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Table of Contents
- TL;DR
- Premise
- Pool Performance Summary
- Pool Reserve Changes
- Impermanent Loss
- Comparison With a Baseline CLAMM Position
- Comparison Summary
- Conclusion
- FAQ
- What is Algebra’s Price Convergence Plugin?
- How did the wtMSTR/USDC pool perform?
- Did the pool experience impermanent loss?
- How did it compare with static concentrated liquidity?
- Why did the Price Convergence Plugin perform better?
- Why is Price Convergence relevant for RWA liquidity?
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TL;DR
Algebra’s Price Convergence Plugin demonstrated that capital efficiency alone is not enough – liquidity also needs to be positioned intelligently.
Over a 13-day observation period, the wtMSTR/USDC pool on Hydrex:
- processed ~$44K in volume, equivalent to roughly 1.5x pool TVL per day;
- generated ~$132 in fees, corresponding to a 164% fee APR;
- achieved this with only ~$2.3K of initial liquidity;
- avoided impermanent loss and instead finished approximately $114 above the HODL benchmark.
Most importantly, when compared with an idealized, optimally positioned 1-tick concentrated liquidity position, the Price Convergence pool generated comparable volume and fees while producing a dramatically better LP outcome.
The baseline position would have earned roughly $131 in fees but suffered ~$132 in impermanent loss. The Price Convergence Plugin generated similar trading activity while delivering an estimated +$114 gain versus HODL.
The takeaway: the plugin did not need more liquidity to improve performance. It made the existing liquidity work more efficiently by continuously adapting it to the external reference price.
Premise
Algebra deployed its Price Convergence Plugin on a Hydrex pool trading wtMSTR, a tokenized MSTR equity token issued by ST0x.
The pool launched on August 24, 2026, with approximately $2,300 in liquidity provided by Algebra and a static fee of 0.3%.
Starting August 27, trading fees were redirected to the Hydrex ve(3,3) gauge and therefore stopped autocompounding into the pool's liquidity.
The Price Convergence Plugin used a Pyth oracle price feed as its external reference price.
Pool performance data was collected through September 5, 2026 for analysis.
Pool Performance Summary
Observation period: 13 days
Total volume processed: $43,984 – approximately 1.5x pool TVL per day
Fees collected: $132
Fee APR: 164%
Pool Reserve Changes
Date | Event | wtMSTR | USDC | Total Value, USD | wtMSTR Price, USD |
Aug 24 | Initial Deposit | 4.04 | 0.5 | 511 | 126.3 |
Aug 25 | Final Deposit | 6.34 | 1,534.28 | 2,302 | 121 |
Aug 27 | Fees Rerouted | 9.33 | 1,168.8 | 2,328 | 124.2 |
Sep 5 | Observation End | 18.97 | 0.1 | 2,705.5 | 142.5 |
Impermanent Loss
The pool experienced no impermanent loss relative to HODL. Instead, the value of its reserves grew beyond the corresponding HODL benchmark.
Detailed performance graph:
Comparison With a Baseline CLAMM Position
To evaluate the efficiency of the Price Convergence Plugin, we can compare its performance with an idealized baseline concentrated-liquidity position.
The strongest possible static baseline would be a single highly concentrated, 1-tick-wide position using the same amount of liquidity and placed at the optimal price level – the level crossed by the market most frequently during the observation period.
For the analysis, we assume that the position is fully traded whenever the price difference is large enough to cover the pool fee.
The analysis identified 19 tradable crossings of the optimal price level of $127.82.
Under these assumptions, the baseline position would have:
- processed approximately $43,696 in volume;
- generated approximately $131 in fees;
- incurred approximately $132 in impermanent loss.
In other words, even an optimally placed static concentrated-liquidity position would have generated almost the same trading volume and fee income as the Price Convergence pool – but with a substantially worse outcome for the LP.
Comparison Summary
Baseline | Price Convergence | |
Volume, USD | 43,696 | 44,489 |
Collected Fees, USD | 131 | 133 |
Gain vs HODL / IL, USD | -132 | +114 |
The difference is particularly significant because the improvement did not come from processing dramatically more volume or charging higher fees.
Both strategies captured roughly the same trading activity.
The difference came from how liquidity was managed around the reference price.
The static position earned fees but lost approximately the same amount through adverse inventory revaluation. The Price Convergence Plugin, by contrast, preserved the fee-generation potential of concentrated liquidity while turning inventory changes into an additional source of value.
Conclusion
The Price Convergence Plugin delivered strong results over the 13-day observation period.
With only around $2.3K in liquidity, the pool processed approximately $44K in trading volume, generated a 164% fee APR, and maintained roughly 1.5x daily volume-to-TVL turnover.
But the more important result is visible in the comparison with static concentrated liquidity.
An idealized 1-tick baseline could have generated almost identical volume and fees. However, it would have suffered approximately $132 in impermanent loss, effectively offsetting its fee income.
The Price Convergence pool instead ended the period approximately $114 above the HODL benchmark.
This suggests that the plugin's main efficiency advantage is not simply higher volume generation. It is the ability to capture comparable trading activity while managing liquidity and inventory more efficiently around an external reference price.
In the observed market conditions, the plugin effectively transformed the traditional concentrated-liquidity trade-off: rather than earning fees at the expense of impermanent loss, the pool earned fees while also generating additional value through active market-making mechanics.
FAQ
What is Algebra’s Price Convergence Plugin?
Algebra’s Price Convergence Plugin manages concentrated liquidity around an external reference price. In the wtMSTR/USDC pool, it used a Pyth oracle feed to adapt liquidity to the market price.
How did the wtMSTR/USDC pool perform?
Over 13 days, the Hydrex pool processed about $44K in volume with roughly $2.3K in liquidity, generated $132 in fees, and reached a 164% fee APR.
Did the pool experience impermanent loss?
No. The Price Convergence pool finished approximately $114 above the HODL benchmark during the observation period.
How did it compare with static concentrated liquidity?
An optimized 1-tick static CL position would have generated similar volume and fees, but suffered about $132 in impermanent loss. The Price Convergence pool instead finished +$114 vs HODL.
Why did the Price Convergence Plugin perform better?
The advantage came from dynamic liquidity positioning. The plugin continuously adapted liquidity around the external reference price instead of keeping it fixed in a static range.
Why is Price Convergence relevant for RWA liquidity?
Tokenized RWAs often rely on external reference prices and have limited arbitrage venues. Price Convergence can help align onchain liquidity with those reference prices while improving capital efficiency.
