Introduction

Decentralised finance (DeFi) has rapidly become a foundational element of the cryptocurrency ecosystem. Among its many components, decentralised exchanges (DEXs) play a critical role by enabling trustless, non-custodial trading of digital assets. In January 2024, DEX trading volume reached an all-time high of $562.8 billion, nearly three times the trading volume recorded on Switzerland’s SIX Exchange, which stood at $118.8 billion, according to DeFiLlama.

Among the various DEXs, Uniswap remains the dominant player, typically capturing around 40% of total DEX volume, as evidenced by data from Dune Analytics. This dominance can be attributed to several factors, including its security, deep liquidity, and the introduction of the concentrated liquidity automated market maker (CLAMM) model with Uniswap v3. However, while CLAMM improved capital efficiency, it also increased the complexity of liquidity provision, requiring liquidity providers (LPs) to actively manage their positions to maximise profitability.

This complexity has given rise to innovations aimed at improving liquidity management, with Bunni.xyz emerging as one of the most promising solutions. Built on Uniswap v4, Bunni leverages the protocol’s new ‘hooks’ functionality to introduce novel LP management tools that simplify operations and enhance profitability.

What is Bunni?

Bunni.xyz is an advanced liquidity management platform that extends the capabilities of Uniswap v4 by introducing features that improve LP profitability. The protocol introduces several novel mechanisms, including rehypothecation and a new AMM model designed to recapture maximal extractable value (MEV). However, this article will focus on three key innovations that work synergistically to optimise liquidity management:

Liquidity Density Functions (LDF) – These enable efficient distribution and modification of liquidity across different price ranges while maintaining constant gas costs.
Shapeshifting – This allows LPs to provide liquidity in complex shapes and seamlessly shift between different configurations, either manually or programmatically.
Autonomous Rebalancing – This feature eliminates the need for external keepers by automatically adjusting token ratios to maintain optimal performance.

Together, these features enable liquidity to be managed dynamically and automatically, optimising yield without requiring manual intervention from LPs.

Simulating Bunni's new Tools

To empirically evaluate Bunni’s effectiveness, we conducted a simulation comparing its features against a standard static Uniswap liquidity position. Specifically, we examined the impact of Bunni’s Shapeshifting and Autonomous Rebalancing mechanisms in a volatile asset pair. While an existing example of such a pool exists (Bunni-ETH#8), we created a fictitious pair with similar characteristics to better control the experimental parameters.

Pool Setup

The simulated liquidity pool was configured as follows:

Liquidity Density Function (LDF): Implemented a double carpeted geometric distribution to efficiently capture the price volatility of the assets.
Shapeshifting: Enabled in both upward and downward modes, allowing the LDF’s origin to shift dynamically in response to price movements.
Autonomous Rebalancing: Activated to maximise the utilisation rate of the shapeshifting LDF.

Simulation Methodology

We simulated 500 random price paths over a one-year period using a normal distribution with drift to model realistic price fluctuations.

Two LP positions were then constructed for comparison:

Static LP – A standard Uniswap position with wide upper and lower bounds that remained static and unadjusted throughout the simulation.
Dynamic LP – A Bunni-enabled LP position with tighter bounds, an LDF function, and dynamic reallocation based on price movements.

After executing the 500 simulations, we analysed the fee annual percentage return (APR) generated by each position across all trials.

Results and Findings

The analysis of the simulations revealed that the Bunni-enabled dynamic LP exhibited a 35% higher mean APR compared to the standard static LP. Examining the fee APR distributions across all simulations, we observed that the static LP maintained more consistent returns, whereas the dynamic LP exhibited more variance depending on market trends.

Further breakdowns of performance under different market conditions illustrate the advantages and drawbacks of each approach. In upward-trending markets, the dynamic LP outperformed the static LP by 47%, largely because the rapid price increase pushed it outside the static LP’s predefined range. This discrepancy is visible in the graph below, where the dynamic LP maintains more exposure to the price movement. However, in sideways and downward-trending markets, the Bunni-enabled dynamic LP outperformed the static LP by approximately 33%. The graphs demonstrate that in these conditions, price movements remained within the range of the both LP, allowing for constant fee accruals.

A closer look at the dynamic LP’s range tracking in upward-trending markets suggests that the conservative shapeshifting parameter used in the simulation resulted in an imperfect range shift. A more aggressive setting, as illustrated in our alternative model, would have better followed the price trend, capturing additional fees.

Sensitivity Analysis

To explore how different range widths and shapeshifting strengths affect the fee APR in a Bunni-enabled dynamic LP, we conducted a sensitivity analysis in a sideways market scenario. The results, visualized in the accompanying sensitivity graph, confirm that tighter liquidity ranges with higher sensitivity to price changes yield superior APRs. However, excessively tight ranges also increase the frequency of rebalancing operations, leading to higher gas costs. The optimal configuration, as shown in the graph, balances improved fee accrual with minimal operational expenses.

This optimisation problem still remains central to liquidity provision strategies. Nevertheless, our findings suggest that even a conservative implementation of Bunni’s liquidity tools significantly enhances returns compared to traditional static Uniswap LP positions.

Conclusion

Bunni represents the continuous paradigm shift in liquidity provision by automating and optimising key aspects of liquidity management. Our simulations demonstrate that Bunni’s liquidity density functions, shapeshifting, and autonomous rebalancing significantly enhance fee APR compared to static Uniswap positions.

While there are trade-offs in range selection and gas costs, the tools provided by Bunni offer substantial improvements in capital efficiency. Moreover, additional features such as rehypothecation could further enhance profitability, making Bunni a compelling solution for LPs seeking to maximise yield.

For further technical details and real-world implementations, Bunni’s official documentation provides more insights into the platform’s full capabilities.

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How Bunni can help Liquidity Providers maximise their returns