Liquidity Pool Selection & Analysis Framework

You are a DeFi liquidity analyst who evaluates pools across AMMs and lending protocols. You have developed quantitative methods for comparing pool opportunities that account for all costs and risks.

CONTEXT: There are thousands of liquidity pools across hundreds of protocols and dozens of chains. Most advertised APYs are misleading because they do not account for impermanent loss, gas costs, token depreciation from farming rewards, or smart contract risk. I need a rigorous framework to compare pools on a true risk-adjusted basis. I have $20,000-$100,000 to deploy.

TASK: Build a liquidity pool analysis and selection framework:
1. Define the key metrics for pool evaluation: real APY (after IL and gas), fee APR from trading volume, reward token sustainability (emission schedule vs. protocol revenue), pool depth and utilization rate, historical volatility of the pair, and correlation between pool assets.
2. Create a "true yield" calculation methodology that deducts: estimated impermanent loss (based on historical volatility), gas costs for entry/exit/claiming, reward token sell pressure impact, and protocol fee if applicable. Provide the formulas and a worked example.
3. Build a pool risk scoring model (1-10 scale) incorporating: smart contract audit status, protocol age and TVL, chain security (L1 vs. L2 vs. sidechain), oracle dependency, admin key risks, and composability risks (if the pool is built on top of other protocols).
4. Design a pool comparison matrix template where I can input 5-10 pools and compare them across yield, risk, capital efficiency, lock-up requirements, and chain gas costs. Specify which data sources to use for each metric.
5. Create specific playbooks for different pool types: concentrated liquidity (Uniswap v3/v4 style), stable pairs, correlated pairs (ETH/stETH), and volatile pairs. Each playbook should specify range settings, rebalancing frequency, and expected IL scenarios.
6. Include a pool monitoring and exit strategy: define deterioration signals (declining volume, reward cuts, TVL flight, oracle manipulation attempts) and the exit decision tree.