Cadabra's mechanics
Cadabra is a yield aggregator designed to simplify yield generation for liquidity providers (LPs) through easy-to-use vaults (strategies). These strategies offload user-provided liquidity to the multiple underlying yield-generating protocols (yield sources), with periodic rebalance between these yield sources. At its core, Cadabra employs a modified ve(3;3) tokenomics model, enhanced to achieve several key goals in a simple and elegant way.
Core mechanics, the high level overview.
LPs depositing into Cadabra strategies forgo any rewards of integrated protocols and receive emission-based rewards exclusively in ABRA tokens. This simplifies the user experience by abstracting away the complexities of managing multitude of reward types from underlying yield sources, including points and vested rewards.
Unlike typical ve(3;3) systems, where ve-token holders vote for pools based on past performance (fees already collected), Cadabra's ve(3,3) is proactive. Voters at Cadabra vote for the yield sources they anticipate will be most profitable in the coming week to capture future rewards that these sources will generate.
Based on the weekly vote, Cadabra rebalances liquidity within each strategy, allocating a larger share of the strategy's liquidity to the yield sources that received more votes. This means voters effectively predict the most profitable yield sources, steering the rebalance according to their collective insights.
Here’s how the whole process works:
Vote: veABRA holders (locked ABRA) cast their votes for the yield sources they believe will perform best in the upcoming week.
Rebalance: Once voting concludes, Cadabra rebalances liquidity within strategies to align with the vote results.
Reward LPs: LPs receive ABRA emissions based on how many votes their strategy's yield sources accumulate. Strategies with more votes earn a larger share of ABRA emissions, leading to higher APRs for those LPs.
Collect: Over the next week, the underlying yield sources generate yield, and at the end of the week, the yield is proportionally distributed to voters.
While ve(3;3) tokenomics isn’t new, Cadabra innovates by applying it to liquidity aggregation and refining its mechanics. Instead of traditional ve-tokenomics, where votes are influenced by past rewards, our model focuses on future rewards. By incorporating voting-based rebalancing and allowing speculation on illiquid rewards (such as points and vested tokens), Cadabra directly addresses several key problems outlined in the introduction:
Solving aggregator parasitism. By having two categories of users: LPs and voters we decoupling LP yields from underlying rewards. Voters capture all underlying rewards, each voter then manages these rewards as they see fit. Cadabra itself doesn't sell any tokens.
Realization of points and vested tokens. We intend to direct points from the integrated protocols to the voters. The potential future value of these points will drive competition among voters to capture them, leading to more votes for the associated yield source (and hence the strategy containing this source), increasing the APR for LPs in the strategy. This system is remarkably capital-efficient for protocols. They can attract and incentivize liquidity by offering a real APR without needing to allocate funds for direct reward distributions.
The same principle applies for vested tokens. Some protocols distribute rewards as tokens that unlock over time (often months). Voters willing to wait for these rewards can capture their value by voting for yield sources that offer vested tokens, leading to higher APRs for strategies that have these types of yield sources. This basically turns voting into yield speculation where strategic voting can return high rewards.
Using "wisdom-of-crowd" instead of blackbox rebalancing mechanisms. We basically turn our rebalancing process into a prediction market. That means voters effectively predict the most profitable yield sources, steering the rebalance according to their collective insights. This we think is much more transparent and will lead to better outcomes than black box algorithm-driven rebalancing methods used by some yield aggregators.
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