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Every analytic on PythFeeds is computed from publicly auditable data. This page documents the exact formulas so you can reproduce or challenge any number you see.
All return-based metrics use continuously-compounded log returns, not simple returns:
r_t = ln(P_t / P_(t-1))
Log returns are time-additive and approximately normally distributed for short horizons, which makes correlation, β and volatility math more stable.
Pearson measures linear co-movement of log returns:
ρ(x,y) = Σ((x_i − x̄)(y_i − ȳ)) / √(Σ(x_i − x̄)² · Σ(y_i − ȳ)²)
Spearman is Pearson computed on ranks — captures monotonic (not just linear) relationships and is robust to outliers.
β = Cov(r_asset, r_BTC) / Var(r_BTC)
β = 1 means the asset moves 1:1 with BTC. β > 1 amplifies BTC moves; β < 0 is negative correlation.
σ_annual = σ_daily · √365
Crypto uses 365 (not 252) because markets trade 24/7.
Sharpe = (r̄ − r_f) / σ
Default risk-free rate r_f is 0% for crypto (adjustable in portfolio settings).
MDD = max_t ( (peak_t − P_t) / peak_t )
The largest peak-to-trough decline in the period, as a percentage of the peak.
Every Pyth price ships with a conf value. The real price is bounded by price ± conf. For risk-sensitive use cases we apply:
price − conf (conservative).price + conf (conservative).price with conf as a secondary badge.Equity feeds go stale outside US market hours by design.
Questions about a specific number? See the Glossary or open an issue on our community channels (see About).