AMM Automated Market Maker Explained Godex Crypto Blog

Here is an overview of some of the notable automated market maker protocols you could find today. It is important to ensure a match between a buy order and a sell order for the execution of a trade. You can think of such an approach as similar to that of an order book model, where all the orders sit in an order book. The order book exchange definitely presents a proven approach for global finance, which involves multiple market makers alongside many investors. Another example of an automated market maker (AMM) is PancakeSwap, the number one AMM on Binance Smart Chain (BSC). However, PancakeSwap boasts various features, including a lottery, non-fungible tokens (NFTs), and a predictions market.

This is where liquidity pools and liquidity providers (LPs) come in. In this article, we’re going to look at how AMMs work and why they are so popular. Also, we’ll look at liquidity providers (LPs), liquidity pools, and impermanent loss! Plus, we’ll discuss why AMMs are such a vital component of the crypto space. In the world of cryptocurrency exchanges, AMMs are smart contracts. Their functionality is to create pools of token liquidity that are automatically traded using an algorithm.

Automated Market Maker Variations

Once you have determined this, you can then begin to identify the persistent market inefficiencies that your strategy needs to target. As such, preliminary research focuses on developing a strategy that suits your own personal trading goals and personality traits. Added to this, if the cause of the market inefficiency is unidentifiable, then there will be no way to know if the success or failure of the strategy was due to chance or not. Obviously, you’re going to need a computer and an Internet connection to become an algorithmic trader. The best way to learn to program is to practice, practice and practice. Sound knowledge of programming languages like Python/C++/Java/R is a pre-requisite for a Quant Developer.

The following discussion offers a detailed understanding of what is an automated market maker and how it works. When liquidity providers (LPs) deposit token pairs into liquidity pools, they generally deposit an equal ratio of each asset. As in the previous example, when providing liquidity to a Uniswap liquidity pool, LPs provide an equal ratio of two different assets. But, if you deposit one ETH worth $3,000 along with 3,000 USDC, there’s no guarantee that this ratio will be the same when you withdraw your liquidity.

To summarize, Curve V2 achieves very small slippage near the equilibrium point and better slippage than CPMM in other region. As for other price pegs rather than 1, we simply changes p in the cubic / sextic equation above. Take the simplest 2-token pool for instance, the market maker function can be expressed in terms of A, γ, p, D, x, y. The function can be simplified to a cubic function with respect to x, y (a sextic function with respect to D).

Essentially, this is equivalent to solve the following formula, but this time x, y are known. Finally solving this equation for x0 gives us the new equilibrium point. The price at (x0, y0) will always be the current market price Pt by design. This, in concept, ensures that arbitrage will always bring the pool back to point (x0, y0). However, in reality, this algorithm does not have enough parameters to fit both the current reserve (x, y) and (x0, y0). This means we have to wait for the pool to go back to (x0, y0) and then change the market maker function.

Automated Market Maker Variations

We personally do not see any counter measurement in the Balancer whitepaper and docs. Curve, on the other hand, introduces something called imbalance fee which ranges from 0% to 0.02%, when depositing single-sided liquidity. In reality, there is no real incentive for depositing single-sided liquidity under the second approach, due to arbitrage and impermanent loss. It is not hard to calculate how much we need to swap so that the value of each token is equal after the swap. It is also easy to show that β is always between 0 and 1, meaning a reasonable result. However, the price after the swap can be different from the price when depositing liquidity.

Automated Market Maker Variations

To utilize such a system, a centralized API must be queried for a quote, which can then be used on-chain to execute the trade. Off-chain pricing has the advantage of allowing for the use of off-chain information. This can include any of a range of private market-making tactics, including a marginal rate structure (used by Hashflow), or more traditional order book methods used by centralized exchanges [16]. Alternatively, high-frequency tuning of AMM equations can be done to achieve better performance than their on-chain counterparts. Clipper’s FMM uses a rapidly updating live price feed as the oracle price in their AMM formula (discussed above), allowing them to shift closer to a CSMM and achieve greater capital efficiency [17].

Providing liquidity in price ranges essentially enables Uniswap V3 to be a universal AMM, with the ability to become any possible AMM by changing its liquidity distribution. As for impermanent loss derivations for other popular AMM algorithms, including Uniswap V3, we refer readers to this lovely paper by Jiahua Xu et al⁵. In the above discussion, we only considered the case where the relative price goes down. We can also calculate the exact range of k in which liquidity providers will have a positive gain. MSc in Computer Science, BSc in Smart Engineering, and BSc in Economics and Statistics.

The liquidity always equals the total quantity of token A plus the total quantity of token B. As a result, for this model to work, token A and token B need to be supplied in the correct ratio by liquidity providers, and the amount of liquidity must be sufficient. The SushiSwap team launched what is known as a “vampire attack”, whereby a protocol attempts to steal LPs from a competitor by offering better rates and rewards. SushiSwap managed to lure Uniswap LPs to the new SushiSwap protocol by offering SUSHI token rewards on top of attractive trading fees. Interactive Brokers API allows users to build their own automated trading systems using popular programming languages like Java, Python, and C++.

  • The process of earning rewards by providing liquidity is also called liquidity mining or yield farming.
  • However, PancakeSwap boasts various features, including a lottery, non-fungible tokens (NFTs), and a predictions market.
  • Liquidity pools combine the funds deposited by LPs for users of AMMs to trade against.

AMMs use liquidity pools, where users can deposit cryptocurrencies to provide liquidity. These pools then use algorithms to set token prices based on the ratio of assets in the pool. When a user wants to trade, they swap one token for another directly through the AMM, with prices determined by the pool’s algorithm. However, decentralized exchanges (DEXs) and automated market makers (AMMs) are non-custodial. Not only does this mean that users have control of their assets, but it also means that assets cannot be seized, frozen, or restricted in the same way that they can be with CEXs. When the prices of assets deposited to liquidity pools fall and the ratio of the token pairs is unfavorable, there is no way to reverse this.

Automated Market Maker Variations

Generally, a high amplification factor can work well for stable pairs but becomes riskier the more volatile a pair gets. In order to guarantee better price, the algorithm has to constantly re-peg (changing x0 and y0) to keep the current pool reserve near the (1, 1) point. The algorithm re-pegs by following its internal price oracle, which tracks the market price.

EOption also lets users set up automated trading systems, but there’s no programming language knowledge needed. Instead, eOption has a series of trading newsletters available to clients. Factors such as your personal risk profile, time commitment, and trading capital are all important considerations when developing a strategy. In addition to helping traders who are afraid to make a decision, automated trading can hold back those who tend to overtrade, and buy and sell at every perceived opportunity. In other words, challenging human decisions can be solved by a computer in milliseconds, as the computer can scan for trading opportunities across a range of markets, generate orders, and monitor trades.

These pools are vital for the ecosystem’s health, as they ensure liquidity and minimize price impacts and trading inefficiencies. AMMs incentivize asset contribution by offering a portion of transaction fees or LP tokens as rewards, a practice known as yield farming. The prices of assets in AMMs are dynamically adjusted according to supply and demand, showcasing the platforms’ decentralized and autonomous operation.

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