Detailed Instructions

First, clearly define your portfolio's investment thesis and risk tolerance. Are you a yield farmer, a liquidity provider, or a long-term holder? Your objectives determine the correct benchmark. Next, select your benchmark indices. For a general DeFi portfolio, consider the DeFi Pulse Index (DPI) or the Crypto Market Index (CMI). For a specific strategy like liquidity provision, benchmark against the average APY for the relevant pool on platforms like Uniswap V3 or Curve. Establish a performance measurement period (e.g., quarterly) and decide on your key performance indicators (KPIs), such as Total Value Locked (TVL) growth, Annual Percentage Yield (APY), or Sharpe Ratio.

• Sub-step 1: Document Thesis: Write down your primary strategy, target assets (e.g., 60% ETH, 40% stablecoin LPs), and risk parameters.
• Sub-step 2: Select Primary Benchmark: For a diversified DeFi portfolio, use the DPI token contract address: 0x1494CA1F11D487c2bBe4543E90080AeBa4BA3C2b.
• Sub-step 3: Set KPIs and Cadence: Define that you will track Net Asset Value (NAV) weekly and calculate risk-adjusted returns monthly.

Tip: Use a portfolio tracker like Zapper or DeBank to automatically aggregate your holdings as a starting point for your baseline NAV.

Detailed Instructions

This step involves data ingestion from all your DeFi interactions. You must collect transaction histories, liquidity pool shares, staking positions, and airdrops. The goal is to create a normalized time-series dataset of your portfolio's Net Asset Value (NAV) in a single base currency, typically USD. Use blockchain explorers and APIs to pull raw data. For Ethereum, you can use the Alchemy or Infura RPC endpoints. A critical task is handling impermanent loss calculations for LP positions, which requires historical price data for the asset pairs.

• Sub-step 1: Export Transaction History: Use your wallet address (e.g., 0x742d35Cc6634C0532925a3b844Bc9e90F1b6f1d6) to query the Etherscan API for all ERC-20 transfers and internal transactions.
• Sub-step 2: Calculate LP Token Value: For a Uniswap V3 USDC/ETH position, use the subgraph to query your position's liquidity and underlying assets at specific block heights.
• Sub-step 3: Normalize to USD: Use a price oracle like Chainlink's historical price feeds (e.g., 0x5f4eC3Df9cbd43714FE2740f5E3616155c5b8419 for ETH/USD) to convert all asset values.

pythonCopy
# Example using web3.py to get historical ETH balance
from web3 import Web3
web3 = Web3(Web3.HTTPProvider('YOUR_INFURA_URL'))
balance = web3.eth.get_balance('YOUR_ADDRESS', block_identifier=12345678)
usd_price = get_historical_price('ETH', block_number=12345678) # Fetch from oracle
nav_in_usd = web3.from_wei(balance, 'ether') * usd_price

Tip: Consider using a dedicated portfolio analytics platform like TokenTax or Rotki to automate much of this data aggregation.

Detailed Instructions

With normalized data, calculate your Time-Weighted Rate of Return (TWR) to eliminate the impact of deposits and withdrawals, providing a pure measure of investment skill. Simultaneously, calculate the Simple Return of your chosen benchmark over the same period. The core of benchmarking is the active return, calculated as Portfolio TWR - Benchmark Return. Don't neglect risk metrics; calculate the Sharpe Ratio using the risk-free rate (e.g., the yield on Aave's USDC market, ~2.5% APY) and the standard deviation of your portfolio's periodic returns. For liquidity providers, calculate Impermanent Loss (IL) as (Value of LP Position - Value of Hodled Assets) / Value of Hodled Assets.

• Sub-step 1: Compute TWR: Break your period into sub-periods between cash flows, calculate returns for each, and chain them geometrically.
• Sub-step 2: Fetch Benchmark Performance: Query the DPI's price change from your start to end date using a Coingecko API call: https://api.coingecko.com/api/v3/coins/defi-pulse-index.
• Sub-step 3: Calculate Sharpe Ratio: Use 90-day daily returns. If your portfolio's annualized volatility is 65% and return is 40%, with a 2.5% risk-free rate, Sharpe = (0.40 - 0.025) / 0.65 ≈ 0.577.

Tip: A negative active return indicates your portfolio underperformed the passive benchmark, signaling a need for strategy reassessment.

Detailed Instructions

Performance attribution breaks down your active return into components: allocation effect (choosing the right protocols) and selection effect (choosing specific assets within those protocols). For example, if your portfolio outperformed because you were overweight in a high-yielding lending protocol like Aave while the benchmark was weighted toward DEXs, that's a positive allocation effect. Use this analysis to conduct a post-mortem on underperforming positions. Was underperformance due to network congestion fees, a smart contract exploit, or simply market timing? Finally, update your benchmark if your strategy evolves and schedule the next review cycle.

• Sub-step 1: Run Attribution Analysis: Compare your portfolio's sector weight (e.g., 30% Lending) vs. the benchmark's weight (20% Lending). Calculate the contribution of this overweight to your overall return.
• Sub-step 2: Review Key Transactions: Audit large losing trades. For instance, if providing liquidity to a new pool resulted in significant impermanent loss, note the volatility conditions that caused it.
• Sub-step 3: Adjust Strategy and Re-benchmark: If you shift focus to Layer 2 solutions, switch your benchmark to an L2-centric index. Update your portfolio's target allocations in a living document.

Tip: Create a simple dashboard (e.g., using Google Sheets or Dune Analytics) to visualize your portfolio's performance against the benchmark over time, making trends immediately apparent.