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In the ever-evolving landscape of Solana DeFi, liquidity provision (LP) strategies are a critical component for maximizing returns. A recent experiment by Bradydon | 🐣HawkFi.ag on X (formerly Twitter) sheds light on the performance of two prominent decentralized exchanges (DEXs), Meteora and Orca, when paired with HawkFi's autorebalance feature. This thread, posted on June 30, 2025, details a 1 SOL LP position into the "Useless" token pools, revealing unexpected results and prompting further exploration into optimization techniques.

The Experiment Setup

Bradydon initiated the experiment by allocating 1 SOL to each of the Useless token pools on Meteora and Orca. Both positions utilized HawkFi's autorebalance feature, specifically set to a directional up-only strategy with a stop loss. The initial yield metrics, as observed from HawkFi's interface, indicated a higher daily yield on Orca (2.99%) compared to Meteora (2.07%) at the time of deposit. The goal was to compare the long-term performance over a multi-day period.

Results and Analysis

After the experiment concluded, the results were surprising. Meteora's pool yielded 1.28 SOL, outperforming Orca's 1.16 SOL. This outcome contradicted the initial yield metrics, prompting an investigation into the underlying factors.

Key Insights:

1. Autorebalance Settings: The primary reason for the discrepancy was inconsistent autorebalance settings. Meteora's pool was configured with tighter rebalance ranges, allowing it to capture more trading fees by frequently adjusting to stay in range. In contrast, Orca's wider rebalance ranges led to less frequent adjustments and, consequently, fewer fee captures.
2. HawkFi's Role: HawkFi's autorebalance feature is designed to automate the process of keeping LP positions optimal by adjusting them based on market conditions. This experiment highlights the importance of customizing these settings to align with the specific characteristics of each pool and token.

Bradydon noted, "I wonder how much MORE would USELESS on Orca print if I had set tight rebalance ranges," indicating a recognition of the potential for further optimization.

Implications for LP Strategies

This experiment underscores several important considerations for liquidity providers on Solana:

• Customization is Key: The effectiveness of autorebalance features heavily depends on the configuration. Tight rebalance ranges can enhance fee capture in volatile markets, while wider ranges might be more suitable for stable pairs.
• Tool Utilization: Platforms like HawkFi offer powerful automation tools that can significantly impact LP performance. Understanding and leveraging these tools can lead to better outcomes.
• Continuous Learning: The DeFi space is dynamic, and experiments like this one are crucial for refining strategies and staying ahead of market trends.

Looking Ahead

Bradydon expressed a commitment to further experiments, stating, "I'm throwing more SOL into LP experiments to find the best LP yield across Solana." This ongoing exploration is part of a broader effort to optimize LP strategies using HawkFi, which supports Meteora, Orca, and soon Raydium pools.

The competition in the Solana DeFi ecosystem is heating up, with Hoshii and chiftine commenting on the thread, indicating keen interest in the results and future developments. The mention of upcoming tokens like $FRAG also suggests that the community is eagerly anticipating new opportunities for yield farming.

Conclusion

The Useless SOL LP experiment on Meteora and Orca, facilitated by HawkFi's autorebalance feature, provides valuable insights into the nuances of yield optimization in Solana DeFi. While Meteora edged out Orca in this instance, the results highlight the critical role of configuration and the potential for further improvements. As the DeFi landscape continues to evolve, such experiments and the tools that enable them will be instrumental in shaping effective LP strategies.

For those interested in diving deeper into Solana's DeFi offerings, HawkFi's documentation offers a comprehensive guide to its features, including auto-compound, autorebalance, and more. Additionally, resources like Solana Compass can help identify top-yielding LPs across various AMMs.

Stay tuned for more updates as Bradydon and the HawkFi team continue to push the boundaries of DeFi automation and yield optimization.