Automated Market Making of Yield Token Pool


ALEX aims to provide a fixed rate borrowing and lending service with pre-determined maturity in the world of decentralised finance (DeFi). We include forward contracts in our trading pool, with Automated Market Making (AMM) engine in association with generalised mean. While we formalise the trading practise swapping forward contracts with underlying asset, we incorporate the latest innovation in the industry - concentrated liquidity. Consequently, liquidity provider of ALEX can save decent amount of capital by making markets on a selected range of interest rate.


ALEX stands for Automated Liquidity EXchange. It is a hybrid of automated market making and on-chain loanable fund built on Stacks blockchain network. While lenders and borrowers can minimise uncertainty by securing the loan with fixed rate and tenor, liquidity providers are able to take advantage of our capital efficiency mechanism by imposing cap and floor on the interest rate. This allows liquidity to be offered on parts of the curve that contains majority of trading activities and leads to efficient capital management.

On ALEX, lending and borrowing activities are facilitated by a forward contract based token โ€œayTokenโ€. It is similar to an OTC bilateral forward contract in the conventional financial market, which specifies underlying asset โ€œTokenโ€ and expiry date. This paper assumes ayToken is minted and ready to be exchanged. Lenders purchase ayToken at a discount to the spot Token price when the contract is initiated and reclaim underlying asset upon expiration when forward price converges to spot price. Borrowers sell ayToken in return for Token on day one and return Token upon expiration. Implied interest rate depends on how much discount that the forward price is to the spot price at the time of transaction, which is executed on AMM.

Last but not least, ALEX hopes to bridge the gap between Defi and conventional finance by applying an AMM protocol derived from one of the basic instruments in fixed income market - zero coupon bond firstly proposed by Yield Space. This empowers ALEX to learn from the fiat world and offer more decentralised financial products in the future.

This paper focuses on technical aspects of AMM and is the first of a series of ALEX papers unveiling all exciting features and applications of ALEX development.

AMM and Invariant Function

ALEX AMM is built on three beliefs: (i) it is mathematically neat and reflect economic demand and supply; (ii) it is a type of mean, like other AMMs; and (iii) it is derived and can be interpreted in terms of yield and is somehow related to conventional finance, where research has been conducted for decades.

We will firstly review some desirable features of AMM that ALEX hopes to exhibit.

Properties of AMM

AMM protocol, which provides liquidity algorithmically, is the core engine of Defi. In the liquidity pool, two or more assets are deposited and subsequently swapped resulting in both reserve and price movement. The protocol follows an invariant function f(X)=Lf(X)=L, where X=(x1,x2,โ€ฆ,xd)X=\left(x_1,x_2,\dots,x_d\right) is dd dimension representing dd assets and LL is constant. When d=2d=2, which is the common practise by a range of protocols, AMM f(x1,x2)=Lf(x_1,x_2)=L can be expressed as x2=g(x1)x_2=g(x_1). Although it is not always true, gg tends to be twice differentiable and satisfies the following

  • monotonically decreasing, i.e. dg(x1)dx1<0\frac{dg(x_1)}{dx_1}<0. This is because price is often defined as โˆ’dg(x1)dx1-\frac{dg(x_1)}{dx_1}. A decreasing function ensures price to be positive.

  • convex, i.e. d2g(x1)dx12โ‰ฅ0\frac{d^2g(x_1)}{dx_1^2} \geq 0. This is equivalent to say that โˆ’dg(x1)dx1-\frac{dg(x_1)}{dx_1} is a non-increasing function of x1x_1. It is within the expectation of economic theory of demand and supply, as more reserve of x1x_1 means declining price.

Meanwhile, ff can usually be interpreted as a form of mean, for example, mStable relates to arithmetic mean, where x1+x2=Lx_1+x_2=L (constant sum formula); one of the most popular platforms Uniswap relates to geometric mean, where x1x2=Lx_1 x_2=L (constant product formula); Balancer, which our collateral rebalancing pool employs, applies weighted geometric mean. Its AMM is x1w1x2w2=Lx_1^{w_1} x_2^{w_2}=L where w1w_1 and w2w_2 are fixed weights. However, none of these three protocols consider time to maturity, which is essential in modern interest rate theory.


After extensive research, we consider it possible for ALEX AMM to be connected to generalised mean defined as

(1dโˆ‘i=1dxip)1p\left( \frac{1}{d} \sum _{i=1}^{d} x_i^p \right)^{\frac{1}{p}}

where 0โ‰คpโ‰ค10 \leq p \leq 1. The expression might remind readers of pp-norm when xiโ‰ฅ0x_i \geq 0. It is however not true when p<1p<1 as triangle inequality doesn't hold.

When d=2d=2 and pp is fixed, the core component of generalised mean is assumed constant as below.


This equation is regarded reasonable as AMM, because (i) function gg where x2=g(x1)x_2=g(x_1) is monotonically decreasing and convex; and (ii) The boundary value of p=1p=1 and p=0p=0 corresponds to constant sum and constant product formula respectively. When pp increases from 0 to 1, price โˆ’dg(x1)x1-\frac{dg(x_1)}{x_1} gradually converges to 1. This is what ALEX hopes to achieve when forward becomes spot. This also means that pp is somehow related to time to maturity. Please refer to Appendix 1 for a detailed discussion.

In the benchmark research piece by Yield Space, the invariant function above is formalised from the perspective of zero coupon bond. pp is replaced by 1โˆ’t1-t where tt is time to maturity and LL is a function of tt, so that


This is derived by solving the following differential equation when tโ‰ 1t \neq 1

โˆ’dx2dx1=(x2x1)t-\frac{dx_2}{dx_1}=\left(\frac{x_2}{x_1} \right)^t

In the rest of the paper, to be consistent with Yield Space, we employ notations below

  • xx : balance of the underlying Token

  • yy : balance of ayToken

  • rr : implied interest rate, defined as the natural logarithm of balance of ayToken and Token

r=log(yx)r=log \left( \frac{y}{x} \right)
  • pp : price of Token in terms of ayToken. The commonly quoted ayToken price is the inverse, i.e. 1p\frac{1}{p}

p=(yx)t=ertp=\left(\frac{y}{x} \right)^t=e^{rt}

ALEX's implied interest rate is compound. Not only does the compound rate allow us to derive mathematical formulas throughout the paper, we can also conduct further research and offer more products by referring to vast amount of literatures and applications in conventional finance, which is largely built on Black-Scholes model with compound rate employed as the discounting factor.

Using notations above, the invariant function is rewritten as x1โˆ’t+y1โˆ’t=Lx^{1-t}+y^{1-t}=L with the differential equation โˆ’dydx=(yx)t-\frac{dy}{dx}=\left(\frac{y}{x} \right)^t. Unless specified, we assume LL constant and call it invariant constant. This means that tt is fixed and there is no minting or burning coins. In practise, liquidity providers can add or reduce liquidity, and LL needs to be recalibrated daily when tt changes.

Though purely theoretical at this stage, Appendix 2 maps LL to the liquidity distribution of Uniswap V3. This is motivated by an independent research from Paradigm.

Trading Formulae

Market transaction, which involves exchange of Token and ayToken, satisfies the invariant function. While fee is deposited back to the liquidity pool in some protocols, such as Uniswap V2, resulting in slight increase of LL after each transaction, ALEX counts the fee separately. This is consistent with Uniswap V3. Hence LL remains constant.


In order to purchase ฮ”y\Delta y amount of ayToken from the pool, the buyer needs to deposit ฮ”x\Delta x amount of Token. ฮ”x\Delta x and ฮ”y\Delta y satisfy the following

(x+ฮ”x)1โˆ’t+(yโˆ’ฮ”y)1โˆ’t=x1โˆ’t+y1โˆ’t(x+\Delta x)^{1-t}+(y-\Delta y)^{1-t}=x^{1-t}+y^{1-t}

After each transaction, balance is updated as below: xโ†’x+ฮ”xx\rightarrow x+\Delta x and yโ†’yโˆ’ฮ”yy\rightarrow y-\Delta y. Balance of yy should not be less than that of xx to avoid negative interest rate, which will be discussed in detail later. Rearranging the formula results in

ฮ”y=yโˆ’[x1โˆ’t+y1โˆ’tโˆ’(x+ฮ”x)1โˆ’t]11โˆ’t\Delta y=y-\left[x^{1-t}+y^{1-t}-(x+\Delta x)^{1-t}\right]^{\frac{1}{1-t}}

When transaction cost exists, the actual deposit to the pool is less than ฮ”x\Delta x. Assuming ฮปฮ”x\lambda\Delta x is the actual amount and (1โˆ’ฮป)ฮ”x(1-\lambda)\Delta x is the fee, above can now be expressed as

(x+ฮปฮ”x)1โˆ’t+(yโˆ’ฮ”y)1โˆ’t=x1โˆ’t+y1โˆ’tฮ”y=yโˆ’[x1โˆ’t+y1โˆ’tโˆ’(x+ฮปฮ”x)1โˆ’t]11โˆ’t\begin{split} &(x+\lambda\Delta x)^{1-t}+(y-\Delta y)^{1-t}=x^{1-t}+y^{1-t}\\ &\Delta y=y-\left[x^{1-t}+y^{1-t}-(x+\lambda\Delta x)^{1-t}\right]^{\frac{1}{1-t}} \end{split}

To keep LL constant, the updated balance is: xโ†’x+ฮปฮ”xx\rightarrow x+\lambda\Delta x and yโ†’yโˆ’ฮ”yy\rightarrow y-\Delta y.


This is the opposite case to above. We are deriving ฮ”x\Delta x from ฮ”y\Delta y.

ฮ”x=1ฮป[x1โˆ’t+y1โˆ’tโˆ’(yโˆ’ฮ”y)1โˆ’t]11โˆ’tโˆ’x\Delta x=\frac{1}{\lambda}{\left[x^{1-t}+y^{1-t}-(y-\Delta y)^{1-t}\right]^{\frac{1}{1-t}}-x}

In-Given-Price / Yield

Sometimes, trader would like to adjust the price/yield, perhaps due to deviation of AMM price to the market value. Define pโ€ฒp' the AMM price after rebalancing the Token and ayToken in the pool

pโ€ฒ=(yโˆ’ฮ”yx+ฮปฮ”x)tp'=\left(\frac{y-\Delta y}{x+\lambda\Delta x}\right)^{t}

Then, the added amount of ฮ”x\Delta x can be calculated from the formula below

(x+ฮปฮ”x)1โˆ’t+(yโˆ’ฮ”y)1โˆ’t=x1โˆ’t+y1โˆ’t1+(yx)1โˆ’t=(1+ฮปฮ”xx)1โˆ’t+(yโˆ’ฮ”yx)1โˆ’t1+p1โˆ’tt=(1+ฮปฮ”xx)1โˆ’t+pโ€ฒ1โˆ’tt(1+ฮปฮ”xx)1โˆ’tฮ”x=xฮป[(1+p1โˆ’tt1+pโ€ฒ1โˆ’tt)11โˆ’tโˆ’1]\begin{split} &(x+\lambda\Delta x)^{1-t}+(y-\Delta y)^{1-t}=x^{1-t}+y^{1-t}\\ &1+\left(\frac{y}{x}\right)^{1-t}=\left(1+\lambda\frac{\Delta x}{x}\right)^{1-t}+(\frac{y-\Delta y}{x})^{1-t}\\ &1+p^{\frac{1-t}{t}}=\left(1+\lambda\frac{\Delta x}{x}\right)^{1-t}+p'^{\frac{1-t}{t}}\left(1+\lambda\frac{\Delta x}{x}\right)^{1-t}\\ &\Delta x=\frac{x}{\lambda}\left[\left(\frac{1+p^{\frac{1-t}{t}}}{1+p'^{\frac{1-t}{t}}}\right)^{\frac{1}{1-t}}-1\right]\\ \end{split}

Denote rr and rโ€ฒr' the current and trader's target interest rate respectively. Because p=ertp=e^{rt} and pโ€ฒ=erโ€ฒtp'=e^{r't}, the above equation can also be rewritten as

ฮ”x=xฮป[(1+er(1โˆ’t)1+erโ€ฒ(1โˆ’t))11โˆ’tโˆ’1]\Delta x=\frac{x}{\lambda}\left[\left(\frac{1+e^{r(1-t)}}{1+e^{r'(1-t)}}\right)^{\frac{1}{1-t}}-1\right]

Transaction Cost on Notional and Yield

In the previous sections, fee is in proportion to the notional amount. This is consistent with AMM such as Uniswap. However, it could be hard to interpret in the yield space, as market participants tend to think of borrowing or lending activity in terms of rate.

The formula below expresses ฮป\lambda regarding bid/offer imposed on interest rate rr, so that conversion in between the two is possible. Denote rmr_m as the mid rate calculated from AMM

erm=ฮ”yฮปฮ”xe^{r_m}=\frac{\Delta y}{\lambda\Delta x}

However, trader deposits ฮ”x\Delta x rather than ฮปฮ”x\lambda\Delta x. Therefore, the bid rate rbr_b when purchasing ayToken satisfies

erb=ฮ”yฮ”xe^{r_b}=\frac{\Delta y}{\Delta x}

ฮ”rb=rmโˆ’rb\Delta r_b=r_m-r_b is then the fee charged to the purchaser in the yield space,

eฮ”rb=1ฮปe^{\Delta r_b}=\frac{1}{\lambda}

Hence, ฮป\lambda can be expressed as a function of ฮ”rb\Delta r_b

ฮป=eโˆ’ฮ”rb\lambda=e^{-\Delta r_b}

Actual fee is 1โˆ’ฮป=1โˆ’eโˆ’ฮ”rbโ‰ˆฮ”rb1- \lambda=1-e^{-\Delta r_b} \approx \Delta r_b using Taylor expansion to the first order. Thus ฮปโ‰ˆ1โˆ’ฮ”rb\lambda \approx 1-\Delta r_b .

It can be shown that the above equality also holds when redeeming ayToken for Token, except ฮ”rb\Delta r_b replaced by ฮ”ro=roโˆ’rm\Delta r_o=r_o-r_m, where erm=ฮปฮ”yฮ”xe^{r_m}=\frac{\lambda\Delta y}{\Delta x} and ero=ฮ”yฮ”xe^{r_o}=\frac{\Delta y}{\Delta x}. Here, ror_o is the offer rate when selling ayToken and ฮ”ro\Delta r_o is the corresponding fee charged to the seller in the yield space.

Concentrated Liquidity

In the current setting, liquidity provider can make market on any rate between โˆ’โˆž-\infty to +โˆž+\infty. However, market participants might wish to impose certain constraint, for example no negative interest rate. One solution is to set up bounds on yx\frac{y}{x}, which are related to the rate. In the case of positive rate, this means balance of ayToken always larger than Token. Although it solves the problem, the amount of ayToken lower than Token would be excluded from trading activities in any means. We are proposing an alternative approach by introducing virtual tokens.

Virtual tokens constitute part of the trading pool reserve that would never be touched hence underutilised. Liquidity providers should not be required to maintain this part of the pool and we are therefore set them as virtual. For example, when rate is floored at 0%, tt= 0.5 and LL= 20, liquidity providers will never face the situation of ayToken balance falling below 100, which can then be regarded as virtual to save the actual capital cost.

The idea is inspired by concentrated liquidity in Uniswap v3.

Pool with interest rate floored at zero

This section only allows liquidity on non-negative interest rate. Concentrated liquidity is achieved by introducing virtual token reserves yvy_v, which satisfies


Figure 1 illustrates the example above of tt= 0.5 and LL= 20 by displaying two sets of curves: Invariant Function Curve (โ€œIFC") satisfying x1โˆ’t+y1โˆ’t=Lx^{1-t}+y^{1-t}=L and Capital Efficiency Curve (โ€œCEC") satisfying x1โˆ’t+(y+yv)1โˆ’t=Lx^{1-t}+(y+y_v)^{1-t}=L. Intuitively CEC is attained by lowering IFC by yvy_v= 100.


Instead of contributing equal amount of Token and ayToken to initialise the pool with interest rate 0%, liquidity provider only needs to contribute x amount of Token. This is because virtual token yv=x=(12L)11โˆ’ty_v=x=\left(\frac{1}{2}L\right)^{\frac{1}{1-t}}.


Balance of Token and ayToken, including both actual and virtual, still satisfy the invariant function. However, once the actual ayToken is depleted and only Token is left in the pool, trading would be ceased until more ayToken is deposited.

Minting and Burning

Before liquidity expansion or reduction by minting or burning coins, assume that the old pool has Token xx and ayToken yy satisfying x1โˆ’t+y1โˆ’t=Lx^{1-t}+y^{1-t}=L, where y=ya+yvy=y_a+y_v and yay_a and yvy_v are balance of actual and virtual ayToken respectively.

Minting and burning should not affect price and interest rate. This means that newly added or withdrawn coins would be in proportion to x and y. Denote new amount of Token and ayToken as xโ€ฒ=kxx'=kx and yโ€ฒ=kyy'=ky respectively. yโ€ฒ=yaโ€ฒ+yvโ€ฒy'=y'_a+y'_v where yaโ€ฒy'_a is actual whereas yvโ€ฒy'_v virtual. They satisfy the following

yaโ€ฒ+yvโ€ฒ=ky2yvโ€ฒ1โˆ’t=k1โˆ’tL\begin{split} &y'_a+y'_v=ky\\ &2y'^{1-t}_v=k^{1-t}L \end{split}

Solution to the above equations is

yaโ€ฒ=kyโˆ’yvโ€ฒyvโ€ฒ=(12L)11โˆ’tk\begin{split} &y'_{a}=ky-y'_{v}\\y'_{v} &=\left(\frac{1}{2}L\right)^{\frac{1}{1-t}}k \end{split}

This means yvโ€ฒ=kyvy'_v=ky_v and yaโ€ฒ=kyay'_a=ky_a.


Assume tt= 0.5. Rachel initialises a liquidity pool of 0% interest rate with 100 Token on CEC. Although there is no actual ayToken, 0% rate implies 100 virtual tokens and LL= 20 on IFC.

Suppose Rachel then sells 50 ayToken to the pool on the same day. On IFC, this means ayToken amount of 150 (50 actual and 100 virtual) and the amount of 60.10 Token remaining on IFC.

Now suppose Billy wants to mint 10% of the liquidity pool. This means that Billy needs to deposit 6.01 (10% of 60.10) Token. Virtual balance is updated to (12ร—20)10.5ร—1.1=110\left(\frac{1}{2}\times20\right)^{\frac{1}{0.5}}\times1.1=110. Billy needs to deposit 5 ayToken (1.1ร—150โˆ’110โˆ’501.1\times150-110-50), so that the summation of actual and virtual ayToken is 165. Interest rate remains the same before and after Billy's participation. Note that both actual and virtual ayToken balance increase by 10%, which is the same proportion as the growth of liquidity pool.

Range-bound Pool

The above section can be extended to any constraint pool with upper interest rate rur_{u} and lower interest rate rlr_{l}. If interest rate falls out of [rlr_{l},rur_{u}], swapping would be suspended as one of the tokens would have been depleted.

Denote xax_{a}, xvx_{v}, yay_{a} and yvy_{v} balance of actual Token, virtual Token, actual ayToken and virtual ayToken respectively. They satisfy invariant function on IFC, i.e. (xa+xv)1โˆ’t+(ya+yv)1โˆ’t=L(x{a}+x{v})^{1-t}+(y{a}+y{v})^{1-t}=L.

The amount of Token an ayToken can be expressed as a function of L and current interest rate rc=ya+yvxa+xvr_{c}=\frac{y_{a}+y_{v}}{x_{a}+x_{v}}.

Intuitively, when rc=rlr_{c}=r_{l}, ayToken is depleted; Similarly, when rc=rur_{c}=r_{u}, Token is used up. Therefore,

xv=[L1+e(1โˆ’t)ru]11โˆ’tyv=[L1+eโˆ’(1โˆ’t)rl]11โˆ’t\begin{split} &x_{v}=\left[\frac{L}{1+e^{(1-t)r_{u}}}\right]^{\frac{1}{1-t}}\\ &y_{v}=\left[\frac{L}{1+e^{-(1-t)r_{l}}}\right]^{\frac{1}{1-t}} \end{split}

See Appendix 3 for a detailed derivation of virtual, as well as actual token reserve.

Similar to the case of 0% floor, minting or burning coins would result in invariant constant changing from LL to k1โˆ’tLk^{1-t}L. Meanwhile, both actual and virtual Token and ayToken would grow proportionally by kk, as they are linear function of L11โˆ’tL^{\frac{1}{1-t}}.


We aim to show here how virtual token is able to assist liquidity providers to efficiently manage capital.

In Figure 2, assume lower bound is 0%, whereas upper bound is 50%. We also set tt= 0.5 and LL= 20. If interest rate is 0%, LL= 20 means holding equal amount of Token and ayToken of 100 each (1000.5+1000.5=20)\left(100^{0.5}+100^{0.5}=20\right). The figure compares actual holding of Token and ayToken with and without cap and floor.

According to the figure, when current implied interest rate is 10%, without capital efficiency, liquidity provider is required to deposit 95.06 Token and 105.06 ayToken. This is in comparison with 18.39 Token and 5.06 ayToken after imposing cap and floor. In this example, the capital saving is at least 77%.

Appendix 1: Generalised Mean when d=2

ALEX's invariant function is f(x1,x2;p)=x1p+x2p=L.f(x_{1},x_{2};p)=x{_1}^{p}+x_{2}^{p}=L. It can be rearranged as x2=g(x1)=(Lโˆ’x1p)1px{2}=g(x_{1})=(L-x_{1}^{p})^{\frac{1}{p}}. x1x_{1} and x2x_{2} should both be positive meaning the liquidity pool contains both tokens.


When 0<p<10<p<1, g(x1)g\left(x_{1}\right) is monotonically decreasing and convex.


This is equivalent to prove dg(x1)dx1<0\frac{dg(x_{1})}{dx_{1}}<0 and d2g(x1)dx12โ‰ฅ0\frac{d^{2}g(x_{1})}{dx_{1}^{2}}\geq0.

dg(x1)dx1=1p(Lโˆ’x1p)1pโˆ’1(โˆ’px1pโˆ’1)=โˆ’(Lโˆ’x1px1p)1โˆ’pp<0d2g(x1)dx12=โˆ’1โˆ’pp(Lโˆ’x1px1p)1โˆ’2pp[โˆ’px1pโˆ’1x1pโˆ’(Lโˆ’x1p)pxpโˆ’1x12p]=L(1โˆ’p)(x2x1)1โˆ’2px1โˆ’pโˆ’1โ‰ฅ0\begin{split} &\frac{dg(x_{1})}{dx_{1}}=\frac{1}{p}(L-x_{1}^{p})^{\frac{1}{p}-1}\left(-px_{1}^{p-1}\right)=-\left(\frac{L-x_{1}^{p}}{x_{1}^{p}}\right)^{\frac{1-p}{p}}<0\\ &\frac{d^{2}g(x_{1})}{dx_{1}^{2}}=-\frac{1-p}{p}\left(\frac{L-x_{1}^{p}}{x_{1}^{p}}\right)^{\frac{1-2p}{p}}\left[\frac{-px_{1}^{p-1}x_{1}^{p}-(L-x_{1}^{p})px^{p-1}}{x_{1}^{2}p}\right]\\ &=L(1-p)\left(\frac{x_{2}}{x_{1}}\right)^{1-2p}x_{1}^{-p-1}\geq0 \end{split}

The last inequality holds because each component is positive.

When pp= 1, it is straightward to see that the invariant function is constant sum. To show that the invariant function converges to constant product when pp= 0, we will show and prove an established result in a generalised dd dimensional setting.




(1dโˆ‘i=1dxip)1p=exp[log(1dโˆ‘i=1dxip)p]\left(\frac{1}{d}\sum{i=1}^{d}x_{i}^{p}\right)^{\frac{1}{p}}=\text{exp}\left[\frac{\text{log}\left(\frac{1}{d}\sum{i=1}^{d}x_{i}^{p}\right)}{p}\right]. Applying L'Hospital rule to the exponent,which is 0 in both denominator and nominator when pโ†’0p\rightarrow0, we have





When d = 2,


Proof of the corollary is trivial, as it is a direct application of the theorem. It shows that generalised mean AMM implies constant product AMM when pโ†’0p\rightarrow0.

Appendix 2: Liquidity Mapping to Uniswap v3

As Uniswap v3 is able to simulate liquidity curve of any AMM, we are interested in exploring the connection between ALEX's AMM and that of Uniswap's. Interesting questions include: what is the shape of the liquidity distribution? Which point(s) has the highest liquidity? We acknowledge that the section is more of a theoretical study for now.

Uniswap V3 AMM can be expressed as a function of invariant constant LL with respect to price pp, LUniswap=dydpL_{\text{Uniswap}}=\frac{dy}{d\sqrt{p}}. In terms of ALEX, as price p=e^{rt}, where rr is the implied interest rate, we have


In the previous sections, we express yy as



dydr=L11โˆ’teโˆ’(1โˆ’t)r(1+eโˆ’(1โˆ’t)r)2โˆ’t1โˆ’tLUniswap=2tL11โˆ’t(er(1โˆ’t)2+eโˆ’r(1โˆ’t)2)โˆ’2+t1โˆ’t=2tL11โˆ’t{2coshโก[r(1โˆ’t)2]}โˆ’2+t1โˆ’t\begin{split} &\frac{dy}{dr}=L^{\frac{1}{1-t}}\frac{e^{-(1-t)r}}{(1+e^{-(1-t)r})^{\frac{2-t}{1-t}}}\\ &L_{\text{Uniswap}}=\frac{2}{t}L^{\frac{1}{1-t}}\left(e^{\frac{r(1-t)}{2}}+e^{\frac{-r(1-t)}{2}}\right)^{\frac{-2+t}{1-t}}\\ &=\frac{2}{t}L^{\frac{1}{1-t}}\big\{2\cosh\left[\frac{r(1-t)}{2}\right]\big\}^{\frac{-2+t}{1-t}} \end{split}

Figure 3 plots LUniswapL_{\text{Uniswap}} against interest rate rr regarding various levels of tt. When 0<t<10<t<1, LUniswapL_{\text{Uniswap}} is symmetric around 0% at which the maximum reaches . This is because

  1. coshโก[(r(1โˆ’t)2)]\cosh\left[(\frac{r(1-t)}{2})\right] is symmetric around rr= 0% with minimum at 0% and the minimum value 1;

  2. xzx^z is a decreasing function of xx when xx is positive and power zz is negative. In our case, we have z=โˆ’2+t1โˆ’t<โˆ’1z=-2+t1-t<-1. Therefore, it is the maximum rather than minimum that LUniswapL_{\text{Uniswap}} achieves at 0.

Furthermore, the higher the tt, the flatter the liquidity distribution is. When tt approaches 1, i.e. AMM converges to the constant product formula, the liquidity distribution is close to a flat line. When tt approaches 0, the distribution concentrates around 0%. This makes sense, as forward price starts to converge to spot price upon expiration.

Appendix 3: Derivation of Actual and Virtual Token Reserve

On CEC, there are two boundary points (xbx_{b},0) and (0,yby_{b}) corresponding to the lower and upper bound of interest rate rlr_{l} and rur_{u} respectively. We assume LL is pre-determined, as liquidity provider knows the pool size. We aim to find xbx_{b}, yby_{b}, xvx_{v} and yvy_{v} which satisfy the following equations

(xb+xv)1โˆ’t+yv1โˆ’t=Lxv1โˆ’t+(yb+yv)1โˆ’t=Lyvxb+xv=erlyb+yvxv=eru\begin{split} &(x_{b}+x_{v})^{1-t}+y_{v}^{1-t}=L\\ &x_{v}^{1-t}+(y_{b}+y_{v})^{1-t}=L\\ &\frac{y_{v}}{x_{b}+x_{v}}=e^{r_{l}}\\ &\frac{y_{b}+y_{v}}{x_{v}}=e^{r_{u}} \end{split}

As there are four unknown variables with four equations, solutions can be expressed as below

xv=[L1+e(1โˆ’t)ru]11โˆ’tyv=[L1+eโˆ’(1โˆ’t)rl]11โˆ’txb=yveโˆ’rlโˆ’xv=[L1+erl(1โˆ’t)]11โˆ’tโˆ’[L1+eru(1โˆ’t)]11โˆ’tyb=xveruโˆ’yv=[L1+eโˆ’ru(1โˆ’t)]11โˆ’tโˆ’[L1+eโˆ’rl(1โˆ’t)]11โˆ’t\begin{split} &x_{v}=\left[\frac{L}{1+e^{(1-t)r_{u}}}\right]^{\frac{1}{1-t}}\\ &y_{v}=\left[\frac{L}{1+e^{-(1-t)r_{l}}}\right]^{\frac{1}{1-t}}\\ &x_{b}=y_{v}e^{-r_{l}}-x_{v}=\left[\frac{L}{1+e^{r_{l}(1-t)}}\right]^{\frac{1}{1-t}}-\left[\frac{L}{1+e^{r_{u}(1-t)}}\right]^{\frac{1}{1-t}}\\ &y_{b}=x_{v}e^{r_{u}}-y_{v}=\left[\frac{L}{1+e^{-r_{u}(1-t)}}\right]^{\frac{1}{1-t}}-\left[\frac{L}{1+e^{-r_{l}(1-t)}}\right]^{\frac{1}{1-t}} \end{split}

When rl=0r_{l}=0, the pool is floored at 0%. This means that xv=0x_{v}=0, yv=(12L)11โˆ’ty_{v}=\left(\frac{1}{2}L\right)^{\frac{1}{1-t}}, xb=yvx_{b}=y_{v}.

When the current interest rate rcr_{c} is known and rcโˆˆ[rl,ru]r_{c}\in[r_{l},r_{u}], we can calculate xax_{a} and yay_{a} satisfying the following equations. When rcโˆ‰[rl,ru]r_{c} \notin[r_{l},r_{u}], only one token exists and swapping activities are suspended.

(xv+xa)1โˆ’t+(yv+ya)1โˆ’t=Lyv+yaxv+xa=erc\begin{split} &(x_{v}+x_{a})^{1-t}+(y_{v}+y_{a})^{1-t}=L\\ &\frac{y_{v}+y_{a}}{x_{v}+x_{a}}=e^{r_{c}} \end{split}

Solution to above is

xa=[L1+erc(1โˆ’t)]11โˆ’tโˆ’xv=[L1+erc(1โˆ’t)]11โˆ’tโˆ’[L1+eru(1โˆ’t)]11โˆ’tya=[L1+eโˆ’rc(1โˆ’t)]11โˆ’tโˆ’yv=[L1+eโˆ’rc(1โˆ’t)]11โˆ’tโˆ’[L1+eโˆ’rl(1โˆ’t)]11โˆ’t\begin{split} &x_{a}=\left[\frac{L}{1+e^{r_{c}(1-t)}}\right]^{\frac{1}{1-t}}-x_{v}=\left[\frac{L}{1+e^{r_{c}(1-t)}}\right]^{\frac{1}{1-t}}-\left[\frac{L}{1+e^{r_{u}(1-t)}}\right]^{\frac{1}{1-t}}\\ &y_{a}=\left[\frac{L}{1+e^{-r_{c}(1-t)}}\right]^{\frac{1}{1-t}}-y_{v}=\left[\frac{L}{1+e^{-r_{c}(1-t)}}\right]^{\frac{1}{1-t}}-\left[\frac{L}{1+e^{-r_{l}(1-t)}}\right]^{\frac{1}{1-t}} \end{split}

At the boundary points, when rc=rlr_{c}=r_{l}, xa=xbx_{a}=x_{b} and ya=0y_{a}=0; when rc=rur_{c}=r_{u}, xa=0x_{a}=0 and ya=yby_{a}=y_{b}.

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