fix: use log1p(pct_change()) in to_log_returns for NaN-safe computation#282
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timkpaine merged 1 commit intopmorissette:masterfrom Feb 28, 2026
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The previous implementation np.log(prices / prices.shift(1)) can produce RuntimeWarnings when prices.shift(1) introduces NaN values, since np.log(NaN) triggers a warning in some numpy configurations. Using np.log1p(prices.pct_change()) is mathematically equivalent: ln(p1/p0) = ln(1 + (p1-p0)/p0) = log1p(pct_change) This approach: - Avoids potential RuntimeWarnings from np.log(NaN) - Uses pandas' built-in pct_change() which handles NaN gracefully - Is numerically more stable for small returns (log1p avoids floating-point precision loss near zero) Fixes pmorissette#156
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Summary
Use
np.log1p(prices.pct_change())instead ofnp.log(prices / prices.shift(1))into_log_returns().Problem
The previous implementation can produce
RuntimeWarnings in certain numpy/pandas configurations becauseprices.shift(1)introduces a NaN for the first element, andnp.log(NaN)may trigger a warning.Fix
The two expressions are mathematically equivalent:
Benefits:
pct_change()handles NaN from shift gracefullylog1pavoids floating-point precision loss for small returnspct_change()instead of manual divisionFixes #156