Clip Finance ALPHA
How we generate ALPHA
Last updated
How we generate ALPHA
Last updated
In DeFi, "Alpha" refers to the active management of strategies used to outperform the market or generate higher returns.
Concentrated liquidity involves strategically positioning liquidity within certain price ranges on automated market makers (AMMs) to maximize fee earnings and minimize impermanent loss. This approach requires deep market analysis and understanding to anticipate price movements and adjust positions accordingly. Successfully managing concentrated liquidity can provide significant Alpha by optimizing the balance between risk and reward in DeFi liquidity provision.
Providing liquidity in concentrated liquidity pools with tight ranges can offer significant leverage compared to wider ranges or providing unconcentrated liquidity. The fees collected for providing liquidity depend on price movement and the chosen range. Thus, it becomes important to choose optimal ranges and consider possible risks of price movements to the liquidity position value or as an impermanent loss, by managing the delta risk and considering gamma exposure. Another input to choosing optimal ranges is the cost in terms of gas fees associated with adjusting the liquidity position range in both absolute terms and as a proportion to the employed capital as also shown by Li et al. (2023).
Our approach to adjusting ranges depends on various quantitative measures about the market state and price volatility and movement which are used as input to the algorithm that suggests a recommended range for a liquidity position. We employ active liquidity management strategy as the volatility of the crypto market tends to be high so passive liquidity management would require a wide range of selections which in turn means less capital efficiency and in certain cases might even result in harder-to-manage gamma risk to hedge the underlying liquidity position value risk.
Our approach has the following components:
Choosing or creating attractive liquidity pools that enable to collection fees in attractive proportions relative to the amount of employed capital.
Using volatility prediction models, including autoregressive models in combination with realized volatility modeling and machine learning models to make both short- and long-term predictions of market volatility.
Feeding volatility predictions as well as current market volatility measures to a custom algorithm to estimate optimal range width as well as range adjustment timing in relation to the volatility pool characteristics and the amount of employed capital.
Using market sentiment measures in combination with machine learning models (incl. a custom implementation of an LSTM neural net model) and traditional technical analysis market indicators to predict and react to the changes of market sentiment and price trend or channel changes to estimate necessary hedging measures.
Using outputs from volatility and market price estimation models, employ hedging strategies to hedge against adverse price movements, dependent on the ranges and open risks of opened liquidity-providing positions.
The following outlines the basic principles and models used in various steps. The exact algorithms and models remain proprietary. Additionally, we run a general market regime estimation model which classifies the market as:
Trending positive
Trending negative
Trading in a range
And based on volatility:
High volatility
Medium volatility
Low volatility
The general market regime estimations are based on longer-term trends and outlooks, whereas range and liquidity management models and algorithms consider more short-term fluctuations as liquidity management is performed actively which can mean multiple adjustments per day in certain volatility situations but can, in contrast, result in rare weekly adjustments during low-volatility markets.
The choice of liquidity pools for providing liquidity depends on the fee accrual rates but is also dependent on the characteristics of the traded assets. For low-volatility assets (like stablecoins) the price movements are usually small which means that optimal ranges should also be small to provide higher capital efficiency. Small price movements also mean that trading in such pools may not be that profitable for arbitragers and thus the current characteristics of the pool (e.g. total value locked (TVL) in the pool and fee accumulation metrics) play a more important role. For volatile assets (e.g. WETH/USDC pair) it is more likely that arbitragers will trade in the pool if the spot price changes. In the first case, the fees earned will be lower, and thus, price movement risk is smaller; in the second case, the opposite is true.
Our volatility prediction models are inspired by the results of Bergsli et al. (2022) and Dudek et al. (2024) who have shown that various GARCH-type models can successfully be used for long-term volatility estimation in cryptocurrency markets, whereas realized volatility models and machine learning models help to predict volatility more accurately in the short run.
The range selection algorithm is a function of volatility estimation to determine a suitable width of the range, at the same time, considering the cost of rebalancing as a function of gas fees, pool fee tier, and allocated capital amount. In addition to volatility estimation, the range selection algorithm considers the state of the market regime, as for example Li et al. (2023) show that range management in trending markets is more challenging, however, our algorithm can suggest a more frequent adjustment of ranges in such an environment, if the cost function does not restrict such a position rebalancing.
Depending on the market sentiment (from technical indicators to more elaborate news sentiment and order flow-based indicators) and prediction of market movement, LPs may prefer different risk profiles. We offer a choice of pools without or with an included hedging layer. Thus, users can decide based on their expected (or our model suggested) market direction the degree or direction of hedging liquidity position loss or impermanent loss. Such choice is warranted as all hedging strategies come with a certain cost and our default hedging approach may not correspond to the risk appetite of every user.
