Currently, there are two major shortcomings in the DeFi fixed income sector: First, the value anchoring is weak, with over 70% of protocols anchoring asset value solely to a single crypto asset price (such as ETH/USDC). During market fluctuations, the deviation between the actual asset value and nominal value exceeds 5%, and institutions must additionally bear the 'value drift' risk during allocation; Second, scenario adaptation is rigid, with most scenarios adopting a 'uniform rule adapting to all assets' model, leading to fixed collateral rates for tAssets across chains and uniform RWA investment review processes, causing high-credit assets to be unable to enjoy better conditions and low-risk users to face redundant costs. TreehouseFi constructs a 'Value Anchoring Enhancement and Scenario Adaptation Flexibility System (VAEA)', strengthening asset value stability and scenario adaptability through multi-anchor value anchoring, flexible adjustment of scenario rules, and dynamic matching of user needs, while aligning with the industry's trends of RWA normalization and institutional refined allocation.
1. Multi-anchor enhancement of asset value: Breaking the 'single anchor' volatility risk.
The industry generally anchors the value of fixed-income assets to a single crypto asset, resulting in weak resistance to market fluctuations—when the crypto market drops by more than 5% in a single day, the actual redemption value of most tAssets deviates from their nominal value by over 3%, requiring institutions to additionally allocate hedging tools, increasing costs by 1%-2%. The VAEA system constructs a 'multi-dimensional value anchoring structure' for assets, establishing a triple anchoring support of 'fundamental anchoring + credit anchoring + ecological anchoring', reducing the risk of value drift.
Technical layer relies on multi-source Oracle and on-chain credit contracts: The foundational anchoring layer connects both decentralized interest rate benchmarks (DOR) and traditional financial indicators (such as the U.S. 10-year Treasury yield, LIBOR). The returns of stable assets like tUSDC are linked to the real-time supply and demand of DOR, while also referencing traditional bond yields, to mitigate the impact of fluctuations in a single crypto market; the credit anchoring layer is based on users' on-chain performance data (such as the duration of holding tAssets, and full payment records for RWA). Users holding assets for over 60 days without default can gain a 'credit premium anchor' of 5%-8% on their asset value, enhancing value stability without the need for third-party endorsement; the ecological anchoring layer correlates with the ecological contributions corresponding to the assets (such as governance voting and liquidity provision). Users participating in ecological governance can enjoy 'value discount protection' on their assets in scenarios—when market fluctuations occur, assets with higher ecological contributions experience a value deviation that is 40% lower than that of ordinary assets.
This multi-anchoring model narrows the asset value fluctuation range from the industry average of 8% to under 3%, reducing institutional hedging costs by over 60%, aligning with institutions' core configuration needs for 'value stability'.
2. Flexible adjustment of scenario adaptation: Resolving inefficiency in 'uniform rule' adaptation.
Traditional protocols often employ 'one-size-fits-all' scenario rules—tETH crossing to any public chain uniformly adopts a 92% collateral ratio, and the review process for AA+ rated RWA is the same as that for A-rated RWA, resulting in low efficiency for allocating high credit assets and redundant operations for low-risk users. The VAEA system utilizes an 'asset attribute-driven flexible rule engine', allowing scenario rules to dynamically adjust based on asset type, credit rating, and user risk preference.
Its core logic relies on the 'Asset Attribute Database' and 'Dynamic Parameter Library': For tAssets, the scene automatically reads the liquidity data of the target chain for the assets (such as the staking demand for tETH on Arbitrum, and the lending rates for tUSDC on Mantle). When crossing to a highly liquid chain, the collateral ratio is reduced by 2%-3%, and when crossing to a low liquidity chain, the collateral ratio is increased by 3%-5%, improving adaptability efficiency by 70%; for RWA, the scene adjusts the review process based on asset credit ratings (such as S&P AA+, Moody's A3)—AA+ rated RWA can enable a 'fast review channel' (reducing review time from 48 hours to 6 hours), while A-rated RWA triggers 'additional due diligence verification' (connecting to underlying asset cash flow data interfaces); for user risk preferences, the scene adjusts operational permissions based on users' on-chain risk ratings (such as low risk, medium risk)—low-risk users redeeming tUSDC can enjoy 'T+0 arrival + 0.1% fee exemption', while medium-risk users must wait for 'T+1 arrival', balancing risk and efficiency.
Additionally, the rule engine supports 'asset-scenario' bi-directional adaptation—when a certain type of asset (such as tBTC) sees a surge in demand in lending scenarios, the scene automatically reduces the lending rate for that asset by 0.2%-0.3%, while increasing the collateral rate by 1%-2%, guiding more assets into the market while controlling risk. This flexible adjustment model enhances the asset adaptation efficiency of the scene by 45%, reducing user operational redundancy by 60%, catering to the differentiated needs of different assets and users.
3. Dynamic matching of user needs: Solving the mismatch of 'layered demands'.
The DeFi fixed income industry has long faced the issue of 'confusion between institutional demand and retail demand': institutions need to connect to custody systems and generate regulatory reports but lack specialized functions; retail users seek low-threshold operations but face complex parameter settings, resulting in a satisfaction rate of less than 65% for both user groups. The VAEA system achieves precise matching of needs and services through a 'user-layered driven dynamic matching mechanism'.
For institutional users, a 'compliance-driven dynamic module' is provided: It supports connections to the APIs of licensed custodians (such as Fireblocks, Anchorage), allowing asset holding data to be synchronized in real-time to the custody system; it integrates automatic regulatory report generation functions, producing compliance documents such as on-chain asset transaction flows, income details, etc., based on different regional requirements (EU MiCA, U.S. SEC), reducing review time from 48 hours to 4 hours; it opens customizable risk control parameter interfaces, allowing institutions to set tAssets staking rate ranges (such as 90%-95%), RWA investment limits, meeting detailed management needs.
For retail users, a 'lightweight demand matching tool' is launched: after users input 'idle fund duration (7 days/30 days), risk preference (conservative/aggressive)', the tool automatically matches suitable assets and scenarios—'30-day conservative' users are recommended a 'tUSDC liquidity + short-term debt RWA' combination, reducing the operational steps from 6 to 2; '7-day aggressive' users are recommended a 'tETH cross-chain arbitrage + small derivatives' combination, while also being alerted to risk boundaries (such as automatically exiting when the interest rate difference is less than 0.3%).
This dynamic matching mechanism enhances institutional compliance operational efficiency by 75%, and the demand satisfaction rate for retail users exceeds 90%, effectively breaking the industry's dilemma of 'mismatched layered demand adaptation'.
Future trend predictions.
In light of the trends of 'RWA normalization deepening' and 'increased institutional allocation ratios' in the DeFi fixed income sector, the value of the VAEA system will be further released within the next 12 months: its TVL is expected to grow from the current $400 million to $1.2 billion, entering the top 25 of DeFi fixed income protocol TVLs; the coverage of RWA scenarios will expand from the existing 8 categories (government bonds, corporate bonds, etc.) to 20 categories, adding sub-sectors like green energy ABS and consumer finance RWA; its 'multi-anchoring + flexible adaptation' logic is expected to become an industry adaptation standard, promoting the upgrade of DeFi fixed income from 'weak anchoring + rigid adaptation' to 'strong anchoring + flexible adaptation', providing technological support for the digitalization of global fixed income.
