As an experienced market maker and DeFi enthusiast, I’ve spent years collecting data, building models, conducting research, and creating quantitative analyses. This journey inspired me to develop software for Wisenode, aiming to simplify and enhance the visual appeal of these processes. Recently, I had the opportunity to join the beta testing team for HawkFi, a platform with Meteora integration. It quickly became a favorite, automating tedious manual tasks like managing liquidity pools and implementing risk mitigation tools such as stop-losses.
However, I noticed inconsistencies in my performance between HawkFi and Meteora, prompting me to investigate further. My goal? To establish a baseline and determine how to best leverage HawkFi’s toolkit for optimal performance with minimal active management.
The Experiment
I set up a controlled experiment involving two SOL/USDC liquidity pools on Meteora and two on HawkFi. Each pool used the same amount of capital ($1,000) with full-range and spot liquidity profiles to ensure a level playing field. Specifically, I tested:
- 20-bin pool on Meteora
- 50-bin pool on Meteora
- 20-bin pool on HawkFi
- 50-bin pool on HawkFi
The experiment has been running for four weeks, capturing sideways and downtrending market conditions. I plan to continue until I gather data across all market conditions, including uptrends. Below, I’ll share interim results, supported by screenshots and charts from the Wisenode Finance app.
Performance Analysis
Fees Earned
- 20-bin pool on HawkFi: $188.93 (even after an 8% performance fee). HawkFi’s auto-rebalancing ensured the position stayed in range, maximizing fee collection.
- 20-bin pool on Meteora: Performed slightly worse due to frequent out-of-range periods, which halted fee generation.
- 50-bin pool on HawkFi: $102.84, lower yield due to the wider range.
- 50-bin pool on Meteora: $96.56, marginally underperforming HawkFi due to occasional out-of-range periods.
Divergence Loss (Impermanent Loss)
- 50-bin pool on HawkFi: $168.30 – Lowest divergence loss due to wider range and automated rebalancing.
- 50-bin pool on Meteora: $179.51 – Slightly higher loss, attributed to manual rebalancing timing.
- 20-bin pool on Meteora: $203.69 – Smaller range led to frequent out-of-range periods and realized losses during manual rebalancing.
- 20-bin pool on HawkFi: $299.59 – Highest loss due to excessive rebalancing in unfavorable market conditions.
Total P&L
After incorporating fees earned, divergence loss, and liquidity value:
- 50-bin pool on HawkFi: -$65.46 – Slightly better performance overall.
- 20-bin pool on Meteora: -$68.98 – Benefited from less frequent rebalancing.
- 50-bin pool on Meteora: -$82.95.
- 20-bin pool on HawkFi: -$110.66 – Overactive rebalancing significantly impacted performance.
Key Insights and Recommendations
While HawkFi’s auto-rebalancing increases uptime for fee generation, it can also hinder performance due to frequent, non-discretionary timing. Manual management on Meteora, though less automated, offers better control over rebalancing decisions. HawkFi’s planned addition of conditional order features for rebalancing could greatly enhance its efficiency by introducing discretionary elements.
Another opportunity lies in leveraging custom liquidity profiles. Although they require manual adjustments, they can boost performance by adapting to market conditions. Alerts for rebalancing on Meteora could further improve manual management outcomes.
Next Steps
This experiment will continue until I’ve gathered data in an uptrending market. I’ll provide updates as new insights emerge. For now, I hope this analysis helps you optimize your own liquidity pool strategies.
Stay tuned for more findings, and let me know if you have questions or insights of your own!
