DEX polymerization protocol 1inch new product Mooniswap

Original title: "Mooniswap, 1inch's new product under DEX polymerization protocol"

The original source: babbitt

On August 11, decentralized trading platform (DEX) aggregation protocol 1 inch. Exchange today announced the release of automated market-makers service Mooniswap this new AMM DEX for Uniswap design is improved, it will give arbitrageurs part of income redistribution to pool of liquidity, and protect the trader from first deal, according to the simulation experiment, Mooniswap bring liquidity providers of income, is expected to be 50% to 200% more than Uniswap V2.

What is an automated market maker (AMM)?

Automated market makers (AMM) have dramatically changed the way users exchange cryptocurrencies, and liquidity pools allow users to easily switch between tokens in a fully decentralized and unmanaged manner. At the same time, the liquidity provider (LP) receives passive income from transaction fees, which is calculated in proportion to the funds it contributes to the reserve pool.

In 2017, Bancor launched its first AMM product, which allows participants to trade cryptocurrencies without relying on external data pricing. After that, innovative AMM, designed by different projects such as Uniswap, Balancer, Curve, began to emerge in DeFi.

Historically, Alan Lu first used the constant product formula to predict the market. Later, Hayden Adams used this beautiful formula to build his Uniswap, and the elegant AMM formula is:

X times y is equal to k

Where x represents the number of tokens 1, Y represents the number of tokens 2, and k is a fixed constant.

In other words, the equation expresses the dependency between two quantities of assets whose product must remain the same. With this equation, AMM can refer to the prices of two assets in a pool to maintain that their unit product is equal to a constant.

The idea was quickly embraced by market participants, with Vitalik Buterin calling for "decentralised trading platforms along the chain to be run the way we run forecast markets" and reflecting on the issue of pre-emptive trading. In fact, the AMM mechanism also brings the risk of possible losses to the trader. After that, many projects can solve these defects from different perspectives.

How does an automated market maker (AMM) work?

A constant product invariant equation balances assets in the asset pool through two surfaces. The first is the trader, who trades with the asset pool and causes the price to move, and the second is the arbitrageurs, who trade with the asset pool and bring the price back to the market price after the trader completes the trade. So we have two in and out flows for the AMM.

Generally speaking, traders are retail investors who exchange assets at the prices offered by AMM, while arbitrageurs, by contrast, are usually trading robots designed to capture profits. Arbitrageurs have more advantages than other market participants due to the high degree of certainty in the arbitrage process.

Ideally, market makers would profit from price differences if profits from falling prices returned to the pool. Pools are pre-financed, so a swap can be executed with ease. At any given time, one trader is selling and another is buying, and they cancel each other out.

Unfortunately, this does not exist in the real world, where traders who trade in advance are actually stealing money from liquidity providers (LPS) through price movements. There is no denying that the issue of front-running deals is important.

Existing problems facing AMM

The constant product invariant formula lays the foundation for many subsequent innovations in AMM design. Now AMM innovation, aimed at improving the price curve, is exploding.

The most direct implementation of the constant product formula is the AMM of Uniswap, which is completely licension-free and whose pool of assets can be market-made by anyone. Uniswap AMM does not require any external feedings from the forecaster and has little effect on pricing other than the trades executed against it. Unfortunately, when liquidity providers withdraw under adverse conditions, they suffer losses (impermanent losses), which is naturally unpleasant.

Curve, on the other hand, creates a better pricing function for the stabilizers, which consists of a constant product and a constant sum function. Since pool imbalance coefficient is embedded in both sides of the equation, AMM of Curve can be quoted more effectively. The AMM will switch between the constant sum formula and the constant product formula according to the degree of asset pool imbalance at query time.

Curve's innovation has greatly improved the DEX space, but it still hasn't solved some of the headaches.

As a pioneer in AMM field, Bancor tried to solve the problem of high sliding point and volatile loss through liquidity amplification and dynamic weight mechanism for the first time in its V2 update. However, Bancor V2's floating weight solution to mitigate volatile losses relied on a steady price quote flow from a forecaster operating outside the agreement. Bancor found this to be a key disadvantage, as the foretell machine was vulnerable to a preemptive trade attack. We agree with Bancor that a chain is only as strong as its weakest link.

Many project parties are trying to improve the AMM design with their own unique implementations.

AMM, designed by 1Inch's team, allows liquidity providers to capture a slice of the profits from price slippage.

Mooniswap profile

Mooniswap is the next generation of automated marketmakers with virtual balance, which enables liquidity providers to capture the profits of arbitrageurs.

Faced with the problem of pre-emptive trading, Vitalik Buterin recommends using a "virtual volume" solution, but in a slightly different context.

This idea is fully implemented in the design of Mooniswap. The new AMM is able to keep most of the slippage revenue in the pool by maintaining virtual balances in different interchangeable directions. When a swap occurs, the market maker does not automatically apply the invariant algorithm and display the new price for the upcoming trade. AMM slowly raised the carry traders' exchange rates over a period of about five minutes. As a result, they will recoup only a portion of the slippage, while the rest will remain in a pool Shared by liquidity providers (LPS). We believe this is a game changer.

By delaying price updates, market makers will create a highly competitive environment for arbitrageurs, forcing them to trade at less profitable prices, which in turn will increase the value received by liquidity providers.

The following figure shows how several arbitrages significantly increase the constant product invariant from X to Q:

Among them:

1. A is the initial balance;

2. X is the sliding balance after a swap transaction;

3. B is the virtual balance reversed after a period of time;

4. The true line of 0A -- 0Q price;

5. BC, DE and ZQ are arbitrage trades based on virtual balance;

6. XY, YZ-arbitrage trading and the actual balance of parallel trading;

After the swap, the virtual balance of the reverse swap begins to move linearly from Point A to point X. At some point B, the arbitrageur finds A price to pay and rebalances the asset pool by performing the swap from point B to Point C. Notice that points A and C and the origin are all on the same line, which means they have the same price. The chart shows three consecutive carry trades that continue until the actual balance at Point Q reaches the equilibrium price.

We simulated the performance of Mooniswap many times based on real world data and compared the results with Uniswap V2. Below, you can find charts of the projected results for volume, cumulative price slippage, Uniswap V2 liquidity provider revenue, and Mooniswap liquidity provider revenue. To illustrate this point, we selected some of the most liquid markets on Uniswap V2:


Figure: Uniswap V2 LP income versus Mooniswap LP income for different asset pools

On average, we expect Mooniswap to generate 50% to 200% more revenue for liquidity providers than Uniswap V2 due to the price slide profit readjustment.

Swap transactions and referral fees

Mooniswap initially set a fee of 0.3 per cent for swaps, which could be gradually reduced to 0 per cent in the future, to provide a more competitive way of trading.

Mooniswap has introduced the concept of referral fees to encourage integration with wallets and other services, increasing additional revenue for transaction and liquidity providers. A recommendation fee is charged only if a recommendation wallet is specified in the transaction parameters.

The referral fee is fixed and is equal to 5 per cent of the income earned by the liquidity provider on the transaction. As a result, 0.015 per cent of the initial 0.3 per cent swap fee will be recommended, leaving 0.285 per cent for liquidity providers. The extra profit generated by the virtual balance is also distributed in the same proportion, with 5 per cent going to the recommendation fee.

Referral fees do not put additional pressure on prices and can reward outside participants who contribute to the agreement by providing volume.

Mooniswap will not charge any additional protocol fees other than exchange and referral fees.

VWAP predicted machine

Mooniswap has also introduced a trade-weighted average price forecaster on the chain whose data is stored as a cumulative sum of all trade inputs and outputs in both directions and updated after each trade. By selecting different time periods, the user of the predictor can configure the desired degree of price shortness and resistance to manipulation. We believe that because Mooniswap USES a virtual balance mechanism, the VWAP predictor will be difficult to manipulate by malicious participants.

It should be noted that Mooniswap's user interface references Uniswap, which indicates that Uniswap's user interface has proven to be intuitive, reliable, and well-liked by many users.

Text link:https://www.8btc.com/article/633734

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