Announcing Layerswap ALM - Automated Liquidity Management
How we managed to process around 100M volume with just $500k in liquidity
The multi-chain/rollup future of the blockchain space creates new challenges for institutional liquidity operators like Centralized/Decentralized Exchanges and Market Makers. One of the main challenges is managing and operating liquidity of the same asset in multiple rollups and chains while keeping operational costs low.
If a multi-chain liquidity operator wants to succeed, they should be able to answer the following questions:
How much effective liquidity is needed in each chain?
When to transfer liquidity from one chain to another?
How to move from one chain to another?
Cross-chain bridges and interoperability protocols already provide a solution to the last question. But the remaining questions about managing the liquidity are still open and create a lot of operational overhead.
At Layerswap, we propose a solution that implements Automated Liquidity Management (ALM) that tracks and monitors all the asset balances in different chains. Based on historical balance data, our algorithms determine the best asset utilization ratio and, based on that ratio, automatically distribute total liquidity across all chains.
It is cost-effective, scalable, and compatible with all infrastructure setups (including any custom institutional grade key management solutions). Furthermore, we believe this solution can be implemented on the chain as well, being one of the reasonable examples of utilizing machine learning on-chain.
Our onramp layerswap.io already demonstrates the success of our liquidity management solution. In a few months, we processed around $100M in total volume by just using $500k in liquidity capital.
Layerswap ALM is in the active R&D and testing stage now. We hope to lower the operational costs of institutional liquidity operators to accelerate cross-chain adoption further. If you are interested in connecting with us, fill in this form, and we will get back to you as soon as possible.
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