Detailed Instructions

Begin by identifying the smart contract addresses for the target yield optimizer vaults and their underlying strategies. Use block explorers like Etherscan or blockchain-specific RPC nodes to query historical transaction logs and event emissions. For off-chain data such as APY calculations and strategy descriptions, access the protocol's official API endpoints or subgraph queries. For example, Yearn's v3 vault data can be sourced from its public subgraph. Establish a reliable connection to an archival node or a service like The Graph to ensure access to full historical state, not just recent blocks.

• Sub-step 1: Compile a list of vault contract addresses (e.g., 0x19D3364A399d251E894aC732651be8B0E4e85001 for yvDAI).
• Sub-step 2: Identify the relevant event signatures for deposits, withdrawals, and strategy reports.
• Sub-step 3: Set up a connection to a node provider (Infura, Alchemy) with archival capabilities.

javascriptCopy
// Example: Fetching vault creation events from Yearn Registry
const eventSignature = 'VaultCreated(address,address,address,string,string)';
const topics = [ethers.id(eventSignature)];
const logs = await provider.getLogs({
    address: registryAddress,
    topics: topics,
    fromBlock: 12000000
});

Tip: Bookmark the protocol's official GitHub repository for contract ABIs and documentation to ensure accurate event decoding.

Detailed Instructions

Query the blockchain for all logs emitted by the vault and strategy contracts over your analysis period. The key events are Deposit, Withdraw, and StrategyReported. Decode the log data using the contract's Application Binary Interface (ABI) to extract meaningful parameters: user address, asset amount, share price, and total assets. For performance analysis, the StrategyReported event is critical as it contains the gain, loss, debt paid, and new total debt for a strategy. Pay close attention to block timestamps to sequence events correctly. Use batch processing and consider gas costs when querying large historical ranges.

• Sub-step 1: Filter logs by the specific vault address and event topics for a defined block range.
• Sub-step 2: Decode the data and topics fields using the ethers.js or web3.py ABI.
• Sub-step 3: Calculate derived fields like effective share price at the time of each user action.

pythonCopy
# web3.py example: Decoding a StrategyReported event
strategy_report_abi = vault_contract.events.StrategyReported._get_event_abi()
log_data = w3.eth.get_logs({'address': vault_address, 'fromBlock': start_block, 'toBlock': end_block})
decoded_events = w3.eth.contract(abi=[strategy_report_abi]).events.StrategyReported().processReceipt({'logs': log_data})
for event in decoded_events:
    gain = event['args']['gain']
    totalDebt = event['args']['totalDebt']

Tip: Store raw log data with block numbers and transaction hashes to maintain an immutable audit trail for your analysis.

Detailed Instructions

Process the parsed event data to construct a daily time-series for each vault. The core metric is the vault share price, which can be calculated from sequential StrategyReported events: newSharePrice = (totalGain - totalLoss + totalDebt) / totalSupply. From this, compute the daily return. Calculate the Annual Percentage Yield (APY) by compounding daily returns over 365 days. Also, derive risk metrics like drawdown (peak-to-trough decline in share price) and volatility (standard deviation of daily returns). For multi-asset vaults, normalize all values to a common denomination like USD using historical price oracles (e.g., Chainlink data feeds) to enable cross-vault comparison.

• Sub-step 1: For each day, determine the closing share price from the last relevant event.
• Sub-step 2: Compute daily return: (Price_t / Price_{t-1}) - 1.
• Sub-step 3: Annualize returns: APY = ((1 + avg_daily_return) ^ 365) - 1.
• Sub-step 4: Calculate the maximum drawdown over rolling 30-day windows.

sqlCopy
-- Example SQL to calculate daily returns from a share_price table
SELECT
    date,
    share_price,
    LAG(share_price, 1) OVER (ORDER BY date) as prev_price,
    (share_price / LAG(share_price, 1) OVER (ORDER BY date)) - 1 as daily_return
FROM vault_share_prices
ORDER BY date;

Tip: Use a consistent compounding model (daily vs. continuous) when calculating APY and clearly document your methodology.

Detailed Instructions

Design a normalized database schema or Pandas DataFrame structure to support efficient analysis. Core tables should include vaults (metadata), daily_metrics (share price, TVL, APY), user_actions (deposits/withdrawals), and strategy_reports. Establish clear foreign key relationships. For time-series analysis, ensure the daily_metrics table has a composite key of (vault_id, date) with no gaps. Include derived columns for rolling volatility (e.g., 7-day and 30-day standard deviation) and risk-adjusted returns like the Sharpe Ratio (assuming a risk-free rate). This structure allows for complex queries comparing performance across vaults, strategies, and market conditions.

• Sub-step 1: Create a vaults dimension table with columns: id, address, name, underlying_asset, version.
• Sub-step 2: Populate a daily_metrics fact table with calculated fields for each vault and date.
• Sub-step 3: Create indexes on frequently queried columns like vault_id and date.
• Sub-step 4: Add a market_data table for historical ETH/USD or relevant asset prices.

pythonCopy
# Pandas DataFrame structure example
import pandas as pd
df_metrics = pd.DataFrame({
    'date': pd.date_range(start='2023-01-01', periods=365),
    'vault_id': 'yvUSDC',
    'share_price': [...],
    'tvl_usd': [...],
    'daily_return': [...],
    'apy_7d_ma': [...]  # 7-day moving average APY
})
df_metrics.set_index(['vault_id', 'date'], inplace=True)

Tip: Use a data versioning system (like DVC) to track changes to your processed datasets, especially when recalculating metrics with updated logic.

Detailed Instructions

Before analysis, run a series of validation rules on your structured dataset. Check for data completeness—ensure there are no missing days in the time-series for active vaults. Verify logical consistency: the total value locked (TVL) should be non-negative, and share price should be monotonically non-decreasing outside of fee events or losses. Cross-validate your calculated APY against official protocol dashboards for a sample period, allowing for minor discrepancies due to calculation timing. Perform sanity checks on outlier values; a daily return exceeding 50% likely indicates a data parsing error or a unique event like a massive liquidity injection. Automate these checks in your data pipeline.

• Sub-step 1: Query for gaps in the daily time-series for each vault.
• Sub-step 2: Assert that TVL equals (share_price * total_supply) within a small tolerance.
• Sub-step 3: Spot-check APY calculations for specific dates against third-party sources like DefiLlama.
• Sub-step 4: Identify and investigate days with returns beyond +/- 3 standard deviations from the mean.

pythonCopy
# Example validation function for share price consistency
def validate_share_price_integrity(df):
    # Check for non-negative share price
    assert (df['share_price'] > 0).all(), "Share price must be positive"
    # Check for drastic single-day drops not explained by fees (e.g., >5%)
    df['price_change'] = df['share_price'].pct_change()
    drastic_drops = df[(df['price_change'] < -0.05) & (df['fee_event'] == False)]
    if not drastic_drops.empty:
        logger.warning(f"Unexpected drops detected: {drastic_drops.index.tolist()}")
    return True

Tip: Maintain a validation log that records the date, check performed, and any anomalies found for audit purposes.

Detailed Instructions

Begin by gathering data on all historical incidents involving the yield optimizer, its integrated protocols (e.g., Aave, Compound, Curve), and its underlying infrastructure (e.g., oracles, keepers). Sources include protocol post-mortems, DeFi security databases (Rekt.news, DeFiYield), and community forums. Categorize each event by type: smart contract exploit, oracle failure, governance attack, economic design flaw, or liquidity crisis. For example, note the specific optimizer vault (e.g., Yearn's yvUSDC) and the date of the incident. This creates a structured event log for quantitative analysis.

• Sub-step 1: Query on-chain analysis platforms like Dune Analytics for unusual withdrawal spikes or TVL drops.
• Sub-step 2: Review the optimizer's official GitHub repository for incident-related issue reports and commits.
• Sub-step 3: Cross-reference with blockchain explorers (Etherscan) to verify transaction activity during suspected event periods.

Tip: Maintain a spreadsheet linking each event to its root cause analysis report and the affected total value locked (TVL).

Detailed Instructions

For each categorized incident, calculate the financial impact. Determine the total value at risk (VaR) at the time of the event and the actual realized loss. Use on-chain data to track fund movements from optimizer vaults to attacker addresses or during emergency withdrawals. Calculate the recovery rate if funds were reimbursed via treasury or insurance. For instance, analyze the 2021 Yearn v1 DAI vault exploit where $11 million was lost, with a subsequent 100% reimbursement. Use block explorers to trace transactions like 0x6c905b4108a87499ced1e0498721f2b831c6ab13 (the attacker's contract). This quantifies the protocol's historical loss severity and its financial resilience.

• Sub-step 1: Use Dune dashboards to query vault balance changes before, during, and after the incident.
• Sub-step 2: Calculate the loss as a percentage of the vault's TVL at the block height of the exploit.
• Sub-step 3: Verify any reimbursement transactions from the protocol's treasury or governance-controlled addresses.

Tip: Distinguish between permanent loss and temporary insolvency; the latter may indicate robust emergency mechanisms.

Detailed Instructions

Assess the protocol's response timeline and mitigation actions. Review official communications (Discord, Twitter) for time-stamped announcements. Evaluate the technical response: were emergency pauses (pause() function) invoked, and how quickly? Examine post-incident governance proposals for security upgrades, such as migrating to a new vault version or adding multisig signers. For example, after the Pickle Finance Jarrifier exploit, the team implemented a new time-lock on critical functions. Analyze the code diff in the repository commit to understand the fix. This step measures the team's operational security maturity and commitment to improving system robustness after a failure.

• Sub-step 1: Check the optimizer's smart contract for pause function calls on the incident date using Etherscan's "Internal Txns" tab.
• Sub-step 2: Review all governance forum posts and Snapshot votes created in the 30 days following the event.
• Sub-step 3: Audit the commit history in the vault's GitHub repository for security-related patches post-incident.

solidityCopy
// Example of a time-lock modifier added post-incident
modifier timelock() {
    require(block.timestamp >= executionTime, "Timelock: too early");
    require(block.timestamp <= executionTime + gracePeriod, "Timelock: too late");
    _;
}

Tip: A swift, transparent response with verifiable on-chain actions is a positive risk indicator.

Detailed Instructions

Synthesize the collected data into actionable risk metrics. Calculate the Mean Time Between Failures (MTBF) for the optimizer by averaging the days between significant incidents. Estimate the historical Probability of Loss per epoch (e.g., annually) by dividing the number of loss events by the protocol's operational lifespan. Compute the Average Loss Given Default (LGD) as a percentage of TVL. For a concrete example, if an optimizer experienced 2 loss events over 800 days with an average loss of 5% of TVL, its annualized probability is ~0.91% and MTBF is 400 days. These metrics allow for comparative risk assessment against other yield protocols and inform position sizing decisions.

• Sub-step 1: Define a "significant incident" threshold (e.g., loss > 1% of vault TVL) to filter noise.
• Sub-step 2: Use your event log to calculate the exact number of days between qualifying incidents.
• Sub-step 3: Aggregate total losses and average them against the relevant TVL figures for each event to find LGD.

Tip: Combine these historical metrics with current security measures (audit scores, bug bounty payouts) for a forward-looking view.

Detailed Instructions

Benchmarking provides relative risk context. Compare your calculated metrics (MTBF, Probability of Loss) with those of direct competitors (e.g., Yearn vs. Convex vs. Beefy). Also, analyze if incidents correlate with broader market stress, such as the May 2022 UST depeg or the March 2020 Black Thursday. Did the optimizer experience disproportionate withdrawals or losses during these periods? Use total crypto market cap or DeFi TVL charts as a baseline. For instance, assess if a Curve pool exploit that impacted a Convex wrapper occurred during low liquidity conditions. This identifies systemic risk linkages and determines if the optimizer's risk is idiosyncratic or market-driven.

• Sub-step 1: Gather publicly available post-mortems from competitor protocols to build a comparable incident dataset.
• Sub-step 2: Overlay the optimizer's incident dates on a chart of ETH price or DeFi Pulse TVL to visually identify correlations.
• Sub-step 3: Calculate the optimizer's TVL drawdown during market-wide events versus the sector average.

Tip: An optimizer with incidents isolated to its own design flaws may be riskier than one only affected by widespread, exogenous shocks.