Automated market makers have become a mainstay of the decentralized crypto trading experience, facilitating liquidity management across hundreds of DEX platforms and protocols.
Also known as AMMs, they are the engine that sits under the hood of protocols, supporting hundreds of trade executions in seconds. They facilitate price discovery, ensuring traders can find the most optimal prices of different assets, and also help to mitigate risks and tighten the spreads across prices and assets, stabilizing some of the most volatile DeFi markets. In this way, AMMs create a more resilient and robust trading environment, where buyers and sellers can execute transactions in real-time at the most advantageous prices.
Understanding AMMs
AMMs are powered by liquidity pools, where users are incentivized to deposit cryptocurrencies into a common pot to provide enough liquidity to support high-speed trading on DeFi platforms. They use complicated algorithms to establish token prices, based on the ratio of the two assets within a specific pool. When someone attempts to execute a swap, the trade will go directly through the AMM, with the price determined by the algorithm at the exact time the order was processed.
Liquidity providers are incentivized with a share of the transaction fees for each trade that the pool facilitates, and the exact rate of the incentive is adjusted in real-time to ensure that sufficient liquidity is always available. In this way, AMMs can ensure continuous liquidity for almost any kind of asset, even less popular tokens, simply by adjusting the incentive for users.
One of the most attractive aspects of this model is that AMM liquidity pools are accessible to everyone, so anyone who is holding crypto tokens long term can deposit them into an LP to earn passive income. The algorithmic nature of AMMs is what makes them fully decentralized, which means they eliminate intermediaries to process swaps more efficiently at lower costs. This also translates to greater autonomy and privacy for users.
AMMs were a game-changer for decentralized exchange platforms. Before they became widely established, DEX platforms relied on the older orderbook system employed by centralized exchanges, but this was hugely inefficient. Because they were new, DEXs could only attract a small number of users, which meant they found it difficult to find participants willing to fulfill orders in a timely fashion.
AMMs fixed this problem by pooling the liquidity of the entire community through incentives. By having this liquidity constantly available, AMM-based DEXs can facilitate trades instantly, creating a more efficient experience.
The Inefficiencies Of AMMs
AMMs have achieved big success, helping to establish platforms such as Uniswap, Balancer, PancakeSwap and SushiSwap as some of the most widely used DeFi trading protocols. But although AMMs are now in widespread use, they are still a work in progress.
One of the main challenges with AMMs is that, in order to be effective, they need access to high-quality and high-speed market data, to ensure the accuracy of their prices and minimize the opportunities for traders to take advantage of anomalies through arbitrage.
AMMs must also have a deep understanding of crypto markets, including the various trading strategies employed by professional traders so as to minimize risk and ensure sufficient liquidity is always available. This entails the creation of complex rules based on factors such as market dynamics, moving averages, supply, demand, trading volumes and other technicalities.
Creating this kind of intelligence in AMMs is tricky, because every trader plays by their own rules, using their own sophisticated trading strategies. However, newer DeFi protocols such as Ithaca are looking to solve these challenges by integrating sophisticated matching engines into algorithmic AMMs, so their trades will always be executed at optimal rates and speeds, no matter what the market dynamics are.
The Role Of Crypto Matching Engines
One of Ithaca’s key innovations is the way it embeds its AMM into a sophisticated crypto matching engine. Crypto matching engines perform a key function in DeFi protocols, helping to match buy and sell orders submitted to an exchange. When a trader submits a buy order with their desired price and quantity, and another user makes a sell order with a compatible price and volume, the matching engine can bring them together to ensure the trade is facilitated at an optimal price for both parties.
To ensure genuine fairness, matching engines follow the principle of price-time priority, which means that orders based on similar price levels are prioritized based on the time they were placed, with the oldest orders fulfilled first – similar to the idea of first come, first served. Thus, crypto matching engines must be extremely efficient to ensure fairness and a smooth trading experience for every user. They’re required to handle hundreds, if not thousands of orders at high frequencies so as to facilitate order matching with the lowest possible latency.
Matching engines therefore must be extremely transparent to show every user that they’re adhering to the price-time principle to ensure fairness.
Ithaca’s AMM-Integrated Matching Engine
Ithaca has built a novel, composable, non-custodial options trading protocol that optimizes risk sharing across crypto options markets. It integrates its efficient matching engine with an AMM
to accommodate the most demanding and active cryptocurrency markets, ensuring sufficient liquidity is available to execute orders at more optimal prices. In this way, it creates a more responsive marketplace and provides a superior experience for traders, who can take bigger risks, safe in the knowledge that they can swiftly enter or exit any position in an instant.
In Ithaca’s model, the matching engine can facilitate trades in the most efficient way, intelligently routing orders through either the AMM or the orderbook, whichever provides the most optimal path for traders. When a user places an order on Ithaca’s exchange, the matching engine will evaluate its size and the available liquidity in the orderbook, and compare this with the AMM. If the orderbook liquidity is sufficient to complete the order, it will be fulfilled immediately, ensuring both the buy and sell order are fulfilled at the most optimal price for both users. However, if there is no matching liquidity on the orderbook, it will then be routed to the AMM, where it can be executed at a price that’s within the user’s accepted range.
If the AMM’s liquidity is not sufficient, the order will be rerouted back to the orderbook, and this loop will continue until the order is fulfilled. One advantage is that orders can be broken down into multiple traders and executed using a combination of orderbook and AMM fills, as opposed to just using one or the other.
Efficient Options Trading At Scale
Ithaca believes that this model, which fragments orders into smaller, more manageable blocks, provides a more efficient user experience that’s capable of supporting options trading at institutional-scale volumes. It believes there will be big demand for this hybrid AMM/matching engine, as existing decentralized platforms remain extremely inefficient when executing orders at higher scales. It’s a unique model that helps to aggregate liquidity across DeFi markets while enabling risk transfer to be shared across multiple assets.
Ithaca’s approach analyzes large volumes of trading data to identify superior execution opportunities, enhancing DeFi market making. This paves the way for traders to optimize their trading strategies and make more profitable trades, taking advantage of the sub-second volatility that’s inherent to crypto asset prices.
