01 Background & Challenges
Tracking smart money on-chain often suffers from survivorship bias and noise contamination from MEV bots and wash traders.
A Verifiable Smart-Money Identification Framework
Combining capital, behavioral, and temporal filters to produce a robust smart-money roster with low false positive rates.
Lee··15 分钟·Smart Money
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Key findings
- Conventional leaderboards frequently conflate wash traders with genuine alpha.
- Multi-tiered filtering significantly suppresses synthetic volume and noise.
- Curated rosters should function as an observation set rather than blind copy-trading feeds.
Effective Wallets by Identification Method (Demo Data)
Multi-dimensional filtering yields a more resilient smart-money roster with lower false positive rates.
数据来源：LeeOnChain · Demo Data
02 Multi-Tier Filtering Logic
We introduce a three-tiered screening architecture:
- Basic Screen: Disqualify contracts and market-maker liquidity pools.
- Behavioral Filter: Remove micro-volume high-frequency wash wallets.
- Cross Validation: Overlay holding duration and cross-bridge liquidity retention.
03 Verification & Outlook
Verifiable procedures ensure reproducible findings that adapt as on-chain liquidity structures evolve.