track fixed point numbers using Rust's native u32 as the underlying data type. This example is educational, but is only practical in the simplest scenarios. Generally you will have a more fun less error-prone time coding if you use one of the previous two fixed-point types in your real-world applications.

Fixed point is not very complex conceptually. We represent fractional numbers as regular old integers, and we decide in advance to consider some of the place values fractional. It's just like saying we'll omit the decimal point when talking about money and all agree that "1995" actually means 19.95 €. This is exactly how Substrate's Balances pallet works, a tradition that's been in blockchain since Bitcoin. In our example we will treat 16 bits as integer values, and 16 as fractional, just as substrate-fixed's U16F16 did.

If you're rusty or unfamiliar with place values in the binary number system, it may be useful to brush up. (Or skip this detailed section and proceed to the compounding interest example.)

Normal interpretation of u32 place values
... ___ ___ ___ ___ ___ ___ ___ .
...  64  32  16  8   4   2   1

Fixed interpretation of u32 place values
... ___ ___ ___ ___ . ___ ___ ___ ___ ...
...  8   4   2   1    1/2 1/4 1/8 1/16...

Although the concepts are straight-forward, you'll see that manually implementing operations like multiplication is quite error prone. Therefore, when writing your own blockchain applications, it is often best to use one of the provided libraries covered in the other two implementations of the accumulator.

As before, we begin by declaring the storage value. This time around it is just a simple u32. But the default value, 1 << 16 looks quite funny. If you haven't encountered it before << is Rust's bit shift operator. It takes a value and moves all the bits to the left. In this case we start with the value 1 and move it 16 bits to the left. This is because Rust interprets 1 as a regular u32 value and puts the 1 in the far right place value. But because we're treating this u32 specially, we need to shift that bit to the middle just left of the imaginary radix point.

pub(super) fn ManualAccumulatorDefaultValue<T: Config>() -> u32 {
	1 << 16
#[pallet::getter(fn manual_value)]
pub(super) type ManualAccumulator<T: Config> =
	StorageValue<_, u32, ValueQuery, ManualAccumulatorDefaultValue<T>>;

The extrinsic to multiply a new factor into the accumulator follows the same general flow as in the other two implementations. In this case, there are more intermediate values calculated, and more comments explaining the bit-shifting operations. In the function body most intermediate values are held in u64 variables. This is because when you multiply two 32-bit numbers, you can end up with as much as 64 bits in the product.

pub fn update_manual(origin: OriginFor<T>, new_factor: u32) -> DispatchResultWithPostInfo {

	// To ensure we don't overflow unnecessarily, the values are cast up to u64 before multiplying.
	// This intermediate format has 48 integer positions and 16 fractional.
	let old_accumulated: u64 = Self::manual_value() as u64;
	let new_factor_u64: u64 = new_factor as u64;

	// Perform the multiplication on the u64 values
	// This intermediate format has 32 integer positions and 32 fractional.
	let raw_product: u64 = old_accumulated * new_factor_u64;

	// Right shift to restore the convention that 16 bits are fractional.
	// This is a lossy conversion.
	// This intermediate format has 48 integer positions and 16 fractional.
	let shifted_product: u64 = raw_product >> 16;

	// Ensure that the product fits in the u32, and error if it doesn't
	if shifted_product > (u32::max_value() as u64) {
		return Err(Error::<T>::Overflow.into());

	let final_product = shifted_product as u32;

	// Write the new value to storage

	// Emit event
	Self::deposit_event(Event::ManualUpdated(new_factor, final_product));

As mentioned above, when you multiply two 32-bit numbers, you can end up with as much as 64 bits in the product. In this 64-bit intermediate product, we have 32 integer bits and 32 fractional. We can simply throw away the 16 right-most fractional bits that merely provide extra precision. But we need to be careful with the 16 left-most integer bits. If any of those bits are non-zero after the multiplication it means overflow has occurred. If they are all zero, then we can safely throw them away as well.

If this business about having more bits after the multiplication is confusing, try this exercise in the more familiar decimal system. Consider these numbers that have 4 total digits (2 integer, and two fractional): 12.34 and 56.78. Multiply them together. How many integer and fractional digits are in the product? Try that again with larger numbers like 98.76 and 99.99, and smaller like 00.11 and 00.22. Which of these products can be fit back into a 4-digit number like the ones we started with?

Compounding Interest


Many financial agreements involve interest for loaned or borrowed money. Compounding interest is when new interest is paid on top of not only the original loan amount, the so-called "principal", but also any interest that has been previously paid.

Discrete Compounding

Our first example will look at discrete compounding interest. This is when interest is paid at a fixed interval. In our case, interest will be paid every ten blocks.

For this implementation we've chosen to use Substrate's Percent type. It works nearly the same as Permill, but it represents numbers as "parts per hundred" rather than "parts per million". We could also have used Substrate-fixed for this implementation, but chose to save it for the next example.

The only storage item needed is a tracker of the account's balance. In order to focus on the fixed-point- and interest-related topics, this pallet does not actually interface with a Currency. Instead we just allow anyone to "deposit" or "withdraw" funds with no source or destination.

#[pallet::getter(fn discrete_account)]
	pub(super) type DiscreteAccount<T: Config> = StorageValue<_, u64, ValueQuery>;

There are two extrinsics associated with the discrete interest account. The deposit_discrete extrinsic is shown here, and the withdraw_discrete extrinsic is nearly identical. Check it out in the kitchen.

pub fn deposit_discrete(
	origin: OriginFor<T>,
	val_to_add: u64,
) -> DispatchResultWithPostInfo {

	let old_value = DiscreteAccount::<T>::get();

	// Update storage for discrete account
	DiscreteAccount::<T>::put(old_value + val_to_add);

	// Emit event

The flow of these deposit and withdraw extrinsics is entirely straight-forward. They each perform a simple addition or substraction from the stored value, and they have nothing to do with interest.

Because the interest is paid discretely every ten blocks it can be handled independently of deposits and withdrawals. The interest calculation happens automatically in the on_finalize block.

impl<T: Config> Hooks<T::BlockNumber> for Pallet<T> {
	fn on_finalize(n: T::BlockNumber) {
		// Apply newly-accrued discrete interest every ten blocks
		if (n % 10u32.into()).is_zero() {
			// Calculate interest Interest = principal * rate * time
			// We can use the `*` operator for multiplying a `Percent` by a u64
			// because `Percent` implements the trait Mul<u64>
			let interest = Self::discrete_interest_rate() * DiscreteAccount::<T>::get() * 10;

			// The following line, although similar, does not work because
			// u64 does not implement the trait Mul<Percent>
			// let interest = DiscreteAccount::get() * Self::discrete_interest_rate() * 10;

			// Update the balance
			let old_balance = DiscreteAccount::<T>::get();
			DiscreteAccount::<T>::put(old_balance + interest);

			// Emit the event

on_finalize is called at the end of every block, but we only want to pay interest every ten blocks, so the first thing we do is check whether this block is a multiple of ten. If it is we calculate the interest due by the formula interest = principal * rate * time. As the comments explain, there is some subtlety in the order of the multiplication. You can multiply PerCent * u64 but not u64 * PerCent.

Continuously Compounding

You can imagine increasing the frequency at which the interest is paid out. Increasing the frequency enough approaches continuously compounding interest. Calculating continuously compounding interest requires the exponential function which is not available using Substrate's PerThing types. Luckily exponential and other transcendental functions are available in substrate-fixed, which is why we've chosen to use it for this example.

With continuously compounded interest, we could update the interest in on_finalize as we did before, but it would need to be updated every single block. Instead we wait until a user tries to use the account (to deposit or withdraw funds), and then calculate the account's current value "just in time".

To facilitate this implementation, we represent the state of the account not only as a balance, but as a balance, paired with the time when that balance was last updated.

#[derive(Encode, Decode, Default)]
pub struct ContinuousAccountData<BlockNumber> {
	/// The balance of the account after last manual adjustment
	principal: I32F32,
	/// The time (block height) at which the balance was last adjusted
	deposit_date: BlockNumber,

You can see we've chosen substrate-fixed's I32F32 as our balance type this time. While we don't intend to handle negative balances, there is currently a limitation in the transcendental functions that requires using signed types.

With the struct to represent the account's state defined, we can initialize the storage value.

#[pallet::getter(fn balance_compound)]
pub(super) type ContinuousAccount<T: Config> =
	StorageValue<_, ContinuousAccountData<T::BlockNumber>, ValueQuery>;

As before, there are two relevant extrinsics, deposit_continuous and withdraw_continuous. They are nearly identical so we'll only show one.

fn deposit_continuous(origin: OriginFor<T>, val_to_add: u64) -> DispatchResultWithPostInfo {

	let current_block = frame_system::Module::<T>::block_number();
	let old_value = Self::value_of_continuous_account(&current_block);

	// Update storage for compounding account
	ContinuousAccount::<T>::put(ContinuousAccountData {
		principal: old_value + I32F32::from_num(val_to_add),
		deposit_date: current_block,

	// Emit event

This function itself isn't too insightful. It does the same basic things as the discrete variant: look up the old value and the deposit, update storage, and emit an event. The one interesting part is that it calls a helper function to get the account's previous value. This helper function calculates the value of the account considering all the interest that has accrued since the last time the account was touched. Let's take a closer look.

fn value_of_continuous_account(now: &<T as frame_system::Config>::BlockNumber) -> I32F32 {
	// Get the old state of the accout
	let ContinuousAccountData{
	} = ContinuousAccount::<T>::get();

	// Calculate the exponential function (lots of type conversion)
	let elapsed_time_block_number = *now - deposit_date;
	let elapsed_time_u32 = TryInto::try_into(elapsed_time_block_number)
		.expect("blockchain will not exceed 2^32 blocks; qed");
	let elapsed_time_i32f32 = I32F32::from_num(elapsed_time_u32);
	let exponent : I32F32 = Self::continuous_interest_rate() * elapsed_time_i32f32;
	let exp_result : I32F32 = exp(exponent)
		.expect("Interest will not overflow account (at least not until the learner has learned enough about fixed point :)");

	// Return the result interest = principal * e ^ (rate * time)
	principal * exp_result

This function gets the previous state of the account, makes the interest calculation and returns the result. The reality of making these fixed point calculations is that type conversion will likely be your biggest pain point. Most of the lines are doing type conversion between the BlockNumber, u32, and I32F32 types.

We've already seen that this helper function is used within the runtime for calculating the current balance "just in time" to make adjustments. In a real-world scenario, chain users would also want to check their balance at any given time. Because the current balance is not stored in runtime storage, it would be wise to implement a runtime API so this helper can be called from outside the runtime.