Generating LP Profits — In-Depth Backtesting (Part II)
In Part I, 2x Your LP Value, we used Y2K as a case study and used historical pricing data to test strategy performance. The focus of this report is to examine Sector’s Automated Liquidity Management (“ALM”) strategy performance in different market scenarios.
DeFi protocols face the challenge of holding idle assets with limited options for effective management. This includes native DAO tokens, as well as L1 and L2 assets. For instance, over $100 million in initial ARB was airdropped to DAOs, and we discovered that a substantial portion of these tokens were sitting in treasuries, not being utilized in a capital-efficient manner.
Sector Finance is addressing this issue by creating a strategy that allows DeFi protocols to confidently deploy their Protocol Owned Liquidity (“POL”). This strategy optimizes liquidity health, LP value, capital efficiency, and protocol stability.
In part II of our report series, we continue our backtesting and research but now use randomly generated and distinct market scenarios to further stress test the ALM strategy performance. We also published the results of the backtest in our Backtest Dashboard.
Our testing encompasses a range of market scenarios, including upwards, downwards, and sideways market conditions at various volatility levels. The testing categories and scenario analyses were selected to represent the diverse and unpredictable nature of the DeFi markets.
We found deploying protocol-owned liquidity in Sector’s Automated Liquidity Management strategy outperformed Uniswap V2 LP by 58% and 48% in the Downwards and Upwards Reversion market scenarios, respectively, while performing in-line with Uni V2 LP in all others.
ALM Strategy Overview
Our ALM strategies are built on top of concentrated-liquidity DEXes such as Uniswap V3. The strategies are designed to optimize concentrated liquidity ranges and rebalance points with the goal of increasing LP Value and POL over time.
While other automated Uni V3 strategies focus on capital efficiency, the Sector ALM optimizes for LP value. Compared to capital efficiency, LP value provides a quantifiable measure of liquidity health, making it easier to assess the protocol and the token robustness. Higher POL levels allow protocols to respond more effectively to market dynamics and ensure a higher degree of stability. Please review the “Demystifying Capital Efficiency” section of our prior report for more details surrounding LP value and capital efficiency: Part I Report.
Our ALM strategy comprises a set of key components that dictate how it operates and adapts over time. These components include but are not limited to:
- Liquidity is allocated to 3 narrow ranges on Uniswap V3. Range widths and LP allocation are based on backtest simulations and can vary based on market conditions.
- Sector keeper bots monitor the strategy and execute rebalances once price crosses a predefined rebalance threshold.
ALM Strategy Development
A central component of our strategy development involves evaluating tradeoffs. We carefully consider the balance between optimizing LP Value and the rebalance weight of our liquidity ranges. This tradeoff analysis is for making informed decisions about where and when to allocate assets.
In the graphic below, the rebalance weight metric scales between 0–1 and represents balancing from the edge of the main range and rebalancing on the secondary range. For the Sector ALM strategy, the default rebalancing weight is 0.2.
Furthermore, we examine the tradeoff between LP Value and capital efficiency to ensure that capital is utilized optimally while maintaining liquidity.
Testing Methodology
In conjunction with the report, we have published our backtests in our Backtest Dashboard.
The assessment of the ALM strategy performance is conducted through our testing framework that aligns with the methodology introduced in Part I of the report series.
The ALM strategy is rigorously tested under each of these market scenarios to evaluate its adaptability and resilience. Results are documented and analyzed to provide insights into how the strategy performs and responds to changing market dynamics.
We generated 15 distinct market scenarios using geometric brownian motion. Given the pricing data follows generated and distinct paths, the backtests are representative of any DAO token paired with a distinct pair like ARB, USDC, or ETH.
We categorize the scenarios into three main categories, each of which we used in our testing framework to compare the performance of our ALM strategy to that of Uniswap V2:
Performance Summary
While comparing the performance of the ALM strategy with Uniswap V2, we observed that there was no material difference in the final token prices. However, our ALM strategy consistently led to increases in LP levels in the tested scenarios.
Testing Category: Downwards Reversion
In this category, we examine the impact of the ALM strategy when prices undergo significant increases followed by reversion downwards.
We found that Sector’s ALM strategy led to an annualized 58% average improvement in LP levels across the backtest period. We tested three scenarios where the price increased by 25x, 10x, or 5x and subsequently reverted to lower levels.
The ALM strategy accumulates profits from price movements. As price oscillates and when the DAO token price increases, the ALM sells the position. When reversion occurs, the ALM strategy acquires the DAO token as prices declines. In short, the strategy acquires the DAO token for less than what is sold (and vice versa). The result of this is a 58% average increase in LP levels for protocol-owned liquidity.
25x
10x
5x
Testing Category: Upwards Reversion
We found that Sector’s ALM strategy led to an annualized 48% improvement in LP levels across the backtest period. We tested three scenarios where the price declined by 80%, 90%, and 96% before reverting. These scenarios enable us to gauge how the strategy handles market downturns and recovery phases.
0.2x
0.1x
0.04x
Testing Category: Randomly Generated Price Paths
A key variable we toggle is market volatility. We explore how the ALM strategy reacts to these spikes and assess its ability to maintain efficient liquidity provisioning and capital utilization during such events.
We examined nine scenarios encompassing various market conditions, including upwards, downwards, and sideways markets at different volatility levels. In these randomly generated scenarios, we stress test extreme fluctuations in price (upwards of 100x movements) and find that even in these cases, the strategy is comparable and does not underperform Uni V2. The graphic below demonstrates one such scenario. Please find the remaining scenarios in the Backtest Dashboard.
Risks
While our backtesting and research have shown significant improvements in capital efficiency and price stability through our ALM strategy on concentrated liquidity, there are certain risks that need to be considered:
Market Volatility: Despite the ALM strategy’s efforts to counter market volatility, unforeseen and extreme market fluctuations could still impact the token price and liquidity, leading to potential impermanent loss for liquidity providers.
Regulatory Risk: The decentralized finance space is subject to evolving regulatory landscapes in various jurisdictions. Changes in regulations may affect the viability and implementation of ALM strategies.
Smart Contract Vulnerabilities: As with any smart contract-based protocol, there is always a risk of potential bugs, exploits, or vulnerabilities that could expose the protocol to security breaches and financial losses.
Automation Risk: The reliance on automated processes and keepers presents the risk of failure due to high gas fees, network congestion, or other technical issues.
Conclusion
Sector Finance’s Automated Liquidity Management strategy, designed for concentrated liquidity pools, provides differentiated performance in POL management. Notably, it outperformed Uniswap V2 LP by 58% and 48% in the Downwards and Upwards Reversion scenarios. In the other scenarios, it consistently maintained a competitive edge. This success can be attributed to key components like the rebalancing mechanisms, which strike a balance between optimizing LP value and capital efficiency.
Our ALM strategy’s focus on LP value optimization offers several advantages, including a quantifiable measure of liquidity health and increased POL levels, contributing to protocol stability.
Sector Finance’s ALM strategy represents a significant advancement in concentrated liquidity pool optimization. Its performance and adaptability make it a valuable asset in navigating the dynamic DeFi landscape, delivering liquidity health, capital efficiency, and protocol stability.
