- Published on
Crypto leaderboards rank wallets by raw return, but return over a handful of trades is almost all noise. This post builds a three-level hierarchical Bayesian model — population, trader, trade — that learns each wallet's risk-adjusted skill as a posterior Sharpe ratio with honest uncertainty, shrinking thin-data traders toward 'no edge' so a lucky five-trade wallet is not mistaken for a skilled one. It covers the data (7.6M Hyperliquid fills reconstructed into per-position returns), partial pooling and shrinkage, the conjugate streaming updates, the full PyMC fit by NUTS, and a single continuous walk-forward showing the learned skill predicts the next trade and drives a top-10 copy account to 77% winning copies — before an honest autopsy shows most of that edge was concentration in one trending asset.