Features
A detailed view of all further updates that equilibria's team will implement.
Risks management solutions
One of the critical points in a lending protocol is to use parameters capable of sustaining the platform in any market conditions and for any type of user. As much as a common and shared decision by the community through a governance proposal is extremely important to ensure a community-driven approach, numerical outputs must be reliable, meticulously analysed and studied through a technical approach based on tangible data. Due to our strong data science background, we would like to introduce our solution that, combined with the Multi-Agent-Based simulation would allow a perfect combination of risks management solutions.
What we are working on is an arbitrary and permissionless solution. It will analyse all the liquidations that have happened in the past, taking into account several parameters such as
Date time
User Collateral Assets
User loan-to-collateral rati
Volatility of the assets involved
Market conditions
The Model will be able to statistically predict the likelihood of the borrower experiencing liquidation difficulties. We'll show these results to any user who wants to borrow assets, making them more aware of the potential risks and suggesting a lower loan-to-collateral ratio to ensure a better risk.
We strongly believe in this solution and we are currently conducting research on this one. We have already shared the first article where we analyse the major lending markets globally and extract some key statistics. You can find the article here. We also invite you to follow us because soon we'll publish the second part where we'll show concrete work of the dataset, including the data collection techniques and the dataset with number and types of features that will be used to train the ML model
Governance
The first upgrade we want to implement is the development of a community-driven governance system. Each user will be able to propose changes and updates to the platform, and the community will be able to vote for or against the proposal. This update would make the platform totally community-driven and decentralised while increasing user engagement. The general workflow of this can be summarised in the following steps:
Governance Mechanism: The token would include a governance mechanism that allows token holders to submit proposals and vote on various aspects of the platform or protocol.
Staking and Voting: Token holders would stake or lock their tokens to participate in the governance process. The more tokens a user stakes, the greater their voting power. Voting could be based on a simple majority or other voting systems like quadratic voting.
Proposal Submission: Token holders can submit proposals related to the platform or protocol's development, upgrades, or financial incentives. Proposals would typically require a minimum number of tokens to be staked by the proposer to prevent spam.
Voting Period: Once a proposal is submitted, there would be a predefined voting period during which token holders can cast their votes. After the voting period ends, the outcome is determined based on the voting system in place, and the proposal is either accepted or rejected.
Updates Implementation: developers will immediately update the platform with changes voted by community users.
This update of the dApp has not only benefits on the Equilibria community, in fact a Governance platform would allow us to update the Interest Rate Model. In V1 a simpler model will be used (already discussed in the previous section), while in V2 a more complex mechanism will be adopted in one hand but more engaging and accurate in the other. The new parameters introduced and the mechanism can be summarised in the following points:
Base Rates and Multipliers: Each market has a base borrowing rate, a multiplier over the base rate, and a jump multiplier. These parameters will be set by the protocol's governance.
Utilization Rate: We already discussed this parameter.
Interest Rate Model: The interest rate is a piecewise linear function of the utilization rate:
For utilization rates below or at 80% (this number may change and is set by the protocol), the borrow rate increases linearly with the utilization rate. The slope of this line is determined by the base rate and the multiplier over the base rate.
For utilization rates above 80%, the borrow rate increases more steeply with the utilization rate. The slope of this line is determined by the jump multiplier.
Formally, the interest rate model will be expressed as follows:
If Utilization Rate <= 80%:
Borrow Rate = Base Rate + Utilization Rate * Multiplier
If Utilization Rate > 80%:
Borrow Rate = Base Rate + 80% * Multiplier + (Utilization Rate - 80%) * Jump Multiplier
These rates are per Ethereum block. To get the annual percentage rate (APR), these per-block rates are compounded every block.
Governance Token
In this update, it is also possible to introduce a platform-specific token, which serves as a Governance Token. It would bring multiple benefits to the platform such as:
Increasing the interest rates of Suppliers: by using their own token, suppliers could be given the option of receiving rewards via the deposited token or the platform's own token.
Voting Power: like any typical governance system, each user would have voting power proportional to the amount of Equilibria tokens held. Greater quantities correspond to greater power on the platform, increasing the demand for the token and its deflationary status.
As this is a long-term projected update, we have not structured tokenomics in detail. However, we do have some principles on which it will be based: considering that the token might be subject to selling pressure due to its use in the rewards system, the token will have a supply cap, a minting system and burning features that will ensure a fair and balanced deflation. Many projects in the previous bullrun failed because of a totally unsustainable system that did not protect the value of the token from selling pressure, leading to its collapse. Our aim is to absolutely prevent the Equilibria token from making the same mistake.
Analytics
We strongly believe that developing and displaying Equilibria analytics is a key part of the User Experience. These key-metrics can bring countless benefits, including greater transparency into the transactions made and the health of the protocol and greater insight for traders and users of the protocol in understanding how the platform is evolving. These statistics will also be important for team members to monitor the growth of the platform. For an awesome User Experience, we would also like to integrate analytics related to the individual user. For this, it is possible to divide the key-stats into two categories. The first is the general metrics, where they will be shown:
Total Value Locked (TVL): This metric represents the total amount of assets deposited into a lending protocol. A higher TVL indicates greater liquidity and user trust in the platform.
Lending and Borrowing Rates: Monitoring the lending and borrowing rates for various assets helps users make informed decisions about supplying or borrowing assets in a lending protocol.
Collateralization Ratio: This metric indicates the level of over-collateralization in the lending protocol, reflecting the protocol's risk profile. A higher collateralization ratio provides a larger buffer against market volatility and reduces the likelihood of mass liquidations.
Loan-to-Value (LTV) Ratios: The LTV ratios for supported assets show the amount users can borrow against their collateral. Monitoring LTV ratios can help users understand the risk associated with borrowing specific assets.
Liquidation Thresholds: This metric represents the maximum LTV allowed before a borrower's position is at risk of being liquidated. Tracking liquidation thresholds can help users manage their borrowing risk more effectively.
Liquidations: Monitoring the number and value of liquidations can help users assess the lending protocol's risk profile and market conditions.
Token Price and Market Cap (for updates): Tracking the native token's price and market capitalization can provide insights into the lending protocol's overall valuation and perceived utility.
Asset Distribution: Analyzing the distribution of different assets within the lending protocol can reveal trends, user preferences, and potential risks associated with specific assets or markets.
Gas Fees: Tracking the gas fees associated with lending protocol transactions can help users make informed decisions about the cost-effectiveness of their interactions with the platform.
Users Clustarization: A Machine Learning algorithm that clusters users by their behavior and transactions on the platform.
As mentioned, user will also be able to display metrics about a specific wallet such as:
Analytics tools can provide valuable insights into a specific user's activity on a DeFi lending protocol by displaying a range of metrics related to their deposits, loans, rewards, and transactions. Some key metrics for analyzing a user's activity include:
Deposited Assets: Display the total value and individual assets deposited by the user in the lending protocol.
Current Lending APY: Show the current annual percentage yield (APY) the user is earning on their deposited assets.
Accrued Interest: Present the total interest earned by the user on their deposits since the initial deposit or the last withdrawal.
Borrowed Assets: Display the total value and individual assets borrowed by the user from the lending protocol.
Current Borrowing APR: Show the current annual percentage rate (APR) the user is paying on their borrowed assets.
Collateralization Ratio: Indicate the user's collateralization ratio, calculated as the total value of collateral divided by the total value of borrowed assets.
Loan-to-Value (LTV) Ratio: Show the user's LTV ratio, calculated as the total value of borrowed assets divided by the total value of collateral.
Liquidation Risk: Display the user's liquidation risk, including the current distance from the liquidation threshold and the threshold itself.
Rewards Earned: Present any rewards the user has earned by participating in the lending protocol, such as governance tokens or liquidity mining rewards.
Transaction History: Display a detailed history of the user's transactions related to the lending protocol, including deposit, withdrawal, borrow, repay, and liquidation events.
Gas Fees: Show the total gas fees incurred by the user for their transactions with the lending protocol.
Net Profit/Loss: Calculate the user's net profit or loss based on their lending, borrowing, rewards, and transaction costs.
As Astrid is a team born primarily from on-chain data analysis, developing an accurate and easy-to-use analytics platform is of paramount importance as well as a doable task for the team knowledges.
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