Decentralized Liquidity Strategies
There are multiple ways to monetize on the growth in decentralized finance networks via trading strategies. Underlying strategies tend to fall into two wider risk categories; 1) guaranteeing on-chain liquidity on applications that rely on external liquidity provision, and 2) extracting fees from applications with guaranteed on-chain liquidity.
Market participants have a few ways to earn a higher rate of return than the rate of ‘risk free’ interest (e.g. lending rates on Compound Protocol, or eventually, the DAI savings rate, or ‘DSR’). Outside of speculative trading, this includes acting as keepers. Keepers are independent operators that exploit opportunities for profit which helps to maintain market equilibrium in various systems. These opportunities exist in applications such as MakerDAO, dYdX, SET Protocol, DDEX, and Compound Protocol. In these applications, operators can perform some or all of the following functions to earn a financial gain by underwriting and providing liquidity:
- Liquidating unsafe collateralized debt positions (CDPs) in MakerDAO by taking on all debt in exchange for PETH collateral plus a 3% liquidation fee via the bite function.
- Liquidating and taking over undercollateralized (<115%) positions on dYdX and receiving a 5% liquidation fee via the liquidate() function.
- Liquidating and taking over undercollateralized (<150%) positions on Compound Protocol and receiving a 5% liquidation fee via the liquidateBorrow() function.
- Liquidating undercollateralized (<110%) positions on DDEX via the liquidateAccount() function (which initiates a dutch auction sale of the collateral) and receiving a 5% fee on any surplus above the maintenance margin.
- Competing for profit by bidding during rebalance auctions in SET Protocol as well as issuing and redeeming undervalued / overvalued Sets.
Looking at available liquidation and rebalance data across the above applications on an annualized basis, we can estimate the total annual value accrued to keepers (assuming a frictionless market with no hedging slippage).
With SET protocol, 0.70% is the average auction slippage on token Set rebalances, with auction slippage assumed to be = market maker profit (rebalance bid/offer vs market offer/bid). In total, we can estimate that in the current state of the market the annual economic gains for keepers are between $3–4mm. We have ignored value accrued to PETH holders in Maker, as liquidation penalties in multi-collateral DAI (MCD) will ultimately accrue to MKR holders. We have also excluded Maker arbitrage-keepers which exploit mispricings between DAI/ETH, DAI/WETH and PETH/WETH pairs due to difficulties of estimating arbitrage profits.
Theoretically, exposure to keeper strategies (excluding Set auctions) could synthetically be replicated through a continuous strip of knock-in barrier options. For a given single period, we can describe the pay-off structure by defining the maximum and minimum of the underlying collateral price process:
𝑆 = 𝑆𝑡 , 0 < 𝑡 < 𝑇 as 𝑚𝑆 = inf 𝑆𝑡 ; 0 < 𝑡 < 𝑇 and 𝑀𝑆 = sup 𝑆𝑡 ; 0 < 𝑡 < 𝑇 .
For a knock-in, the barrier L (liquidation threshold) is set below the spot price (collateralization ratio) at inception, L < 𝑆0 . The option is worthless unless the barrier L is reached some time during the life of the trade, in which case it becomes an in-the-money vanilla call option.
Φ𝐷𝐼 𝑆𝑇, 𝑚𝑆 = 𝑆𝑇 − 𝐾 +𝟏(𝑚𝑆 ≤ L)
With TokenSets, auction participants’ long run expected payoff function is highly dependent on the underlying correlations between strategies. But broadly, we can categorize exposure to keeper strategies as long volatility strategies.
Uniswap Automated Market Making
Uniswap’s automated liquidity is set using a constant product formula; exchange rates for pairs of ERC20 tokens are calculated based on an x * y = k function where x and y are quantities in a given pool (e.g. ETH-DAI), and k is the product. The exchange rate of a token always falls on a particular point on the bonding curve such that the product of the liquidity pool remains the same after a trade has happened. For example; x=ETH, y=DAI, x*y=k, y/x=p. Takers pay a 0.30% fee during each trade, which gets added back to the liquidity pool.
Liquidity providers (LPs) supply pairs of tokens and in return earn tokens representing their share in the pool (LP shares). Due to constant product pricing, a price change in any direction (i.e., any trading activity) impacts LPs negatively. This means the payoff for a Uniswap LP share is a function of the difference in relative value of the assets from when liquidity is added to when it is removed, and the accumulated liquidity pool fees. In other words, optimal profit for LPs occurs when the final price is equal to that at liquidity provisioning.
Assuming constant pool liquidity (no additions or withdrawals), prices follow a stochastic process and that accrued fees accumulate to LPs, heuristically we can think of the payoff function for Uniswap LPs as short ATM perpetual straddles with continuously re-adjusting strikes dependent on volumes per unit of liquidity.
More practically, this payoff can be approximated through a continuous strip of short-expiry same-strike straddles. I.e., selling of ATM straddles at t+0 (upon pool entry), with recursive selling of the same strikes at t+1, t+2…t+n at each time-step during which trades can happen (e.g. on a per-block basis). This payoff is a function of the sum of impermanent losses from non-zero price changes in the pair and premiums accrued (pool fees).
Assessing various payoffs in DeFi using derivatives replication means that for a given set of parameters, market participants will be able to imply pair volatilities from liquidity pools directly, or required rates of returns for keeper strategies, among other things (at least in the abstract). It also makes for interesting thought experiments in thinking about portfolio construction and optimization natively.
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