Introducing Alpha Vaults — an LP strategy for Uniswap V3

Published in
5 min readMay 3, 2021


Charm is excited to announce its latest protocol — Alpha Vaults!

Alpha Vaults automatically manage liquidity on Uniswap V3 for you. They concentrate liquidity to earn higher yields and periodically rebalance to reduce impermanent loss.

Key Features

  1. Alpha Vaults can generate higher yields using the new concentrated liquidity feature in V3.
  2. They rebalance periodically so that its inventory remains balanced. You don’t have to worry about managing your position — you can just leave your deposit in the vault.
  3. This rebalance is executed passively using range orders to avoid swap fees and price impact.
  4. Trading fees are automatically collected from the Uniswap pool for you.
  5. Vault shares are represented as ERC-20 tokens so are composable and fungible.

Passive rebalancing

The unique feature of Alpha Vaults is that it rebalances passively using range orders, which significantly improves its performance.

Rebalancing is necessary when providing concentrated liquidity on V3. For example, if you initially deposit 50% ETH and 50% USDC but the price moves a lot one way, you might be left with 80% ETH and 20% USDC. Then you’d need to somehow rebalance back to 50/50 so that you don’t run out of inventory on either side and can continue providing two-sided liquidity.

One way to rebalance would be to aggressively rebalance by swapping ETH → USDC on Uniswap to get back to 50/50, but this incurs the 0.3% trading fee and price impact.

Instead, Alpha Vaults only passively rebalances, placing a narrow range order on one side of the current price. This avoids the need to swap tokens and incur the fee and price impact. Backtests have been conducted to show passive rebalancing can outperform aggressive rebalancing.

How it works

A strategy is deployed for a certain pool on Uniswap, such as ETH/USDC. It has two main parameters:

  • B (base threshold)
  • R (rebalance threshold)

The strategy always maintains two active range orders:

  • Base order centered around current price X, in the range [X-B, X+B]. If B is lower, it will earn a higher yield from trading fees.
  • Rebalancing order just above or below the current price. It will be in the range [X-R, X], or [X, X+R], depending on which token it holds more of after the base order was placed. This order helps the strategy rebalance and get closer to 50/50 to reduce inventory risk.

Every 24 hours, the rebalance() method is called by a keeper. This shifts the two orders according to the updated price and token balances. If the strategy performs well, this time period can be decreased.

Note that the rebalance order doesn’t guarantee the ratio returns to 50/50. If it holds more ETH than USDC, it makes it more likely it’ll sell more ETH than buying more ETH. If the price keeps increasing, the strategy will get more and more unbalanced, but on average it’s more likely to return to 50/50 since it’s offering to sell more tokens than it’s offering to buy.


For example, let’s say the current price is 150 ticks, B = 50 and R = 20, and the strategy holds 1 ETH and 160 USDC. It would then place a base order between [100, 200] ticks using 1 ETH and 150 USDC. The base order is symmetric around the current price so it deposits equal values of ETH and USDC.

It then has 10 USDC left over, which is used for a rebalancing order between [130, 150] ticks in order to try to buy more ETH with its USDC to get closer to a 50/50 ratio.

If the price goes up to 180, after rebalancing, the base order will be shifted up to [130, 230]. Let’s say the strategy now holds 1.2 ETH and 90 USDC. The strategy would then use 0.5 ETH and 90 USDC for its base order. It has 0.7 ETH left over, so it uses it for a rebalancing order between [180, 200].


We conducted backtests over a 11 month period using Uniswap V2 price data.

If theoretically there was the same volume and liquidity in V3 and all other LPs were providing liquidity over the entire range, Alpha Vaults would vastly outperform other LPs. This is because our strategy could automatically deploy liquidity to the narrow price range where most of the trading takes place.

This isn’t realistic though as in V3, there could be other LPs also using narrow range orders and smart strategies, so the fees/liquidity ratio might be lower. Conservatively assuming the fees/liquidity ratio is half of V2, Alpha Vaults still performs extremely well, outperforming a V2-style LP over the whole range, and someone who’s using an aggressive rebalancing strategy.

Backtest results comparing Alpha Vaults with two other strategies

These results hold across other pairs too. We observed the strategy tends to do much better for markets where the fees/liquidity ratio is high, so it would make sense to initially deploy strategies for these high earning pools only.

The passive and rebalance thresholds can later be adjusted by a strategist or by governance depending on market conditions or competition. For example if the market is less volatile, the thresholds should be reduced.

These backtests make a lot of assumptions about fees earned and should be regarded as just a sanity check rather than estimate of performance. Once there’s some data from V3, more accurate simulations can be conducted.


Alpha Vaults make it extremely easy for users to outperform the market. It achieves this by using concentrated liquidity on Uniswap V3 to earn higher yields and automatically rebalances its inventory to reduce impermanent loss.

Alpha Vaults will go live on mainnet soon. Follow Charm on twitter to stay updated.

Feel free to join our community on Discord if you have any questions, ideas or suggestions!

Disclaimer: The content of this post is provided for informational purposes only. Nothing herein constitutes investment, legal, or tax advice or recommendations. Nothing on this site should not be relied upon as a basis for making an investment decision. It should not be assumed that any investment in the asset class described herein will be profitable and there can be no assurance that future events and market factors would lead to results similar to any historical results described.