Is it worth continuously rebalancing DeFi positions and path?
In the modern digital asset panorama, Decentralized Finance (DeFi) has emerged as a transformative ecosystem, enabling novel ways of economic engagement through autonomous, smart contracts. In the growing universe of DeFi protocols, yield farming has emerged as a popular and undeniably rewarding alternative for crypto asset buyers. Yield farming is the strategic deployment of crypto assets across different liquidity pools to maximize return on investment, an endeavor with a number of complexities and challenges that requires efficient and data-driven management methods.
The aim of this analysis is to provide insight into the efficiency and sustainability of different DeFi yield farming portfolios, with a focus on different rebalancing intervals. By analyzing portfolio efficiency over a 12-month period, we examine the impression of weekly, bi-weekly and monthly rebalancing methods compared to a fixed portfolio with no rebalancing.
Our research considers both the potential for prime returns and the dangers associated with these methods. Additionally, we consider the valuation of Arbitrum Gasoline Fees, which are an integral part of transactions and interactions with DeFi protocols and can significantly impact overall portfolio efficiency. However, our focus will not be on the phenomenon of temporary loss, a unique threat related to liquidity provision in DeFi that, while essential to the broader conversation, is beyond the scope of this particular analysis.
Through a scientific backtesting plan, we will develop a comprehensive understanding of how different rebalancing periods can affect the overall efficiency of a DeFi yield farming portfolio. The results of this analysis aim to educate crypto investors, financial advisors, and DeFi fans on their portfolio management methods, ultimately driving better efficiency and profitability in the fast-moving and ever-evolving DeFi space.
This analysis is based on the expectation that yield farming portfolios with a higher frequency of rebalancing could undoubtedly perform better than those that can be built or sometimes rebalanced. The logic behind this speculation is that by adjusting portfolio allocations more regularly, an investor can better navigate the rapidly changing DeFi landscape and benefit from the emerging high-yield alternatives.
However, it is often assumed that the benefit of frequent rebalancing could diminish or even reverse for portfolios with smaller funding levels. This assumption is based on the financial tenet that rising prices are biased toward variable positive factors. In the context of DeFi yield farming, each rebalancing operation incurs arbitrum gasoline fees as the rising price and yield from different liquidity pools represent the variable success. For smaller portfolios, over time these rising prices could outweigh the benefits of capturing short-term, high-yield alternatives, thereby undermining overall efficiency.
To test these hypotheses empirically, we use a backtesting methodology that leverages historical data from the top 50 TVL pools on Arbitrum, with the exception of Uniswap v3 pools due to their important short-term loss share. This knowledge is to be used to calculate the optimal portfolio for each week over a 12-month period, with different rebalancing frequencies: weekly, bi-weekly, monthly and uniform. The following comparison of these results will provide empirical evidence that supports or refutes our preliminary expectations and will contribute to a better understanding of the dynamics involved in managing the DeFi yield farming portfolio.
In performing this analysis, several assumptions must be used to construct the assessment and constrain the scope to a manageable range. The assumptions guiding our research are as follows:
Funding dimension: First we will assume a funding dimension of $10,000, later we will determine the impact of lower funding dimension and look at the impact of funding dimension on profitability.
Portfolio Optimization: We will use the Trendy Portfolio Principle (MPT) as our policy for portfolio optimization. Therefore, we will use mean-variance optimization to compose our portfolios.
Transaction prices: The price of Arbitrum Gasoline Fees for each settlement event is calculated using self-testing and then averaged in a precautionary overestimate using data from Dune Analytics. This research assumes that four separate transactions are required for each asset in the portfolio – two approvals (one for converting the underlying token and one for providing liquidity) and two exact transactions (one for converting assets and one for providing liquidity). The price of each permit is assumed to be US$0.225 while the price of each transaction is assumed to be US$0.4. This results in a total price of $1.25 per asset per rebalancing interval.
Portfolio constraint: We will limit the number of assets in the portfolio to four. This caveat stems from the notion that rebalancing methods tend to be short term and too many assets can introduce unnecessary complexity and transaction costs, which undoubtedly reduces the effectiveness of periodic rebalancing. In addition, each pool has a maximum weight of 30%.
Ephemeral Loss: In this analysis, we will not consider temporary losses. Although transient losses in certain DeFi protocols are a key factor in liquidity delivery profitability, they are not considered in this particular study in order to isolate the impact of rebalancing frequency and fuel costs on portfolio efficiency.
By leveraging these assumptions, we aim to provide a structured framework within which to conduct our assessment and gain important insights into the impact of various rebalancing methods on DeFi yield farming portfolios.
The next strategy was used in our analysis to carefully study and examine the efficiency of DeFi yield farming portfolios under different rebalancing methods:
Record: We used a dataset that includes the 50 highest TVL pools on Arbitrum, excluding Uniswap v3 due to their important transient loss share. Specifically, our data covers the period from April 25, 2022 to April 25, 2023. Each included pool is from a log with a threat score of 6/10 or higher, based on our proprietary threat analysis methodology. It is important to note that not all pools have return data going back to the beginning of the year; Thus, the set of assets available for portfolio optimization expands as the backtest progresses. This case does not represent a limitation, but rather realistically reflects the dynamic nature of the DeFi space, where new funding opportunities are constantly emerging. See the bibliography for a full list of included pools.
Portfolio structure: In order to compile the portfolios, we have segmented the annual period into individual weeks. For each week, we used mean-variance optimization to compose the portfolio with the highest return, setting the number of assets in the portfolio to 4. This constraint was used for consistency, simplicity, and comparability during the various realignment intervals.
Yield calculation: Returns were calculated at the end of each week based on the previous week’s optimal portfolio, as future returns are of course unpredictable. As an example of the weekly rebalancing portfolio, the week 10 portfolio tracks the returns of these assets at week 11. Essentially, at time T, we tracked the portfolio’s returns from time T-1. When there is a realignment, the positive factors amplify; If, on the other hand, there is no realignment, positive factors are simply added together without compounding.
Gasoline charges and compounding: Gasoline charges were deducted at the beginning of each balancing period and included in compounding calculations. This strategy allows for a hands-on analysis of online returns to be expected after considering transaction prices associated with the portfolio rebalancing.
This system provides a robust and meaningful framework to gauge the impact of various rebalancing frequencies and transaction prices on the performance of DeFi yield farming portfolios over a 12-month period.
Month-to-month rebalanced vs. static portfolio.
Final stability of the assembled portfolio after 52 weeks: $12522.65
Portfolio changing month to month, final stability after 52 weeks: $13527.94
Achieved assembled portfolio at 52 weeks: $2522.65 (25.23%)
Monthly Rebalanced Portfolio Result After 52 Weeks: $3527.94 (35.28%)
Month by month / mounted = +28.5%
Monthly rebalanced vs. biweekly rebalanced.
Portfolio changing month to month, final stability after 52 weeks: $13527.94
Bi-weekly changing portfolio, final stability after 52 weeks: $13657.88
Monthly Rolling Portfolio Result After 52 Weeks: $3527.94 (35.28%)
Bi-Weekly Changing Portfolio Result After 52 Weeks: $3657.88 (36.58%)
Biweekly / month by month = +3.55%
Weekly Rebalancing vs. Biweekly Rebalancing
Weekly changing portfolio, final stability after 52 weeks: $13435.20
Bi-weekly changing portfolio, final stability after 52 weeks: $13657.88
Weekly Changing Portfolio Result After 52 Weeks: $3435.20 (34.35%)
Bi-Weekly Changing Portfolio Result After 52 Weeks: $3657.88 (36.58%)
Weekly/biweekly = -6.09%
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