Pool Attractiveness Evaluation – Initial Screening
Introduction
As we covered in Part 1 with the Introduction to Pool Attractiveness Evaluation, there are many factors to consider before developing a liquidity provision strategy for a new pool. Now it's time to put that framework into practice and leverage the full power of blockchain data.
As you might have guessed, there are no restrictions on data access since protocols like Uniswap operate fully on-chain. Every operation that's ever happened to every pool on every chain is recorded. Fortunately, you don't need to parse through every transaction on the blockchain to find what you need.
While infrastructure and tooling have taken huge leaps forward thanks to teams building in this space, gaps in data completeness remain due to the complex nature of concentrated liquidity, the math behind it, and the compute required to supply this information to everyone quickly.
We aim to solve that and open our tooling to everyone. In the meantime, while we're actively working on it, let's use TradingView and their newly released DEX Screener for this case study.
The Goal
Our objective is to create a shortlist of pools that we can later analyze either manually or programmatically. We'll conduct backtesting, analyze historical APRs, and evaluate position performance.
The filters you choose reflect your strategy, risk profile, and LP style. You might want to:
- Move positions frequently (or even programmatically) to maximize capital efficiency
- Use wider ranges on more expensive blockchains to reduce competition from active managers
- Find a balance that matches your operational capacity and risk tolerance
Screening Process
Let's review the TradingView Screener and examine our configured filters:
1. Transaction Count 24h ≥ 100
- Ensures minimum pool activity
- Filters out dead or low-activity pools
- Higher transaction count typically means more fee generation opportunities
2. Volatility Range 24h: 1% - 5%
- Sweet spot for manageable impermanent loss
- Too low (<1%): Limited fee opportunities
- Too high (>5%): Excessive rebalancing risk
3. FDV (Fully Diluted Valuation)
- Indicates token's market size and maturity
- Helps filter out micro-caps with manipulation risk
- Not a direct performance indicator, but useful for initial screening
4. TVL: ≤ $5,000,000
- Sweet spot between opportunity and liquidity
- Lower TVL means your capital captures a larger share of fees
- Less competition from institutional LPs and whales
- But setting it too low can make you miss the opportunity
- $1M-$5M range offers best risk/reward for mid-sized positions without active liquidity management
5. Network: Ethereum
- Higher gas costs naturally filter out hyperactive position managers
- Allows for wider ranges and less frequent adjustments
- Better suited for larger position sizes
6. Unique Buyers ≥ 10
- Indicates organic trading interest beyond arbitrage bots
- Suggests real user adoption rather than pure MEV activity
- Higher unique buyers often correlate with sustainable volume
7. Exchange: Uniswap V3
- Flagship exchange where new projects typically list first
- Deepest liquidity and most sophisticated LP ecosystem
- Best data availability and tooling support
8. Sorted by: Volume (24h)
- Volume directly correlates with fee generation
- Prioritizes pools with immediate earning potential
Since TradingView's screener doesn't include all the metrics we need (like fee tier or actual fees generated), you might want to calculate custom metrics such as:
- Volume/TVL ratio: Higher ratios indicate capital efficiency
- FDV/TVL ratio: Helps identify underserved pools with growth potential
Results
Our filters yielded only 10 pools. Quality over quantity.
This narrow selection represents the cream of the crop based on our screening criteria. However, we're not ready to deploy capital just yet. Each pool requires deeper analysis before making any LP decisions.
Spoiler alert: The PAXG/USDC pool has generated 40% ROI over the last 6 months. But understanding why requires the detailed analysis we'll cover next.
But here's the thing: these filters just the illustration of how you might use the power of data available. The real alpha comes from developing your own screening hypotheses and testing them relentlessly. Maybe you'll find that pools with specific fee tiers outperform during certain market conditions, or that TVL/FDV ratios predict future growth better than volume. Stay curious, keep experimenting, and always challenge conventional wisdom. That's how edges are born in DeFi!
What's Next
In Part 3, we'll take these 10 pools and conduct:
- Historical performance backtesting
- Fee tier optimization analysis
- Position range modeling
- Risk-adjusted return calculations
- Final scoring based on our evaluation framework
Remember: Initial screening is just the first step. The real edge comes from understanding the nuances of each pool's behavior and matching that to your LP strategy.
See you in the next article!
