Overview

1. Data Fetching:

• The Ringo engine fetches real-time data from decentralized finance (DeFi) platforms, blockchain sources, and market APIs. This data is crucial for risk analysis and portfolio management.

• Data points include asset prices, yields (APY), utilization rates, transaction volumes, and liquidity metrics. For building this part, developers should integrate APIs that can provide real-time market data and transaction information from different DeFi protocols.

2. Risk Calculation:

Risk is calculated to assess the potential exposure to market fluctuations and other risks. The process includes several key components:

• Volatility Risk Analysis:

  • Lending Pools: The risk is based on the APY stability and utilization rate fluctuations. Developers will calculate the standard deviation of hourly APY changes and utilization rates over 24 hours.

  • Liquidity Pools: The risk is assessed using metrics like the turnover ratio and normalized yield factor. These metrics measure trading activity and the pool’s yield performance.

  • Formula examples:

    • Lending pool volatility:

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      Rv,l = w_a * σ_APY + w_u * σ_U

    • Liquidity pool volatility:

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      Rv,p = w_t * Tr + w_y * Yn

  • Developers must implement these formulas in the engine to evaluate risk dynamically.

• Liquidity Risk Assessment:

  • The liquidity risk involves assessing how easily assets can be traded without large price impacts.

  • For lending pools, liquidity risk might depend on how quickly loans can be repaid and borrowed without affecting the pool's health.

  • Liquidity pools focus on the ability to buy and sell assets without significant slippage.

• Protocol Risk Evaluation:

  • This includes assessing the stability and security of the underlying DeFi protocols that the assets are tied to, ensuring that the protocol itself is not prone to vulnerabilities or smart contract failures.

• User Risk Preferences:

  • The risk preferences of users are encoded in a parameter (α), which defines the risk tolerance. This parameter influences the risk profile of the portfolio.

  • Common values might be:

    • Low risk (α = 0.3)

    • Medium risk (α = 0.6)

    • High risk (α = 0.9)

  • Developers need to map these risk preferences to portfolio allocation strategies.

3. Rebalancing:

The rebalancing process ensures that user portfolios stay aligned with their defined risk profiles while adjusting to market changes.

• Rebalancing Logic:

  • Developers need to calculate optimal asset weights for each portfolio based on the risk tolerance and the current market conditions.

  • Cooldown Period: The system implements a cooldown period before rebalancing can happen again. This period varies based on the user's risk profile:

    • Low risk: 24 hours

    • Medium risk: 12 hours

    • High risk: 6 hours

  • The cooldown prevents constant adjustments, reducing transaction costs and market volatility.

• Thresholds for Position Changes:

  • Developers will also set minimum thresholds for position changes. If a rebalancing action would result in a minimal change (e.g., below a certain percentage), it is avoided to reduce unnecessary trading.

4. Workflow:

Here's the step-by-step workflow for managing a portfolio:

• Step 1: Fetch Data

  • Integrate APIs to fetch real-time market data: asset prices, APY, utilization rates, volume, and liquidity.

• Step 2: Assess Risks

  • Calculate volatility, liquidity, and protocol risks using the formulas outlined earlier.

  • Determine user risk preferences based on α values.

• Step 3: Portfolio Allocation

  • Based on the risk calculations, define an optimal asset allocation that aligns with the user’s risk profile.

  • This involves setting weights for assets that ensure the portfolio adheres to the desired risk tolerance.

• Step 4: Rebalance Portfolio

  • If market conditions change and the portfolio drifts from the optimal allocation, trigger rebalancing.

  • Ensure the cooldown period and position change thresholds are respected.

• Step 5: Continuous Monitoring

  • The system should continuously monitor market conditions and user portfolios to make dynamic adjustments as necessary.

  • Automated portfolio rebalancing ensures that portfolios always align with the user’s risk profile.

5. Key Technical Considerations

• Data Handling: Ensure smooth integration with DeFi data sources like Chainlink oracles or other blockchain-based APIs to fetch real-time data.

• Risk Model Implementation: Accurately implement the volatility, liquidity, and protocol risk assessment formulas in the backend logic.

• Rebalancing Algorithm: Develop a robust algorithm for portfolio rebalancing that accounts for risk preferences, cooldown periods, transaction fees, and optimal asset weightings.

• Real-time Execution: The Ringo engine needs to be capable of handling real-time market data and adjust portfolios quickly based on market changes.