Maverick 101: Capital Efficiency and Concentrated Liquidity

Welcome to Maverick 101, a regular blog series where we explain some of the fundamental concepts of DeFi, and some of the specific features of Maverick itself!

In a previous 101 post, we explored how concentrated liquidity AMMs like Uniswap V3 represented a significant innovation in DeFi–one that hoped to improve on the capital efficiency of the constant product model, which necessarily left a lot of Liquidity Providers’ (LPs) capital stagnant.

But this new type of AMM is not without its own drawbacks. In speaking to LPs, we have found that many of them remain confused by the complexity of concentrated liquidity, and have struggled to find the optimal staking strategies for maximizing returns using AMMs like Uniswap V3.

What makes concentrated liquidity so complicated? What do we mean by optimal staking strategies? Read on as we take a deeper dive into how concentrated liquidity AMMs work!

From Constant Product to Concentrated Liquidity

First, a quick refresher on how concentrated liquidity models work.

We’ll start with the problem they were designed to address: stagnant liquidity in constant product AMMs. Part of the simplicity of the constant product AMM is that it distributes an LP’s capital evenly across a range from zero to infinity. That means that if you add liquidity to a constant product AMM, you do not have to think about how that liquidity should be apportioned: an equal amount of it will be made available to traders at every point on the price curve used by that AMM.

In addition to being simple, this also naturally protects LPs from Impermanent Loss (IL), since it is impossible for arbitrageurs to make large swaps with the pool without moving the price significantly. (If you want to learn more about IL, check out our 101 post on the subject!)

Liquidity distribution in constant product AMMs. Liquidity is spread across a wide range, meaning most of it is never used.

This simplicity comes at the cost of slippage: that same price movement that protects LPs from IL also means that traders often find themselves paying more than they would expect for their trades.

Perhaps more importantly for LPs, it also means a lot of their liquidity never gets put to use, since so much of it is distributed towards either end of the price curve (i.e., from zero to infinity). Since even the most volatile pool is unlikely to drift towards either end, this leaves a lot of an LP’s capital stagnant. Essentially, LPs are required to stake a lot of capital that will never see any use, which is what we mean when we talk about capital being inefficient.

Concentrated liquidity models aim to solve this problem by allowing LPs to concentrate their liquidity at points along the price curve. On Uniswap V3, for example, LPs choose a liquidity distribution range defined by ticks, each representing a 0.01% difference in price. When an LP adds liquidity to Uniswap V3, they choose their ticks and their liquidity is distributed evenly within that range.

Theoretically, this allows LPs to concentrate their liquidity in price ranges that see a lot of trading activity, which will put more of their capital work generating fees for them. In practice, the question of how to choose your ticks can get quite complicated. Let’s look at a few examples.

Safety is Inefficient

Perhaps the most basic strategy that an LP could adopt in a concentrated liquidity AMM is to stake a relatively wide range around the current pool price. This strategy would concentrate their capital more effectively than the infinite distribution of constant product, but still afford them some protection from IL in the event there is a sudden price swing.

Choosing a wide range in a concentrated liquidity AMM is safer, but still leaves a lot of liquidity unutilized.

But a strategy like this really only represents a mitigation of the problems with staking constant product AMMs, as a lot of capital is still being left stagnant on either side of the price range. Since the LP’s capital is distributed evenly within the defined range, they’re still only collecting fees on the portion that is within the active price range. By distributing their liquidity so widely, the LP is still not capital efficient.

The Trade Off for High Efficiency

If an LP’s goal is to earn as many fees as possible from their liquidity, then it makes sense that the optimal strategy would be to concentrate that liquidity in a much narrower range around the current price. By making more liquidity available in that range, they should see greater utilization and a concomitant increase in the fees generated.

By concentrating it within a more narrow range around the current price, the LP can put more of their liquidity to work and thus earn more fees.

So long as the price stays within a fairly narrow range within their distribution–for example, during a sideways market–the LP will essentially be printing money. Traders will buy and sell in that range, moving the price back and forth through the LP’s distribution and generating lots of fees in the process. The LP’s IL will be limited, since they stand a good chance of being able to exit the pool with an asset distribution that is close–if not exactly equal–to what they entered with.

But if there is a sudden change in the market price of one of the assets in the pool, this strategy leaves the LP exposed to potentially high IL. The narrow distribution cannot absorb market swings, which could quickly move the price all the way through the LP’s distribution and leave them completely swapped out from one asset to another. Given the way markets work, this will usually leave them holding the less valuable asset in the pair, which is what causes IL.

In this diagram, the price of the base asset has increased dramatically (from the “Old Price” to the “New Price”), driving price activity all the way through the LP’s concentration and leaving them holding only the quote asset, which is no longer generating fees.

Basically, by making more of their liquidity available in this narrow range, the LP has implicitly agreed to sell more of it at this price. If the price moves higher, this provides a fertile opportunity for arbitrageurs to empty the LP’s position of the more valuable asset. The LP ends up holding the under-performing asset. Not only does this cause IL, it also means the LP isn’t earning any fees on whatever they have left, since trading activity has now moved outside of their active range.

The bet an LP makes is that they will make up for any IL they experience in fees. The problem with AMMs like Uniswap V3 is that the liquidity does not follow the price. So the LP that is all in quote because the price has moved up would now need to move their liquidity to follow the price up in order to pursue fees to offset their IL. The net result is that Uniswap V3 is not very capital efficient if the price moves unless the LP manually intervenes to move their liquidity.

Towards a Higher Capital Efficiency Strategy

Ideally, an LP would find a strategy that enables them to have high capital efficiency even as the price moves up or down. Maverick AMM can facilitate strategies like this because it automatically moves liquidity so that it follows the price. Here’s an example of what that might look like:

Diagram showing a strategy designed to follow the price of a high-performing base asset up. The LP has staked a lot more quote than base, in order to position their range on the left edge of the area of price movement (what we call the “zig-zag” below).

In the example above, the LP has staked a narrow range that sits on the left edge of current trading activity. This strategy is essentially a bet on the base asset outperforming the quote asset. In order to cover this range, the LP has had to stake a lot more quote than base (something like 90–10, instead of the traditional 50–50 LP split).

As the price makes small deviations back and forth on the way up, this LP will have trade flow and will have high capital efficiency. So long as there is trade demand, capital efficiency translates into more fees collected by the LP.

Example of BTC-USD price movement from one week in July 2022, to illustrate the “zig-zag” of buying and selling even as price moves.

This strategy takes advantage of basic market volatility, i.e., the fact that market movements are rarely completely monotonic and instead involve “zig-zags” of buying and selling activity as the price moves. By riding the left edge of the price movement, the LP can collect fees from this zig-zag pattern as arbitrage moves the price left and right even if the overall trend is to the right.

When the price of the base asset moves up enough that the area of price movement moves out of the LP’s range entirely, Maverick AMM automatically moves the liquidity to follow the price–ensuring that capital efficiency remains high.

Even if the price moves significantly to the right and the LP gets completely sold-through to quote, they can then follow the price and move their range to the edge of trading activity again, in order to continue capturing fees from these market zig-zags.

Unlike Uniswap V3, which would require the LP to move their range themselves, Maverick AMM will automatically execute this strategy for them, ensuring they continue to be capital efficient. As the price goes up, the LP will tend to stay in an all quote position, leaving them with minimal IL compared to their original position that was all quote.

This is just one example of how Maverick’s intelligent and flexible AMM design can facilitate profitable strategies for LPs. Users can concentrate liquidity in a variety of different ways, and use Maverick’s automated liquidity shifting mechanism to ensure their capital remains efficient. By moving an LP’s liquidity concentration for them, Maverick AMM provides low-maintenance, capital efficient opportunities for maximizing yield from liquidity provision.

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