Ref Finance Onboards Gauntlet for Budget and Incentive Optimization

Gauntlet is excited to announce our partnership with Ref Finance. Ref Finance is a leading decentralized exchange (DEX) on NEAR focused on offering a fast and user-friendly trading experience. The Ref Finance team recognizes the importance of ensuring its LP rewards are budgeted and allocated efficiently to maximize volume and revenue back to the protocol and token holders. As part of this partnership, Gauntlet’s Incentive Optimization (IO) platform will ingest the Ref Finance protocol data to manage rewards quantitatively.

Modeling Incentive Spend

Incentives within DeFi are an essential component to bootstrapping liquidity and growth. Successful incentive programs create a virtuous cycle of attracting users, users generating fees, and fees supporting future growth efforts. It is important to understand the key drivers of efficiency, such as which liquidity pools should receive more incentives than others and how incentives should change over time in response to market conditions. Additionally, the total level of incentive spend needs to be set to ensure alignment with overall protocol budgets. Accounting for factors like partner pools and other strategic initiatives is required. The Gauntlet IO platform incorporates all these specifics and a broad range of market variables when determining optimal rewards to maximize spend efficiency.

Improving Spend Efficiency

Ref Finance and many similar protocols face the difficult challenge of predicting the effects of parameter changes to maintain efficient spending. Our platform simulates realistic market behavior to predict the impact of changes across all pools. To provide dynamic incentive optimization for Ref Finance, Gauntlet will use its incentive models to process Ref Finance protocol data and regularly recommend emission parameters for native, partner, and strategic pools.

The Ref community can find members of the Gauntlet team in the governance forum to deliver, communicate, and clarify parameter changes to facilitate understanding of the model outputs.

If you’re interested in learning more about Gauntlet’s IO platform, you can explore the platform and get in touch with our team here.