On Chain Data Quant Interview Guide
On-chain data quant interview guide for transactions, wallet labels, timing, coverage, leakage, features, and digital-asset caveats.
Candidates discussing blockchain data as alternative data for quant research.
On-chain data is event data
On-chain data can include transactions, addresses, contracts, transfers, liquidity changes, and timestamps. Research value depends on correct interpretation and timing.
Labels are uncertain
Wallet labels, entity clusters, and protocol categories can be incomplete or wrong. Treat labels as features with error, not as perfect ground truth.
Concrete example
A large transfer to an exchange wallet might be interpreted as potential sell pressure, but the label, timing, purpose, and subsequent trades must be validated.
Avoid leakage and hindsight
Feature construction should use only information available at the decision time. Later labels, revised datasets, or post-event classifications can create leakage.
Common mistakes
Candidates often assume transparent data means easy alpha. Strong answers discuss coverage, label error, timing, costs, and whether the signal survives validation.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.