Mathematical Principle of TWAMM ============================================== ## Definition Assume that **TWAMM** requires $N$ blocks to execute a large virtual transaction, and that the pool selling $X$ sells at a rate of $x_{rate}$ per block, while the pool selling $Y$ sells at a rate of $y_{rate}$ per block. Thus, the total amount of $X$ sold during the whole period is $x_{in}=Nx_{rate}$ and the total amount of $Y$ sold is $y_{in}=Ny_{rate}$. Also, we note the initial reserves $x_{reserve}$ and $y_{reserve}$ for this time period in embeded **AMM** as $x_{0}=x_{ammStart}$ and $y_{0}=y_{ammStart}$, respectively. According to the design of **TWAMM**, large orders are traded with blocks, each block sells $x_{rate}$ to get $y_{out}$, or sells $y_{rate}$ to get $x_{out}$, while **AMM** updates the values of $x_{reserve}$ and $y_{reserve}$, the whole process $N$ transactions in total. It is worth noting that each block transaction of **AMM** always follows a constant product market making. ![](https://i.imgur.com/mNQQqAm.png) ## Formula First, after executing the transaction for the $n-1$ block, we assume that the $x_{reserve}$ and $y_{reserve}$ values of **AMM** are $x_{n-1}$ and $y_{n-1}$, respectively. Next, the transaction for block $n$ is executed, with $X-Pool$ and $Y-Pool$ feeding $x_{rate}$ and $y_{rate}$ to **AMM**, respectively. Then, $$\overline{x}_{n}=x_{n-1}+x_{rate}$$ $$\overline{y}_{n}=y_{n-1}+y_{rate}$$ Since $x_{rate}$ and $y_{rate}$ are very small and the exchange follow a constant product market maker, we can obtain that, $$\dfrac{x_{out,n}}{y_{rate}}=\dfrac{\overline{x}_{n}}{\overline{y}_{n}}=\dfrac{x_{n-1}+x_{rate}}{y_{n-1} +y_{rate}}$$ $$\dfrac{y_{out,n}}{x_{rate}}=\dfrac{\overline{y}_{n}}{\overline{x}_{n}}=\dfrac{y_{n-1}+y_{rate}}{x_{n-1}+ x_{rate}}$$ Simplifying, $$x_{out,n}=y_{rate}\cdot\dfrac{\overline{x}_{n}}{\overline{y}_{n}}=y_{rate}\cdot\dfrac{x_{n-1}+x_{rate}}{y_{n-1}+y_{rate}}$$ $$y_{out,n}=x_{rate}\cdot\dfrac{\overline{y}_{n}}{\overline{x}_{n}}=x_{rate}\cdot\dfrac{y_{n-1} +y_{rate}}{x_{n-1}+x_{rate}}$$ After getting the values of $x_{out,n}$ and $y_{out,n}$, we can further obtain the $x_{reserve}$ and $y_{reserve}$ values $x_{n}$ and $y_{n}$ after the transaction of $n$ blocks. $$x_{n}=\overline{x}_{n}-x_{out,n}=\overline{x}_{n}- y_{rate}\cdot\dfrac{\overline{x}_{n}}{\overline{y}_{n}}=y_{n-1}\cdot\dfrac{\overline{x }_{n}}{\overline{y}_{n}}= y_{n-1}\cdot\dfrac{x_{n-1}+x_{rate}}{y_{n-1}+y_{rate}}$$ $$y_{n}=\overline{y}_{n}-y_{out,n}=\overline{y}_{n}- x_{rate}\cdot\dfrac{\overline{y}_{n}}{\overline{x}_{n}}=x_{n-1}\cdot\dfrac{\overline{y }_{n}}{\overline{x}_{n}}=x_{n-1}\cdot\dfrac{y_{n-1}+y_{rate}}{x_{n-1}+x_{rate}}$$ $$Average\ Price\ P^{x}_{y}=\sum_{n=1}^{N}\dfrac{x_{out,n}}{Ny_{rate}}=\dfrac{x_{out}}{y_{in}}$$ $$Average\ Price\ P^{y}_{x}=\sum_{n=1}^{N}\dfrac{y_{out,n}}{Nx_{rate}}=\dfrac{y_{out}}{x_{in}}$$ By observation, we find that $x_{n}\cdot y_{n}= x_{n-1}\cdot y_{n-1}$, which exactly meets the prerequisite requirement of **AMM** for making a market by following a constant product. Let $x_{n}y_{n}=x_{n-1}y_{n-1}=... = x_{1}y_{1}=x_{0}y_{0}=k$, $k$ is a constant. ## Fractional Linear Recursion First find the general formula for $x_{n}$ and obtain the value of $x_{ammEnd}=x_{N}$, and the same for $y_{n}$. $$x_{n}=y_{n-1}\cdot\dfrac{x_{n-1}+x_{rate}}{y_{n-1} +y_{rate}}=\dfrac{k}{x_{n-1}}\cdot\dfrac{x_{n-1} + \dfrac{x_{in}}{N}}{\dfrac{k}{x_{n-1}}+\dfrac{y_{in}}{N}}=k\cdot\dfrac{x_{n-1}+\dfrac{x_{in}}{N}}{\dfrac{y_{in}}{N}\cdot x_{n-1}+k}$$ Let $a=\sqrt{\dfrac{kx_{in}}{y_{in}}},\overline{a}=\sqrt{\dfrac{ky_{in}}{x_{in}}},b=\sqrt{\dfrac{x_{in}y_{in}}{k}}$, then $$x_{n}=\dfrac{x_{n-1}+\dfrac{ab}{N}}{\dfrac{b}{aN}\cdot x_{n-1}+1}$$ The fractional linear recursive expression for $y_{n}$ is as follows: $$y_{n}=k\cdot\dfrac{y_{n-1}+\dfrac{y_{in}}{N}}{\dfrac{x_{in}}{N}\cdot y_{n-1}+k}=\dfrac{y_{n-1}+ \dfrac{\overline{a}b}{N}}{\dfrac{b}{\overline{a}N}\cdot y_{n-1}+1}$$ ## Solution First, if $y_{in}=0$ and $x_{in}\neq0$ , then $$x_{n}=x_{0}+\dfrac{x_{in}}{N}\cdot n,x_{ammEnd}=x_{N}=x_{0}+x_{in},x_{out}=x_{0}+x_{in}-x_{ammEnd}=0$$ And, $$y_{n}=k\cdot\dfrac{y_{n-1}}{\dfrac{x_{in}}{N}\cdot y_{n-1}+ k},\dfrac{1}{y_{n}}=\dfrac{1}{y_{n-1}}+\dfrac{x_{in}}{kN}$$ The calculation yields, $$y_{n}=\dfrac{1}{\dfrac{1}{y_{0}}+\dfrac{x_{in}}{kN}\cdot n},y_{ammEnd}=y_{N}=\dfrac{k}{x_{0}+x_{in}}$$ $$y_{out}=y_{0}+y_{in}-y_{ammEnd}=\dfrac{y_{0}}{x_{0}+x_{in}}\cdot x_{in}$$ $$Average\ Price\ P^{y}_{x}=\dfrac{y_{out}}{x_{in}}=\dfrac{y_{0}}{x_{0}+x_{in}}$$ --- When $x_{in}=0$ and $y_{in}\neq0$ , the discussion is the same as above. --- If both $x_{in}\neq0$ and $y_{in}\neq0$, for general fractional linear recursion, we can use the [**Fixed-Point Iteration**](https://en.wikipedia.org/wiki/Fixed-point_iteration) method to solve the problem. According to $x_{n}=\dfrac{x_{n-1}+\dfrac{ab}{N}}{ \dfrac{b}{aN}\cdot x_{n-1}+1}$, replace $\lambda=\dfrac{\lambda+\dfrac{ab}{N}}{\dfrac{b}{aN}\cdot\lambda+1}$ and solve for $\lambda=\pm{a}$. $\pm{a}$ is exactly the two fixed points of the above fractional linear recursive expression. Further calculations, $$x_{n}-a=\dfrac{a(\dfrac{N}{b}-1)(x_{n-1}-a)}{x_{n-1}+\dfrac{aN}{b}},x_{n}+a=\dfrac{a(\dfrac{N}{b}+1)(x_{n-1}+a)}{x_{n-1}+\dfrac{aN}{b}}$$ Dividing the two expressions above, $$\dfrac{x_{n}-a}{x_{n}+a}=\dfrac{N-b}{N+b}\cdot\dfrac{x_{n-1}-a}{x_{n-1}+a}=(1-\dfrac{2b}{N+b})\cdot\dfrac{x_{n-1}-a}{x_{n-1}+a}$$ According to the geometric progression we get, $$\dfrac{x_{n}-a}{x_{n}+a}=(1-\dfrac{2b}{N+b})^{n}\cdot\dfrac{x_{0}-a}{x_{0}+a},\dfrac{x_{N}-a}{x_{N}+a}=(1-\dfrac{2b}{N+b})^{ N}\cdot\dfrac{x_{0}-a}{x_{0}+a}$$ Similarly, $$\dfrac{y_{n}-\overline{a}}{y_{n}+\overline{a}}=(1-\dfrac{2b}{N+b})^{n}\cdot\dfrac{y_{0}-\overline{a}}{y_{0}+\overline{a}},\dfrac{y_{N}-\overline{a}}{y_{N}+\overline{a}}=(1-\dfrac{2b}{N+b})^{N}\cdot\dfrac{y_{0}-\overline{a}}{y_{0}+\overline{a}}$$ ## Limit Value The basic principle of **TWAMM** is to decompose long term large orders into infinitely many infinitely small virtual orders, i.e. $N$ can be taken to infinity, so the limit value of $x_{N}$ can be obtained. $$\dfrac{x_{ammEnd}-a}{x_{ammEnd}+a}=\lim_{N\rightarrow+\infty}(\dfrac{x_{N}-a}{x_{N}+a})=\lim_{N\rightarrow+\infty}(1-\dfrac{2b}{N+b})^{N}\cdot\dfrac{x_{0}-a}{x_{0}+a}=e^{-2b}\cdot\dfrac{x_{0}-a}{x_{0}+a}$$ > $$x_{ammEnd}=a\cdot\dfrac{e^{2b}+c}{e^{2b}-c}=\sqrt{\dfrac{kx_{in}}{y_{in}}}\cdot\dfrac{e^{2\sqrt{\dfrac{x_{in}y_{in}}{k}}}+c}{e^{2\sqrt{\dfrac{x_{in}y_{in}}{k}}}-c}$$ Where， > $$c=\dfrac{x_{0}-a}{x_{0}+a}=\dfrac{\sqrt{x_{ammStart}\cdot y_{in}}-\sqrt{y_{ammStart}\cdot x_{in}}}{\sqrt{x_{ammStart}\cdot y_{in}}+\sqrt{y_{ ammStart}\cdot x_{in}}}$$ > > $$x_{out}=x_{ammStart}+x_{in}-x_{ammEnd}$$ > > $$Average\ Price\ P^{x}_{y}=\dfrac{x_{out}}{y_{in}}=\dfrac{x_{ammStart}+x_{in}-x_{ammEnd}}{y_{in}}$$ Similarly, > $$y_{ammEnd}=\sqrt{\dfrac{ky_{in}}{x_{in}}}\cdot\dfrac{e^{2\sqrt{\dfrac{x_{in}y_{in}}{k}}}+\overline{c}}{e^{2\sqrt{\dfrac{x_{in}y_{in}}{k}}}-\overline{c}}$$ > > $$\overline{c}=\dfrac{\sqrt{y_{ammStart}\cdot x_{in}}-\sqrt{x_{ammStart}\cdot y_{in}}}{\sqrt{x_{ammStart}\cdot y_{in}}+\sqrt{y_{ammStar t}\cdot x_{in}}}=-c$$ > > $$y_{out}=y_{ammStart}+y_{in}-y_{ammEnd}$$ > > $$Average\ Price\ P^{y}_{x}=\dfrac{y_{out}}{x_{in}}=\dfrac{y_{ammStart}+y_{in}-y_{ammEnd}}{x_{in}}$$ An important point is that，$x_{out}$, $y_{out}$, $x_{ammEnd}$ and $y_{ammEnd}$ must larger than 0: > $$e^{2\sqrt{\dfrac{x_{in}y_{in}}{k}}}>\left\lvert c\right\rvert=\left\lvert \dfrac{\sqrt{x_{ammStart}\cdot y_{in}}-\sqrt{y_{ammStart}\cdot x_{in}}}{\sqrt{x_{ammStart}\cdot y_{in}}+\sqrt{y_{ ammStart}\cdot x_{in}}}\right\rvert$$ Finally, after a simple verification $x_{ammEnd}\cdot y_{ammEnd}=x_{ammStart}\cdot y_{ammStart}=k$, the constant product of **AMM** is still satisfied. When $y_{in}\rightarrow0, x_{in}\neq0$，through **[L'Hôpital's Rule](https://en.wikipedia.org/wiki/L%27H%C3%B4pital%27s_rule)**, we can obtain: $$y_{ammEnd}=\lim_{y_{in}\rightarrow0}\left(\sqrt{\dfrac{ky_{in}}{x_{in}}}\cdot\dfrac{e^{2\sqrt{\dfrac{x_{in}y_{in}}{k}}}+\overline{c}}{e^{2\sqrt{\dfrac{x_{in}y_{in}}{k}}}-\overline{c}}\right)$$ $$=\lim_{y_{in}\rightarrow0}\left(\sqrt{\dfrac{ky_{in}}{x_{in}}}\cdot\dfrac{e^{2\sqrt{\dfrac{x_{in}y_{in}}{k}}}+\dfrac{\sqrt{y_{ammStart}\cdot x_{in}}-\sqrt{x_{ammStart}\cdot y_{in}}}{\sqrt{x_{ammStart}\cdot y_{in}}+\sqrt{y_{ammStar t}\cdot x_{in}}}}{e^{2\sqrt{\dfrac{x_{in}y_{in}}{k}}}-\dfrac{\sqrt{y_{ammStart}\cdot x_{in}}-\sqrt{x_{ammStart}\cdot y_{in}}}{\sqrt{x_{ammStart}\cdot y_{in}}+\sqrt{y_{ammStar t}\cdot x_{in}}}}\right)$$ $$=\dfrac{1}{\dfrac{1}{y_{ammStart}}+\dfrac{x_{in}}{k}}=\dfrac{y_{ammStart}\cdot x_{ammStart}}{x_{ammStart}+x_{in}}$$ $$x_{ammEnd}=\lim_{y_{in}\rightarrow0}\left(\sqrt{\dfrac{kx_{in}}{y_{in}}}\cdot\dfrac{e^{2\sqrt{\dfrac{x_{in}y_{in}}{k}}}-\overline{c}}{e^{2\sqrt{\dfrac{x_{in}y_{in}}{k}}}+\overline{c}}\right)$$ $$=\lim_{y_{in}\rightarrow0}\left(\sqrt{\dfrac{kx_{in}}{y_{in}}}\cdot\dfrac{e^{2\sqrt{\dfrac{x_{in}y_{in}}{k}}}-\dfrac{\sqrt{y_{ammStart}\cdot x_{in}}-\sqrt{x_{ammStart}\cdot y_{in}}}{\sqrt{x_{ammStart}\cdot y_{in}}+\sqrt{y_{ammStar t}\cdot x_{in}}}}{e^{2\sqrt{\dfrac{x_{in}y_{in}}{k}}}+\dfrac{\sqrt{y_{ammStart}\cdot x_{in}}-\sqrt{x_{ammStart}\cdot y_{in}}}{\sqrt{x_{ammStart}\cdot y_{in}}+\sqrt{y_{ammStar t}\cdot x_{in}}}}\right)$$ $$={x_{ammStart}+x_{in}}$$ ## Differential Equation Of TWAMM Assume the trading rate of $X-Pool$ and $Y-Pool$ is uniform according to time， $$\overline{x}_{new}=x_{old}+\dfrac{x_{in}}{T}dt$$ $$\overline{y}_{new}=y_{old}+\dfrac{y_{in}}{T}dt$$ $$x_{new}=y_{old}\cdot\dfrac{\overline{x}_{new}}{\overline{y}_{new}}= y_{old}\cdot\dfrac{x_{old}+\dfrac{x_{in}}{T}dt}{y_{old}+\dfrac{y_{in}}{T}dt}$$ $$y_{new}=x_{old}\cdot\dfrac{\overline{y}_{new}}{\overline{x}_{new}}= x_{old}\cdot\dfrac{y_{old}+\dfrac{y_{in}}{T}dt}{x_{old}+\dfrac{x_{in}}{T}dt}$$ $$x_{new}=y_{old}\cdot\dfrac{x_{old}+\dfrac{x_{in}}{T}dt}{y_{old}+\dfrac{y_{in}}{T}dt}=\dfrac{k}{x_{old}}\cdot\dfrac{x_{old} + \dfrac{x_{in}}{T}dt}{\dfrac{k}{x_{old}}+\dfrac{y_{in}}{T}dt}=k\cdot\dfrac{x_{old}+\dfrac{x_{in}}{T}dt}{\dfrac{y_{in}}{T}dt\cdot x_{old}+k}$$ Let $a=\sqrt{\dfrac{kx_{in}}{y_{in}}},\overline{a}=\sqrt{\dfrac{ky_{in}}{x_{in}}},b=\sqrt{\dfrac{x_{in}y_{in}}{k}}$, then, $$x_{new}=\dfrac{x_{old}+\dfrac{ab}{T}dt}{\dfrac{b}{aT}dt\cdot x_{old}+1}$$ $$\dfrac{x_{new}-a}{x_{new}+a}=\left(1-\dfrac{\dfrac{2b}{T}dt}{1+\dfrac{b}{T}dt}\right)\cdot\dfrac{x_{old}-a}{x_{old}+a}$$ $$\dfrac{\dfrac{x_{new}-a}{x_{new}+a}-\dfrac{x_{old}-a}{x_{old}+a}}{\dfrac{x_{old}-a}{x_{old}+a}}=-\dfrac{\dfrac{2b}{T}dt}{1+\dfrac{b}{T}dt}$$ $$\dfrac{\dfrac{x+dx-a}{x+dx+a}-\dfrac{x_{}-a}{x_{}+a}}{\dfrac{x_{}-a}{x_{}+a}}=-\dfrac{\dfrac{2b}{T}dt}{1+\dfrac{b}{T}dt}$$ $$\lim_{dt\rightarrow0}\dfrac{\dfrac{x+dx-a}{x+dx+a}-\dfrac{x_{}-a}{x_{}+a}}{\dfrac{x_{}-a}{x_{}+a}\cdot dt}=\lim_{dt\rightarrow0}\dfrac{-\dfrac{2b}{T}}{1+\dfrac{b}{T}dt}$$ $$\dfrac{d\left(ln\dfrac{x-a}{x+a}\right)}{dt}=-\dfrac{2b}{T}$$ $$ln\dfrac{x_{ammEnd}-a}{x_{ammEnd}+a}-ln\dfrac{x_{ammStart}-a}{x_{ammStart}+a}=\int ^{T}_{0}-\dfrac{2b}{T}dt=-2b$$ $$\dfrac{x_{ammEnd}-a}{x_{ammEnd}+a}=e^{-2b}\cdot\dfrac{x_{ammStart}-a}{x_{ammStart}+a}$$ $$x_{ammEnd}=a\cdot\dfrac{e^{2b}+c}{e^{2b}-c}=\sqrt{\dfrac{kx_{in}}{y_{in}}}\cdot\dfrac{e^{2\sqrt{\dfrac{x_{in}y_{in}}{k}}}+c}{e^{2\sqrt{\dfrac{x_{in}y_{in}}{k}}}-c}$$ Where， $$c=\dfrac{x_{0}-a}{x_{0}+a}=\dfrac{\sqrt{x_{ammStart}\cdot y_{in}}-\sqrt{y_{ammStart}\cdot x_{in}}}{\sqrt{x_{ammStart}\cdot y_{in}}+\sqrt{y_{ ammStart}\cdot x_{in}}}$$ ------------------------- If we use $f(t)$ and $g(t)$ instead of $\dfrac{x_{in}}{T}$ and $\dfrac{y_{in}}{T}$, that is , the trading rate of $X-Pool$ and $Y-Pool$ is a function of time. $$x_{new}=k\cdot\dfrac{x_{old}+f(t)dt}{g(t)dt\cdot x_{old}+k}$$ Let $a=\sqrt{\dfrac{kf(t)}{g(t)}},\overline{a}=\sqrt{\dfrac{kg(t)}{f(t)}},b=\sqrt{\dfrac{f(t)g(t)}{k}}$, then, $$x_{new}-x_{old}=\dfrac{ab\cdot dt-\dfrac{b}{a}dt\cdot x_{old}^{2}}{\dfrac{b}{a}dt\cdot x_{old}+1}$$ $$\dfrac{x_{new}-x_{old}}{dt}=\dfrac{ab-\dfrac{b}{a}\cdot x_{old}^{2}}{\dfrac{b}{a}dt\cdot x_{old}+1}$$ $$\lim_{dt\rightarrow0}\left(\dfrac{x_{new}-x_{old}}{dt}\right)=\lim_{dt\rightarrow0}\left(\dfrac{ab-\dfrac{b}{a}\cdot x_{old}^{2}}{\dfrac{b}{a}dt\cdot x_{old}+1}\right)$$ $$\dfrac{dx}{dt}=ab-\dfrac{b}{a}\cdot x^{2}=f(t)-\dfrac{g(t)}{k}x^2=\dfrac{g(t)}{k}\cdot \left(\dfrac{kf(t)}{g(t)}-x^2\right)$$ Let $h(t)=\dfrac{f(t)}{g(t)}=\dfrac{a^2}{k}$, > $$\dfrac{dx}{dt}=\dfrac{g(t)}{k}\cdot\left(k\cdot h(t)-x^2\right)$$ If $a$ is constant, i.e.the trading rate of $X-Pool$ and $Y-Pool$ is synchronized. $$\dfrac{d(ln\dfrac{x-a}{x+a})}{dt}=-2\sqrt{\dfrac{f(t)g(t)}{k}}$$ $$ln\dfrac{x_{ammEnd}-a(T)}{x_{ammEnd}+a(T)}-ln\dfrac{x_{ammStart}-a(0)}{x_{ammStart}+a(0)}=\int^{T}_{0}-2\sqrt{\dfrac{f(t)g(t)}{k}}dt$$ > $$x_{ammEnd}=\sqrt{\dfrac{kf(T)}{g(T)}}\cdot\dfrac{e^{2\int^{T}_{0}\sqrt{\dfrac{f(t)g(t)}{k}}dt}+c}{e^{2\int^{T}_{0}\sqrt{\dfrac{f(t)g(t)}{k}}dt}-c}$$ Where， > $$c=\dfrac{x_{ammStart}-a(0)}{x_{ammStart}+a(0)}=\dfrac{x_{ammStart}-\sqrt{\dfrac{kf(0)}{g(0)}}}{x_{ammStart}+\sqrt{\dfrac{kf(0)}{g(0)}}}=\dfrac{\sqrt{x_{ammStart}\cdot g(0)}-\sqrt{y_{ammStart}\cdot f(0)}}{\sqrt{x_{ammStart}\cdot g(0)}+\sqrt{y_{ ammStart}\cdot f(0)}}$$ At this point, we have completed a rigorous argument and explanation of the mathematical principles of **TWAMM** and obtained exactly the same conclusion as in the article [[**The Time-Weighted Average Market Maker - TWAMM**]](https://www.paradigm.xyz/2021/07/twamm/). ## 参考 - [1] [The Time-Weighted Average Market Maker - TWAMM](https://www.paradigm.xyz/2021/07/twamm/)
Last changed by  
 
Showen Peng
2124

Read more

bayes.game 去中心化版

bayes.game 去中心化版

2/19/2024
Mathematical Principle of TWAMM Concentrated Liquidity

The core of Uniswap V3 is to provide liquidity through intervals (Concentrated Liquidity), where liquidity providers add liquidity to a range that has the potential for price fluctuations to improve capital utilization. TWAMM automated market makers as a new type of AMM protocol can also use Concentrated Liquidity. Capital Utilization Let's look at the utilization rate of Uniswap V2 using non-concentrated liquidity. The above graph shows the volume change curve of $x-y$ in the pool. The current price in the pool is at $c$ and is assumed to fluctuate between $a$ price point and $b$ price point. Sliding from point $c$ to point $a$ consumes the maximum $y_{real}$ and sliding from point $c$ to point $b$ consumes the maximum $x_{real}$. That is, the current price $c$ point, oscillating between $a$ and $b$ points, consumes only $x_{real}$ and $y_{real}$ at maximum. Theoretically it is enough to provide $x_{real}$ and $y_{real}$. And in fact, as shown above, at the price $c$ point, $x$ and $y$ are provided greater than $x_{real}$ and $y_{real}$, respectively. It is obvious that the funds $x-x_{real}$ and $y-y_{real}$ are never used in this case, which is also called idle funds. In this case, the capital utilization rate is $\dfrac{x_{real}}{x}$ or $\dfrac{y_{real}}{y}$. If the price fluctuation is very small, the capital utilization is very low. How to add liquidity in a certain price interval and provide Swap function is the focus point of Uniswap V3, which needs to start with the virtual Reserves.

4/1/2022
TWAMM 集中流动性的数学原理

Uniswap V3 核心是通过盘口区间提供流动性（集中式流动性），流动性提供者将流动性添加到有可能价格波动到的区间范围，提高资金使用率。 TWAMM 做市商作为新型的 AMM 协议同样可以采用集中流动性（Concentrated Liquidity）。 资金利用率 先看看 Uniswap V2 使用非集中流动性的资金利用率。 上图为资金池中的 $x-y$ 的量变化曲线。资金池中的当前价格在 $c$ 点，并且假设会在 $a$ 价格点和 $b$ 价格点之间波动。从 $c$ 点向 $a$ 点滑动，消耗最大 $y_{real}$，从 $c$ 点向 $b$ 点滑动，消耗最大为 $x_{real}$。也就是说，当前价格 $c$ 点，在 $a$ 点和 $b$ 点之间震荡的话，最大只需要消耗 $x_{real}$ 和 $y_{real}$。理论上只要提供 $x_{real}$ 和 $y_{real}$ 就足够了。而事实上，如上图所示，在价格 $c$ 点，分别提供了大于 $x_{real}$ 和 $y_{real}$ 的 $x$ 和 $y$。明显可以看出，$x-x_{real}$ 和 $y-y_{real}$ 的资金在这种情况下是永远用不上的，也就称为闲置资金。在这种情况下，资金利用率为 $\dfrac{x_{real}}{x}$ 或者 $\dfrac{y_{real}}{y}$。如果价格波动非常小的话，资金利用率是非常低的。 如何在某个区间添加流动性并提供 Swap 功能是 Uniswap V3 的重点，这需要先从虚拟资金池说起。

3/29/2022
TWAMM 做市商的数学原理

最近，Paradigm 研究合伙人 Dave White、Dan Robinson 与 Uniswap 创始人 Hayden Adams 一起合作设计了一个全新的做市模型「时间加权平均做市商 TWAMM」（Time-Weighted Average Market Maker）。根据其发表文章介绍，TWAMM 可以在以太坊上有效地交易大额订单，工作原理是将长期大额订单分解为无限多个无限小的虚拟订单，在一定时间内使用嵌入式 AMM 平滑地执行这些交易。 关于 TWAMM 的虚拟交易所涉及的数学，Dave White 在文中并没有多费笔墨，只在最后给出了非常简单的数学结论，这对于理解 TWAMM 的数学原理非常不利。本文将重点对 TWAMM 的数学原理进行严格的论证和解释，至于 TWAMM 模型详细的设计原理，可以前往 Paradigm 官网查看:https://www.paradigm.xyz/2021/07/twamm/，本文不再做详述。 定义 假设 TWAMM 执行大额虚拟交易需要 $N$ 个区块，出售 $X$ 的池子以每区块 $x_{rate}$ 的速率出售，而出售 $Y$ 的池子以每个区块 $y_{rate}$ 的速率出售。因此，在整个期间售出的 $X$ 总量为 $x_{in}=Nx_{rate}$，售出的 $Y$ 总量为 $y_{in}=Ny_{rate}$。 同时，我们将此时间段嵌入 AMM 的初始储备 $x_{reserve}$ 和 $y_{reserve}$ 分别表示为 $x_{0}=x_{ammStart}$ 以及 $y_{0}=y_{ammStart}$。 按照 TWAMM 的设计，大额订单是随着区块进行交易的，每个区块出售 $x_{rate}$ 得到 $y_{out}$，或者出售 $y_{rate}$ 得到 $x_{out}$，同时 AMM 会更新 $x_{reserve}$ 和 $y_{reserve}$ 的值，整个过程总共交易 $N$ 次。

11/23/2021
Read more from Showen Peng
Published on HackMD