An Intro to Window Finance’s Clarity AMM+ Strategy: Algorithmic Risk-Adjusted Return Optimization

A novel, long-biased automated strategy for achieving returns on the efficient frontier for automated market maker liquidity pools

Disclaimer: Nothing in this research post constitutes investment advice nor an invitation to invest. In addition, the information and results herein are preliminary and indicative in nature and shared solely for research purposes. This preliminary research should not be relied upon to make any investment decisions.

TL;DR

• Window Finance introduces the Clarity platform — data infrastructure that builds, backtests, simulates, tracks and optimizes DeFi strategies.
• Clarity was leveraged to produce an automated market maker (AMM) liquidity pool (LP) portfolio optimization strategy that outperforms our benchmark — an index reflecting the average AMM LP market performance — by 2,350 basis points (23.5%) with 22% less volatility over a 6-month period.
• AMM+ and other Clarity platform strategies are being developed to be deployed in 2023.

Intro

Window Finance aims to reduce risk and amplify returns for sophisticated crypto users with Clarity, its advanced data infrastructure and platform. In this article, we discuss how the Clarity platform was used to produce an optimized risk-adjusted return strategy for AMM LP portfolios.

Liquidity Pools within AMM protocols have become very popular in decentralized finance (DeFi). They enable a quick-and-simple way to swap tokens in a secure and decentralized manner while providing an appealing opportunity to generate additional income from fees generated by the trading activity. For the providers of liquidity on AMMs, the function is somewhat similar to a Wall Street trading desk with an ‘inventory’ of assets where fees are generated from buyers and sellers of those assets.

However, providing liquidity to AMMs in an efficient manner can be a challenging endeavor:

• Complex. With thousands of liquidity pool options, constructing a suitable portfolio can be overwhelming.
• Unstable. Yield generated from liquidity pool trading volume as well as incentive fees can change/degrade significantly over time.
• Volatile. The prices of the assets in the liquidity pool can diverge, exposing the liquidity provider to an unbalanced portfolio and potentially significant Impermanent Loss.
• Dynamic. New opportunities can arise which can be difficult to track, while protocols can upgrade to new versions, requiring continuous user monitoring and maintenance.
• Opaque. It can be difficult to understand the underlying holdings, expected return, volatility and historical performance of an LP portfolio.
• Time Consuming. Ongoing approval, trading, deposit and withdrawal transactions, as well as constant underwriting in an effort to maintain an optimal portfolio can be a significant burden.

The Clarity platform by Window Finance has been engineered to directly tackle these issues while providing a simple-to-use, low-correlated strategy for sophisticated users with strong risk-adjusted returns. The initial product is Clarity AMM+, an algorithm designed to provide an efficient entry into the AMM LP space.

Results

Our results during the backtest, depicted in Figure 1, show that a broad AMM LP strategy — represented by the Clarity AMM Index discussed further below — outperformed the digital asset market represented by the S&P Cryptocurrency Broad Digital Market Index (S&P CBDM).

However, optimizing the LP portfolio using the Clarity AMM+ strategy results in an outperformance of 2,350 basis points (23.50%) over the Clarity AMM Index, leading to a total outperformance of 4163 basis points (41.63%) over the S&P CBDM.

Figure 1. Excess returns of the Clarity AMM Index and Clarity AMM+ over the S&P CBDM.

Although some outperformance is expected due to the yield generation of the liquidity pools, these results can be materially impacted by impermanent loss, asset composition and overall long exposure (e.g., percent of total stablecoin holdings, etc.). Nevertheless, initial results could warrant closer inspection from those looking to provide liquidity to the sector in a broad manner. Even more compelling, however, is that the Clarity AMM+ strategy significantly and consistently outperformed the Clarity AMM Index, as shown in Table 1.

Table 1. Backtested statistics of Clarity AMM+ and the Clarity AMM Index versus the S&P CBDM benchmark.

The results may indicate that absent material future changes in the AMM market, an algorithm may be employed to optimize the total return performance of liquidity pools over the market, thus potentially generating additional return with a similar level of risk. It is important to note that given the simplicity of the AMM+ strategy for the user, it could be a significant benefit to have an option that would merely track the performance of the overall AMM LP market. In this vein, Window Finance’s initial research has indicated that a randomly selected or low-rebalancing AMM liquidity pool portfolio generally underperforms the Clarity AMM Index.

Note on the table above that while Clarity AMM+ outperformed when considering total return, it also experienced significantly lower volatility. Furthermore, although the AMM+ strategy generated negative returns of 11.7% for the period, this was during a severe bear market where the S&P Broad Crypto Index weighted to the highest quality digital assets declined 53.3%.

AMM+, Powered by the Clarity Platform

Window Finance believes that many existing liquidity providers could achieve additional returns without increasing the overall risk profile of their investments. Based on this, the Clarity AMM+ strategy was designed to select a diversified set of liquidity pools with a strong expected risk-adjusted return profile. It will then periodically rebalance this portfolio on behalf of the user, aiming to maintain an optimal position across the universe of AMMs.

The three critical factors to consider when attempting to optimize an AMM portfolio are: a) price change of underlying tokens, b) impermanent loss, and c) fee income/yield. The problem then becomes one of optimizing a portfolio for these factors on a forward-looking basis.

Forecasting the long-term price movement of digital assets with any degree of confidence is notoriously difficult. Thus, the Clarity AMM+ algorithm does not attempt to forecast token prices — instead, it predicts the expected impermanent loss of liquidity pools over some future time horizon t. It does so by learning from the relative variations in historical token prices.

Clarity has been tracking thousands of AMM LP positions since the beginning of the year. Based on the coverage of AMM LPs from February to September, 54% of LP positions produced impermanent loss greater than 5%, with a median 4% of impermanent loss. This is shown in the histogram of historical impermanent loss in Figure 1. Thus, impermanent loss is a factor to consider when taking an LP position.

Figure 2. Histogram of historical impermanent loss for the various pools in our dataset.

In isolation, a user could look to minimize the impact of expected impermanent loss alone. However, the Clarity AMM+ strategy only considers the impact of expected impermanent loss of a liquidity pool in the context of that pool’s expected yield performance. In other words, high impermanent loss can be tolerated as long as liquidity providers are appropriately compensated for the risk with sufficient yield income.

Forecasting expected yield also looks at historical data for each individual LP to forecast the expected future yield within an expected range using probabilistic models tracking total value locked, trading volume and incentive fees. This data is continuously monitored by Window Finance's Clarity platform.

The algorithm will then select an optimized LP portfolio using the factors discussed. At regular time intervals, Clarity AMM+ strategy will optimize the LP portfolio for the aggregate expected risk-adjusted return comparing existing holdings to LP opportunities in the market.

Proprietary tools in development on the Clarity platform are used to look through to the underlying assets of the portfolio to generate monitoring reports and provide insight and transparency into overall portfolio asset diversification, net long market exposure, as well as historical and expected portfolio yield, impermanent loss, and total return.

Backtesting

Settings

• We evaluate the Clarity AMM+ strategy from a universe of 147 liquidity pools from AMMs collected on the Clarity platform containing data over a 6-month time span from March 1st through August 31st, 2022.
• These 147 pools were selected using investment criteria to generally cater to sophisticated users, being drawn from leading AMM protocols, such as Uniswap, SushiSwap, QuickSwap, Balancer and Curve on the Ethereum, Polygon, Arbitrum, and Avalanche blockchains. In addition, LPs that contain tokens with a very low market cap as well as LPs with low TVL are filtered out.
• The main benchmark is defined as an average of all of the 147 liquidity pools, to represent the overall market of potentially ‘higher quality’ liquidity pools, which we denominate as the Clarity AMM Index.
• The Clarity AMM+ strategy is set to select the top 5 liquidity pools from the 147 initial LP universe and, initially (at time t₀), invests an equal amount of capital into each. Then, at every time interval t, the model and the baselines re-evaluate their current set of pools and select a new set to reinvest for another t time, based on their selection criteria. After all of the periods in our dataset are tested, their performance is compared.

Limitations and Caveats

• For simplicity, gas and trading costs were ignored for this research case. Fees are not expected to significantly alter results as transactions are aggregated, and trading costs can be managed depending on the execution strategy.
• The timeline is based on the current data set which limits results to the March — August 2022 range. AMMs themselves are a fairly new innovation with limited historical data. Nevertheless, the Clarity AMM+ strategy was tested across a hundred random time ranges (not described in this article) within this time period with similar results, thus indicating some potential benefit to the AMM+ strategy in this environment.
• Impact of price movements drive the majority of the results whereas the AMM+ strategy is solving primarily for yield and price divergence. However, results on preliminary evaluations removing the effects of price impacts reveal that AMM+ still outperforms our baselines and benchmarks.
• It is difficult to define a suitable benchmark as the AMM space is still fairly new and underdeveloped. In addition, the benchmark index can have a more bullish market view exposure than the AMM+ portfolio. Nevertheless, in an attempt to compare among similar benchmarks with a similar market exposure, the AMM+ portfolio maintains significant outperformance.

Benchmarks: The Clarity AMM Index and Digital Asset Market

The main benchmark utilized to compare the performance of Clarity AMM+ is the Clarity AMM Index, discussed above, which represents the overall market performance of ‘higher quality’ AMM liquidity pools.

For the results of the simulation, the excess returns of the Clarity AMM+ strategy and the Clarity AMM Index are compared to the broad digital asset market as expressed by the S&P Cryptocurrency Broad Digital Market Index, which is market weighted to the top 250 cryptocurrencies by market cap. Thus, it is largely influenced by the performance of Bitcoin and Ethereum as those make up the majority of the market cap.

Portfolio Construction

The Clarity AMM+ strategy automatically generates a portfolio based on expected performance of the underlying liquidity pools which is then rebalanced at every period, t. Figures 3 and 4 show a sample of auto-generated portfolios at different time periods.

Figure 3. Token exposure at the initial allocation (t₀).

Figure 4. Token exposure at the 11ᵗʰ rebalancing period.

The Clarity AMM+ Strategy selected a market exposure of ~40–70% with ~3–10 digital assets, weighted largely to USD, Bitcoin and Ethereum pools. In a future deployment, Window Finance would look to provide tools that could optimize for additional asset and protocol diversification as well as varying the overall net long exposures to the digital asset space. On the extreme end, Window Finance has explored 100% market neutral portfolios utilizing stablecoin and hedging structures to generate minimal volatility.

Future Development

The Clarity AMM+ strategy was able to materially outperform the selected benchmarks during a bear market period. AMM+ and other Clarity developed strategies are planned to be leveraged by an investment fund focused on AMM LPs as well as other digital asset strategies that is expected to launch in early 2023.

Based on this research, Window Finance plans to offer simple and transparent tools generated on the Clarity platform to users globally. Window Finance aims to expand the reach of DeFi while ensuring access to all those that wish to participate in the next stage of global finance.

To sign in for early access to this AMM+ and the Clarity investment platform, contact us.